I find that on my M2 Mac that number is a rough approximation to how much memory the model needs (usually plus about 10%) - which matters because I want to know how much RAM I will have left for running other applications.
Anything below 20GB tends not to interfere with the other stuff I'm running too much. This model looks promising!
The models feel pretty snappy when interacting with them directly via ollama, not sure about the TPS
However I've also ran into 2 things: 1) most models don't support tools, sometimes it's hard to find a version of the model that correctly uses tools, 2) even with good TPS, since the agents are usually doing chain-of-thought and running multiple chained prompts, the experience feels slow - this is even true with Cursor using their models/apis
I was able to run it on my M2 Air with 24GB. Startup was very slow but less than 10 minutes. After that responses were reasonably quick.
Edit: I should point out that I had many other things open at the time. Mail, Safari, Messages, and more. I imagine startup would be quicker otherwise but it does mean you can run with less than 32GB.
Almost all models listed in the ollama model library have a version that's under 20GB. But whether that's a 4-bit quantization (as in this case) or more/fewer bits varies.
AFAICT they usually set the default tag to sa version around 15GB.
In ollama, how do you set up the larger context, and figure out what settings to use? I've yet to find a good guide. I'm also not quite sure how I should figure out what those settings should be for each model.
There's context length, but then, how does that relate to input length and output length? Should I just make the numbers match? 32k is 32k? Any pointers?
Ollama breaks for me. If I manually set the context higher. The next api call from clone resets it back.
And ollama keeps taking it out of memory every 4 minutes.
LM studio with MLX on Mac is performing perfectly and I can keep it in my ram indefinitely.
Ollama keep alive is broken as a new rest api call resets it after. I’m surprised it’s this glitched with longer running calls and custom context length.
The SWE-Bench scores are very, very high for an open source model of this size. 46.8% is better than o3-mini (with Agentless-lite) and Claude 3.6 (with AutoCodeRover), but it is a little lower than Claude 3.6 with Anthropic's proprietary scaffold. And considering you can run this for almost free, this is a very extraordinary model.
No. I am referring to Claude 3.5 Sonnet New, released October 22, 2024, with model ID claude-3-5-sonnet-20241022, colloquially referred to as Claude 3.6 Sonnet because of Anthropic's confusing naming.
It's the reason Anthropic called their next release 3.7 Sonnet - the 3.6 version number was already being used by some in the community to refer to their 3.5v2.
Anthropic moved from 3.5, to 3.5(new), to 3.7. They skipped 3.6 because of usage in the community, and because 3.5(newer) probably passed some threshold of awfulness.
People also use 3.5.1 to refer to 3.5(new)/3.6.
The remaining difficulty now is when people refer to 3.5, without specifying (new) or (old). I find most unspecified references to 3.5 these days are actually to 3.6 / 3.5.1 / 3.5(new), which is confusing.
*For people without a 24GB RAM video card, I've got an 8GB RAM one running this model performs OK for simple tasks on ollama but you'd probably want to pay for an API for anything using a large context window that is time sensitive:*
Compared to an API call that finishes in about 20% of the time it feels a bit slow without the recommended graphics card and what not is all I'm saying.
In terms of benchmarks, it seems unusually well tuned for the model size but I suspect its just a case of gaming the measurement by testing against it as part of the development of the model which is not bad in and of itself since I suspect every LLM who is in this space marketed to IT folks does the same thing tbh so its objective enough given that as a rough gauge of "Is this usable?" without heavy time expense testing it.
IMO, it's not about ethics, it's about legal risks. What if you want to fine tune a model on output related to your usage? Then my understanding is that all these derivatives need to be under the same license. What if G will change their prohibited use policy (the first line there is that they could update it from time to time)? There's really crazy stuff in terms of use of some services, what if G adds something in the same tune there which basically makes your application impossible.
It's not about ethical or not, it's about risk to your startup. Ethics are super subjective (and often change based on politics). Apache means you own your own model, period.
> Ethics are super subjective (and often change based on politics).
That's obviously not true. Ethics often have some nuance and some subjectiveness, but it's not something entirely subjective up to "politics".
Saying this makes it sound like you work at a startup for an AI powered armed drone, and your view of it is 'eh, ethics is subjective, this is fine' when asked how do you feel about responsibility and AI killing people.
> Ethics often have some nuance and some subjectiveness, but it's not something entirely subjective up to "politics".
Ethics are entirely subjective, as is inherently true of anything that supports "should" statements, because to justify any should statement, you need another "should" statement, you can never rest should entirely on "is" (you can, potentially, reset any entire system of "should" one root "should" axiom, though in practice most systems have more than one root axiom.)
And the process of coming to social consensus on a system of ethics is precisely politics.
You can dislike that this is true, but it is true.
> Saying this makes it sound like you work at a startup for an AI powered armed drone, and your view of it is 'eh, ethics is subjective, this is fine' when asked how do you feel about responsibility and AI killing people.
Understanding that ethics is subjective does not mean that one does not have a strong ethical framework that they adhere to. It just means that one understands the fundamental nature of ethics and the kind of propositions that ethical propositions inherently are.
Understanding that ethics are subjective does not, in other words, imply the belief that all beliefs about ethics (or, a fortiori, matters that are inherently subjective more generally) are of equal moral/ethical merit.
> Saying this makes it sound like you work at a startup for an AI powered armed drone, and your view of it is 'eh, ethics is subjective, this is fine' when asked how do you feel about responsibility and AI killing people.
Is it always wrong to kill people? If you say yes, then you are also saying it's wrong to defend yourself from people who are trying to kill you.
This is what I mean by subjective.
And then since Google is beholden to US laws, if the US government suddenly decides that helping Ukraine to defend itself is wrong, but you personally believe defending Ukraine is right, suddenly you have a problem...
I've worked with other models from All Hands recently, and I believe they were based on Mistral.
My general impression so far is that they aren't quite up to Claude 3.7 Sonnet, but they're quite good. More than adequate for an "AI pair coding assistant", and suitable for larger architectural work as long as you break things into steps for it.
What hardware are y'all using when you run these things locally? I was thinking of pre ordering the Framework desktop[0] for this purpose, but I wouldn't mind having a decent laptop that could run it (ideally Linux).
The same page also gives instructions for running the model through VLLM on a GPU, but it doesn't seem like it supports quantization, so it may require multiple GPUs since the instructions say "with at least 2 GPUs".
Does Nvidia have integrated memory options that allow you to get up to 64GB+ of VRAM without stringing together a bunch of 4090s?
For local LLMs Apple Silicon has really shown the value of shared memory, even if that comes at the cost of raw GPU power. Even if it's half the speed of an array of GPUs, being able to load the mid-sized models at all is a huge plus.
It also costs 4x the entire Framework Desktop for just the card. If you're doing something professional that's probably worth it, but it's not a clear winner in the enthusiast space.
How did you test this? Note that this is not a regular coding model (i.e. write a function that does x). This is a fine-tuned model specifically post-trained on a cradle (open hands, ex open devin). So their main focus was to enable the "agentic" flows, with tool use, where you give the model a broad task (say a git ticket) and it starts by search_repo() or read_docs(), followed by read_file() in your repo, then edit_file(), then run_tests() and so on. It's intended to first solve those problems. They suggest using it w/ open hands for best results.
Early reports from reddit say that it also works in cline, while other stronger coding models had issues (they were fine-tuned more towards a step-by-step chat with a user). I think this distinction is important to consider when testing.
I did a very simple tool calling test and it was simply unable to call the tool and use the result.
Maybe it's specialized to use just a few very specific tools? Is there some documentation on how to actually set it up without requiring some weird external platform?
Which model is optimized to do that? This is what I want out of LLMs! And also talking high level architecture (without any code) and library discovery, but I guess the general talking models are good for that...
Any company in this space outside of the top few should be contributing to the open-source tools (Aider, OpenHands, etc.); that is a better bet than making your own tools from scratch to compete with ones from much bigger teams. A couple folks making harnesses work better with your model might yield improvements faster than a lot of model-tuning work, and might also come out of the process with practical observations about what to work on in the next spin of the model.
Separately, deploying more autonomous agents that just look at an issue or such just seems premature now. We've only just gotten assisted flows kind-of working, and they still get lost--get stuck on not-hard-for-a-human debugging tasks, implement Potemkin 'fixes', forget their tools, make unrelated changes that sometimes break stuff, etc.--in ways that imply that flow isn't fully-baked yet.
Maybe the main appeal is asynchrony/potential parallelism? You could tackle that different ways, though. And SWEBench might be a good benchmark still (focus on where you want to be, even if you aren't there yet), but that doesn't mean it represents the most practical way to use these tools day-to-day currently.
Maybe the EU should cover the cost of creating this agent/model, assuming it really delivers what it promises. It would allow Mistral to keep focusing on what they do and for us it would mean that the EU spent money wisely.
Wow. I was just grabbing some models and I happened to see this one while I was messing with tool support in LLamaIndex. I have an agentic coding thing I threw together and I have been trying different models on it and was looking to throw ReAct at it to bring in some models that don't have tool support and this just pops into existence!
I'm not able to get my agentic system to use this model though as it just says "I don't have the tools to do this". I tried modifying various agent prompts to explicitly say "Use foo tool to do bar" without any luck yet. All of the ToolSpec that I use are annotated etc. Pydantic objects and every other model has figured out how to use these tools.
What's the play for smaller base model training companies like Mistral ?
Mistral's positioning as the European alternative doesn't seem to be sticking. Acquisition seems tricky given how inflection, character.ai and stability have got carved out. The big acquisition bucks are going to product companies (windsurf)
They could pivot up the stack, but then they'd be starting from scratch with a team that's ill-suited for product development.
The base model offerings from pretraining companies have been surprisingly myopic. Deepmind seems to be the only one going past the obvious "content gen/coding automation" verticals. There's a whole world out there. LLM product companies are fast acquiring pieces of the real money pie and smaller pretraining companies are getting left out.
______
edit: my comment rose to the top. It's early in the morning. Injecting a splash of optimism.
LLMs are hard, and giants like Meta are struggling to make steady progress. Mistrals models are cheap, competent, open-source-ish and don't come with AGI-is-imminent baggage. Good enough for me.
I actually think the underdog, cheap, but still capable independent european alternative is a decent selling point. They have also branched out into specialised models, and custom training. as well as their ocr service.
Maybe someone here can suggest tools or at least where to look; what are the state-of-the-art models to run locally on relatively low power machines like a MacBook Air? Is there anyone tracking what is feasible given a machine spec?
"Apple Intelligence" isn't it but it would be nice to know without churning through tests whether I should bother keeping around 2-3 models for specific tasks in ollama or if their performance is marginal there's a more stable all-rounder model.
The best general model you can run locally is probably some version of Gemma 3 or the latest Mistral Small. On a Windows machine, this is limited by VRAM, since system RAM is too low-bandwidth to run models at usable speeds. On an M-series Mac, the system memory is on-die and fast enough to use. What you can run will be the total RAM, minus whatever MacOS uses and the space you want for other programs.
To determine how much space a model needs, you look at the size of the quantized (lower precision) model on HuggingFace or wherever it's hosted. Q4_K_M is a good default. As a rough rule of thumb, this will be a little over half the size of the parameters, if they were in gigabytes. For Devstral, that's 14.3GB. You will also need 1-8GB more than that, to store the context.
For example:
A 32GB Macbook Air could use Devstral at 14.3+4GB, leaving ~14GB for the system and applications.
A 16GB Macbook Air could use Gemma 3 12B at 7.3+2GB, leaving ~7GB for everything else.
An 8GB Macbook could use Gemma 3 4B at 2.5GB+1GB, but this is probably not worth doing.
> An 8GB Macbook could use Gemma 3 4B at 2.5GB+1GB, but this is probably not worth doing.
I am currently using this model on a Macbook with 16GB ram, it is hooked up with a chrome extension that extracts text from webpages and logs to a file, then summarizes each page. I want to develop an episodic memory system, like MS Recall, but local, it does not leak my data to anyone else, and costs me nothing.
Gemma 3 4B runs under ollama and is light enough that I don't feel it while browsing. Summarization happens in the background. This page I am on is already logged and summarized.
I would recommend just trying it out! (as long as you have the disk space for a few models). llama.cpp[0] is pretty easy to download and build and has good support for M-series Macbook Airs. I usually just use LMStudio[1] though - it's got a nice and easy-to-use interface that looks like the ChatGPT or Claude webpage, and you can search for and download models from within the program. LMStudio would be the easiest way to get started and probably all you need. I use it a lot on my M2 Macbook Air and it's really handy.
I use qwen3:30b-a3b-q4_K_M for coding support and spam filtering, qwen2.5vl:32b-q4_K_M for image recognition/tagging/describing and sometimes gemma3:27b-it-qat for writing. All through Ollama, as that provides a unified interface, and then accessed from Emacs, command-line llm tool or my Clojure programs.
There is no single "best" model yet, it seems.
That's on an M4 Max with 64GB of RAM. I wish I had gotten the 128GB model, though — given that I run large docker containers that consume ~24GB of my RAM, things can get tight.
It's nice that Mistral is back to releasing actual open source models. Europe needs a competitive AI company.
Also, Mistral has been killing it with their most recent models. I pay for Le Chat Pro, it's really good. Mistral Small is really good. Also building a startup with Mistral integration.
> Devstral excels at using tools to explore codebases
As an AI and vibe coding newbie, how does that work? E.g. how would I use devstral and ollama and instruct it to use tools? Or would I need some other program as well?
In the Ollama API, you use the "tools" parameter to describe the available tools to the model, then use the "tool_calls" from the response to call the functions and send the results back to the model using "role": "tool".
Most of this is handled very easily by the ollama-python library, so you can integrate tool calling very simply in any script.
That said, this specific model was unable to call the functions and use the results in my "hello world" tests, so it seems it expects a few very specialized tools to be provided, which are defined by that platform they're advertising.
Right now the best tool calling model I've used is still qwen3, it works very reliably, and I can give it any ability I want and it'll use it when expected, even in /no_think mode.
I'd use a "subpar" LLM for any coding practice where I want to do the bulk of the thinking and where I care about how much coal I'm burning.
It's kind-of like asking, for which kind of road-trip would you use a Corolla hatchback instead of a Jeep Grand Wagoneer? For me the answer would be "almost all of them", but for others that might not be the case.
from what I gather it's finetuned to use OpenHand specifically so shows value on thsoe benchmark that target a whole system as a blackbox (i.e. agent + llm) more than directly target the llm input/outputs
The SWE bench is super impressive of model of any size. However just providing one benchmark results and having to do partnership with OpenHands seems like they focused too much on optimizing the number.
I know it's not the recommended runner (OpenHands), but running this on Cline (ollama back-end), it seemed absolutely atrocious at file reading and tool calling.
I feel this is part of a larger and very old business trend.
But do we need 20 companies copying each other and doing the same thing?
Like, is that really competition? I'd say competition is when you do something slightly different, but I guess it's subjective based on your interpretation of what is a commodity and what is proprietary.
To my view, everyone is outright copying and creating commodity markets:
OpenAI: The OG, the Coke of Modern AI
Claude: The first copycat, The Pepsi of Modern AI
Mistral: Euro OpenAI
DeepSeek: Chinese OpenAI
Grok/xAI: Republican OpenAI
Google/MSFT: OpenAI clone as a SaaS or Office package.
I don't think they're actually the same thing, I definitely feel like Claude is much better with code than ChatGPT is so there are clearly differences in the capabilities of these models. One analogy that I find helpful here is the idea that these AIs are like animals. Just like there are animals of the same family (meaning they're genetically related to some degree) they still adapt to different niches. I see all these AI companies ultimately creating models analogous to that.
Some AIs will be good at coding (perhaps in a particular language or ecosystem), some at analyzing information and churning out a report for you, and some will be better at operating in physical spaces.
Will check it out. I like that we are all on the same page that Openness is a numerical value rather than a boolean, the challenge now is how to measure and define it, especially with ML
What a ripoff, considering that a 5090 with 32GB of VRAM also currently costs $3k ;)
(Source: I just received the one I ordered from Newegg a week ago for $2919. I used hotstocks.io to alert me that it was available, but I wasn’t super fast at clicking and still managed to get it. Things have cooled down a lot from the craziness of early February.)
That's probably because the 5000 series seems to be a big let-down. It's pretty much identical to the 4000 series in efficiency; they've only increased performance by massively increasing power usage.
When I needed 21 3090s and none were available but for ridiculously high prices, I bought Dell Alienware comps, stripped them out, and sold the rest. Definitely made my money back mining for crypto with those cards. Dell surprisingly has a lot of computers with great RTX cards in stock.
The vendor disappeared a few hours after my comment and now his Amazon store doesn't exist anymore, replaced by another vendor with no sales but normal prices :) . Even if the seller was written as a german company "Wundshop" it was only registered on Amazon Spain
More like 700 € if you are lucky. Prices are still not back down from the start of the AI boom.
I am hopeful that the prices will drop a bit more with Intel's recently announced Arc Pro B60 with 24GB VRAM, which unfortunately has only half the memory bandwidth of the RTX 3090.
Not sure why other hardware makers are so slow to catch up. Apple really was years ahead of the competition with the M1 Ultra with 800 GB/s memory bandwidth.
The first number I look at these days is the file size via Ollama, which for this model is 14GB https://ollama.com/library/devstral/tags
I find that on my M2 Mac that number is a rough approximation to how much memory the model needs (usually plus about 10%) - which matters because I want to know how much RAM I will have left for running other applications.
Anything below 20GB tends not to interfere with the other stuff I'm running too much. This model looks promising!
Any agentic dev software you could recommend that runs well with local models?
I’ve been using Cursor and I’m kind of disappointed. I get better results just going back and forth between the editor and ChatGPT
I tried localforge and aider, but they are kinda slow with local models
https://github.com/block/goose
I used devstral today with cline and open hands. Worked great in both.
About 1 minute initial prompt processing time on an m4 max
Using LM studio because the ollama api breaks if you set the context to 128k.
How is it great that it takes 1 minute for initial prompt processing?
Have you tried using mlx or Simon Wilson’s llm?
https://llm.datasette.io/en/stable/
https://simonwillison.net/tags/llm/
you can use ollama in VS Code's copilot. I haven't personally tried it but I am interested in how it would perform with devstral
ra-aid works pretty well with Ollama (haven't tried it with Devstral yet though)
https://docs.ra-aid.ai/configuration/ollama/
Do you have any other interface for the model? what kind of tokens/sec are you getting?
Try hooking aider up to gemini and see how the speed is. I have noticed that people in the localllama scene do not like to talk about their TPS.
The models feel pretty snappy when interacting with them directly via ollama, not sure about the TPS
However I've also ran into 2 things: 1) most models don't support tools, sometimes it's hard to find a version of the model that correctly uses tools, 2) even with good TPS, since the agents are usually doing chain-of-thought and running multiple chained prompts, the experience feels slow - this is even true with Cursor using their models/apis
I couldn’t run it on my 16gb MBP (I tried, it just froze up, probably lots of swapping), they say it needs 32gb
I was able to run it on my M2 Air with 24GB. Startup was very slow but less than 10 minutes. After that responses were reasonably quick.
Edit: I should point out that I had many other things open at the time. Mail, Safari, Messages, and more. I imagine startup would be quicker otherwise but it does mean you can run with less than 32GB.
Almost all models listed in the ollama model library have a version that's under 20GB. But whether that's a 4-bit quantization (as in this case) or more/fewer bits varies.
AFAICT they usually set the default tag to sa version around 15GB.
Yes, I agree. I've just ran the model locally and it's making a good impression. I've tested it with some ruby/rspec gotchas, which it handled nicely.
I'll give it a try with aider to test the large context as well.
In ollama, how do you set up the larger context, and figure out what settings to use? I've yet to find a good guide. I'm also not quite sure how I should figure out what those settings should be for each model.
There's context length, but then, how does that relate to input length and output length? Should I just make the numbers match? 32k is 32k? Any pointers?
For aider and ollama, see: https://aider.chat/docs/llms/ollama.html
Just for ollama, see: https://github.com/ollama/ollama/blob/main/docs/faq.md#how-c...
I’m using llama.cpp though, so I can’t confirm these methods.
Are you using it with aider? If so, how has your experience been?
Ollama breaks for me. If I manually set the context higher. The next api call from clone resets it back.
And ollama keeps taking it out of memory every 4 minutes.
LM studio with MLX on Mac is performing perfectly and I can keep it in my ram indefinitely.
Ollama keep alive is broken as a new rest api call resets it after. I’m surprised it’s this glitched with longer running calls and custom context length.
The SWE-Bench scores are very, very high for an open source model of this size. 46.8% is better than o3-mini (with Agentless-lite) and Claude 3.6 (with AutoCodeRover), but it is a little lower than Claude 3.6 with Anthropic's proprietary scaffold. And considering you can run this for almost free, this is a very extraordinary model.
They are referring to SWE bench lite. Just want to make sure you are too.
Where did you get that idea? In the post they are repeatedly referring to SWEBench-Verified and nothing else.
extraordinary.. or suspicious that the benchmarks aren't doing their job
I wasn't considering Mistral for anything, but this show of goodwill to open source is amazing. I'll have to give this a try.
Mistral have a long history of open weight models...
Just to confirm, are you referring to Claude 3.7?
No. I am referring to Claude 3.5 Sonnet New, released October 22, 2024, with model ID claude-3-5-sonnet-20241022, colloquially referred to as Claude 3.6 Sonnet because of Anthropic's confusing naming.
And it is a very good LLM. Some people complain they don't see an improvement with Sonnet 3.7
Also known as Claude 3.5 Sonnet V2 on AWS Bedrock and GCP Vertex AI
> colloquially referred to as Claude 3.6
Interesting. I've never heard this.
It's the reason Anthropic called their next release 3.7 Sonnet - the 3.6 version number was already being used by some in the community to refer to their 3.5v2.
because nobody says that
Anthropic moved from 3.5, to 3.5(new), to 3.7. They skipped 3.6 because of usage in the community, and because 3.5(newer) probably passed some threshold of awfulness.
People also use 3.5.1 to refer to 3.5(new)/3.6.
The remaining difficulty now is when people refer to 3.5, without specifying (new) or (old). I find most unspecified references to 3.5 these days are actually to 3.6 / 3.5.1 / 3.5(new), which is confusing.
That's not correct. I have always referred to it as v3.6, and I've seen plenty of other people do so too. It's why their next model was called v3.7
The model formerly known as Claude 3.6 Sonnet?
*For people without a 24GB RAM video card, I've got an 8GB RAM one running this model performs OK for simple tasks on ollama but you'd probably want to pay for an API for anything using a large context window that is time sensitive:*
total duration: 35.016288581s load duration: 21.790458ms prompt eval count: 1244 token(s) prompt eval duration: 1.042544115s prompt eval rate: 1193.23 tokens/s eval count: 213 token(s) eval duration: 33.94778571s eval rate: 6.27 tokens/s
total duration: 4m44.951335984s load duration: 20.528603ms prompt eval count: 1502 token(s) prompt eval duration: 773.712908ms prompt eval rate: 1941.29 tokens/s eval count: 1644 token(s) eval duration: 4m44.137923862s eval rate: 5.79 tokens/s
Compared to an API call that finishes in about 20% of the time it feels a bit slow without the recommended graphics card and what not is all I'm saying.
In terms of benchmarks, it seems unusually well tuned for the model size but I suspect its just a case of gaming the measurement by testing against it as part of the development of the model which is not bad in and of itself since I suspect every LLM who is in this space marketed to IT folks does the same thing tbh so its objective enough given that as a rough gauge of "Is this usable?" without heavy time expense testing it.
> For people without a 24GB RAM video card, I've got an 8GB RAM one running
What're you using for this? llama.cpp? Have a 12GB card (rtx 4070) i'd like to try it on.
https://ollama.com/library/devstral
https://ollama.com/
I believe its just a HTTP wrapper and terminal wrapper around llama.cpp with some modifications/fork.
Does ollama have support for cpu offloading?
> Does ollama have support for cpu offloading?
https://www.reddit.com/r/ollama/comments/1df757o/high_cost_o...
https://github.com/ollama/ollama/issues/8291
Yes.
A perfect blend of LMGTFY and helpfulness. :)
lol. I try not to be a total asshole, it sometime even works! :)
Good luck to you mate with your life :)
It's very nice that it has the Apache 2.0 license, i.e. well understood license, instead of some "open weight" license with a lot of conditions.
[flagged]
IMO, it's not about ethics, it's about legal risks. What if you want to fine tune a model on output related to your usage? Then my understanding is that all these derivatives need to be under the same license. What if G will change their prohibited use policy (the first line there is that they could update it from time to time)? There's really crazy stuff in terms of use of some services, what if G adds something in the same tune there which basically makes your application impossible.
P.S. I am not a lawyer.
They're all quite easy to strip of protections and I don't think anyone doing unethical stuff is big on following licenses anyway
I'm not sure what you're trying to imply... only rogue software developers use devstral?
TIL Open Source is only used for unethical purposes
What's different between the ethics of Mistral and Gemma?
I think their point was more that Gemma open models have restrictive licences, while some Mistral open models do not.
It's not about ethical or not, it's about risk to your startup. Ethics are super subjective (and often change based on politics). Apache means you own your own model, period.
> Ethics are super subjective (and often change based on politics).
That's obviously not true. Ethics often have some nuance and some subjectiveness, but it's not something entirely subjective up to "politics".
Saying this makes it sound like you work at a startup for an AI powered armed drone, and your view of it is 'eh, ethics is subjective, this is fine' when asked how do you feel about responsibility and AI killing people.
> Ethics often have some nuance and some subjectiveness, but it's not something entirely subjective up to "politics".
Ethics are entirely subjective, as is inherently true of anything that supports "should" statements, because to justify any should statement, you need another "should" statement, you can never rest should entirely on "is" (you can, potentially, reset any entire system of "should" one root "should" axiom, though in practice most systems have more than one root axiom.)
And the process of coming to social consensus on a system of ethics is precisely politics.
You can dislike that this is true, but it is true.
> Saying this makes it sound like you work at a startup for an AI powered armed drone, and your view of it is 'eh, ethics is subjective, this is fine' when asked how do you feel about responsibility and AI killing people.
Understanding that ethics is subjective does not mean that one does not have a strong ethical framework that they adhere to. It just means that one understands the fundamental nature of ethics and the kind of propositions that ethical propositions inherently are.
Understanding that ethics are subjective does not, in other words, imply the belief that all beliefs about ethics (or, a fortiori, matters that are inherently subjective more generally) are of equal moral/ethical merit.
> Ethics are entirely subjective
That's obviously not true. Is it ethical to have slaves or kill children? No, objectively, it is not.
It was in Rome.
You are objectively using objectively wrong :-P (sorry, couldn't resist)
Disclaimer: I agree that having slaves or killing children is very wrong.
You are getting morals and ethics confused.
Ethics are an objective analysis framework. Morals are subjective.
There are plenty of ethical frameworks where those examples you give objectively evaluate to “yes”.
I don't work in defence, but I would be proud to work for a drone manufacturer whose drones were defending Ukraine, for example.
> Saying this makes it sound like you work at a startup for an AI powered armed drone, and your view of it is 'eh, ethics is subjective, this is fine' when asked how do you feel about responsibility and AI killing people.
Is it always wrong to kill people? If you say yes, then you are also saying it's wrong to defend yourself from people who are trying to kill you.
This is what I mean by subjective.
And then since Google is beholden to US laws, if the US government suddenly decides that helping Ukraine to defend itself is wrong, but you personally believe defending Ukraine is right, suddenly you have a problem...
I don't believe the benchmarks they're presenting.
I haven't tried it out yet but every model I've tested from Mistral has been towards the bottom of my benchmarks in a similar place to Llama.
Would be very surprised if the real life performance is anything like they're claiming.
I've worked with other models from All Hands recently, and I believe they were based on Mistral.
My general impression so far is that they aren't quite up to Claude 3.7 Sonnet, but they're quite good. More than adequate for an "AI pair coding assistant", and suitable for larger architectural work as long as you break things into steps for it.
I don't believe them either. We really have to test these ourselves imo.
Qwen3 is a step backwards for me for example. And GLM4 is my current goto despite everyone saying it's "only good at html"
The 70b cogito model is also really good for me but doesn't get any attention.
I think it depends on our projects / languages we're using.
Still looking forward to trying this one though :)
What hardware are y'all using when you run these things locally? I was thinking of pre ordering the Framework desktop[0] for this purpose, but I wouldn't mind having a decent laptop that could run it (ideally Linux).
[0] https://frame.work/desktop
All Hands AI has instructions for running Devstral locally on a MacBook using LMStudio: https://docs.all-hands.dev/modules/usage/llms/local-llms#ser...
The same page also gives instructions for running the model through VLLM on a GPU, but it doesn't seem like it supports quantization, so it may require multiple GPUs since the instructions say "with at least 2 GPUs".
M4 max 128gb ram.
LM studio MLX with full 128k context.
It works well but has a long 1 minute initial prompt processing time.
I wouldn’t buy a laptop for this, I would wait for the new AMD 32gb gpu coming out.
If you want a laptop I even consider my m4 max too slow to use more than just here or there.
It melts if you run this and battery goes down asap. Have to use it docked for full speed really
Yep I have an M4 Max Studio with 128GB of RAM, even the Q8 GGUF fits in memory with 131k context. Memory pressure at 45% lol
How many tokens per second are you both getting?
AMD is going to be off the beaten path, you're likely to have more success/less boring plumbing trouble with nVidia.
Does Nvidia have integrated memory options that allow you to get up to 64GB+ of VRAM without stringing together a bunch of 4090s?
For local LLMs Apple Silicon has really shown the value of shared memory, even if that comes at the cost of raw GPU power. Even if it's half the speed of an array of GPUs, being able to load the mid-sized models at all is a huge plus.
Not quite, but I do have an Ada 6000, which has 48GB.
RTX Pro 6000 Blackwell has 96GB VRAM.
It also costs 4x the entire Framework Desktop for just the card. If you're doing something professional that's probably worth it, but it's not a clear winner in the enthusiast space.
I think your options are generally:
0) A desktop PC with one or more graphics cards, or 1) A Mac with Apple Silicon
My experience with LLMs seems to indicate that the benchmark numbers are more and more detached from reality, at least my reality.
I tested this model with several of my Clojure problems and it is significantly worse than qwen3:30b-a3b-q4_K_M.
I don't know what to make of this. I don't trust benchmarks much anymore.
How did you test this? Note that this is not a regular coding model (i.e. write a function that does x). This is a fine-tuned model specifically post-trained on a cradle (open hands, ex open devin). So their main focus was to enable the "agentic" flows, with tool use, where you give the model a broad task (say a git ticket) and it starts by search_repo() or read_docs(), followed by read_file() in your repo, then edit_file(), then run_tests() and so on. It's intended to first solve those problems. They suggest using it w/ open hands for best results.
Early reports from reddit say that it also works in cline, while other stronger coding models had issues (they were fine-tuned more towards a step-by-step chat with a user). I think this distinction is important to consider when testing.
I did a very simple tool calling test and it was simply unable to call the tool and use the result.
Maybe it's specialized to use just a few very specific tools? Is there some documentation on how to actually set it up without requiring some weird external platform?
> "write a function that does x"
Which model is optimized to do that? This is what I want out of LLMs! And also talking high level architecture (without any code) and library discovery, but I guess the general talking models are good for that...
Any company in this space outside of the top few should be contributing to the open-source tools (Aider, OpenHands, etc.); that is a better bet than making your own tools from scratch to compete with ones from much bigger teams. A couple folks making harnesses work better with your model might yield improvements faster than a lot of model-tuning work, and might also come out of the process with practical observations about what to work on in the next spin of the model.
Separately, deploying more autonomous agents that just look at an issue or such just seems premature now. We've only just gotten assisted flows kind-of working, and they still get lost--get stuck on not-hard-for-a-human debugging tasks, implement Potemkin 'fixes', forget their tools, make unrelated changes that sometimes break stuff, etc.--in ways that imply that flow isn't fully-baked yet.
Maybe the main appeal is asynchrony/potential parallelism? You could tackle that different ways, though. And SWEBench might be a good benchmark still (focus on where you want to be, even if you aren't there yet), but that doesn't mean it represents the most practical way to use these tools day-to-day currently.
Maybe the EU should cover the cost of creating this agent/model, assuming it really delivers what it promises. It would allow Mistral to keep focusing on what they do and for us it would mean that the EU spent money wisely.
>Maybe the EU should cover the cost of creating this model
Wouldn't mind some of my taxpayer money flowing towards apache/mit licensed models.
Even if just to maintain a baseline alternative & keep everyone honest. Seems important that we don't have some large megacorps run away with this.
Pretty sure the EU paid for some supercomputers that AI startups can use and Mistral is partner in that program.
https://www.datacenterdynamics.com/en/news/french-data-cente... this Eclairion colo currently being built south of Paris, mostly for Mistral has received some public money (incl. 3M€ from the region https://www.iledefrance.fr/toutes-les-actualites/ia-un-super...)
Wow. I was just grabbing some models and I happened to see this one while I was messing with tool support in LLamaIndex. I have an agentic coding thing I threw together and I have been trying different models on it and was looking to throw ReAct at it to bring in some models that don't have tool support and this just pops into existence!
I'm not able to get my agentic system to use this model though as it just says "I don't have the tools to do this". I tried modifying various agent prompts to explicitly say "Use foo tool to do bar" without any luck yet. All of the ToolSpec that I use are annotated etc. Pydantic objects and every other model has figured out how to use these tools.
you can use constrained outptus for enforcing tool schemas any model can get it with a lil help
What's the play for smaller base model training companies like Mistral ?
Mistral's positioning as the European alternative doesn't seem to be sticking. Acquisition seems tricky given how inflection, character.ai and stability have got carved out. The big acquisition bucks are going to product companies (windsurf)
They could pivot up the stack, but then they'd be starting from scratch with a team that's ill-suited for product development.
The base model offerings from pretraining companies have been surprisingly myopic. Deepmind seems to be the only one going past the obvious "content gen/coding automation" verticals. There's a whole world out there. LLM product companies are fast acquiring pieces of the real money pie and smaller pretraining companies are getting left out.
______
edit: my comment rose to the top. It's early in the morning. Injecting a splash of optimism.
LLMs are hard, and giants like Meta are struggling to make steady progress. Mistrals models are cheap, competent, open-source-ish and don't come with AGI-is-imminent baggage. Good enough for me.
To my own question: They have a list of target industries at the top. https://mistral.ai/solutions#industry
Good luck to them.
I actually think the underdog, cheap, but still capable independent european alternative is a decent selling point. They have also branched out into specialised models, and custom training. as well as their ocr service.
Maybe someone here can suggest tools or at least where to look; what are the state-of-the-art models to run locally on relatively low power machines like a MacBook Air? Is there anyone tracking what is feasible given a machine spec?
"Apple Intelligence" isn't it but it would be nice to know without churning through tests whether I should bother keeping around 2-3 models for specific tasks in ollama or if their performance is marginal there's a more stable all-rounder model.
The best general model you can run locally is probably some version of Gemma 3 or the latest Mistral Small. On a Windows machine, this is limited by VRAM, since system RAM is too low-bandwidth to run models at usable speeds. On an M-series Mac, the system memory is on-die and fast enough to use. What you can run will be the total RAM, minus whatever MacOS uses and the space you want for other programs.
To determine how much space a model needs, you look at the size of the quantized (lower precision) model on HuggingFace or wherever it's hosted. Q4_K_M is a good default. As a rough rule of thumb, this will be a little over half the size of the parameters, if they were in gigabytes. For Devstral, that's 14.3GB. You will also need 1-8GB more than that, to store the context.
For example: A 32GB Macbook Air could use Devstral at 14.3+4GB, leaving ~14GB for the system and applications. A 16GB Macbook Air could use Gemma 3 12B at 7.3+2GB, leaving ~7GB for everything else. An 8GB Macbook could use Gemma 3 4B at 2.5GB+1GB, but this is probably not worth doing.
> An 8GB Macbook could use Gemma 3 4B at 2.5GB+1GB, but this is probably not worth doing.
I am currently using this model on a Macbook with 16GB ram, it is hooked up with a chrome extension that extracts text from webpages and logs to a file, then summarizes each page. I want to develop an episodic memory system, like MS Recall, but local, it does not leak my data to anyone else, and costs me nothing.
Gemma 3 4B runs under ollama and is light enough that I don't feel it while browsing. Summarization happens in the background. This page I am on is already logged and summarized.
I would recommend just trying it out! (as long as you have the disk space for a few models). llama.cpp[0] is pretty easy to download and build and has good support for M-series Macbook Airs. I usually just use LMStudio[1] though - it's got a nice and easy-to-use interface that looks like the ChatGPT or Claude webpage, and you can search for and download models from within the program. LMStudio would be the easiest way to get started and probably all you need. I use it a lot on my M2 Macbook Air and it's really handy.
[0] - https://github.com/ggml-org/llama.cpp
[1] - https://lmstudio.ai/
This doesn't do anything to answer the main question of what models they can actually run.
LM Studio will tell you if a specific model is small enough for your available RAM/VRAM.
I use qwen3:30b-a3b-q4_K_M for coding support and spam filtering, qwen2.5vl:32b-q4_K_M for image recognition/tagging/describing and sometimes gemma3:27b-it-qat for writing. All through Ollama, as that provides a unified interface, and then accessed from Emacs, command-line llm tool or my Clojure programs.
There is no single "best" model yet, it seems.
That's on an M4 Max with 64GB of RAM. I wish I had gotten the 128GB model, though — given that I run large docker containers that consume ~24GB of my RAM, things can get tight.
It's nice that Mistral is back to releasing actual open source models. Europe needs a competitive AI company.
Also, Mistral has been killing it with their most recent models. I pay for Le Chat Pro, it's really good. Mistral Small is really good. Also building a startup with Mistral integration.
mistral small 3.1 is also apache
> Devstral excels at using tools to explore codebases
As an AI and vibe coding newbie, how does that work? E.g. how would I use devstral and ollama and instruct it to use tools? Or would I need some other program as well?
In the Ollama API, you use the "tools" parameter to describe the available tools to the model, then use the "tool_calls" from the response to call the functions and send the results back to the model using "role": "tool".
Most of this is handled very easily by the ollama-python library, so you can integrate tool calling very simply in any script.
That said, this specific model was unable to call the functions and use the results in my "hello world" tests, so it seems it expects a few very specialized tools to be provided, which are defined by that platform they're advertising.
Right now the best tool calling model I've used is still qwen3, it works very reliably, and I can give it any ability I want and it'll use it when expected, even in /no_think mode.
it can use tool to explore directory like ls grep out of the box.
And how do the results compare to hosted LLMs like Claude 3.7?
Eh, different usecase entirely. I don't really compare these.
Different class. Same exact use case.
For which kind of coding would you use a subpar LLM ?
A LLM that I don’t host is a non-starter, so even a “subpar LLM” is better than someone’s cloud.
I'd use a "subpar" LLM for any coding practice where I want to do the bulk of the thinking and where I care about how much coal I'm burning.
It's kind-of like asking, for which kind of road-trip would you use a Corolla hatchback instead of a Jeep Grand Wagoneer? For me the answer would be "almost all of them", but for others that might not be the case.
In that case examples of which trips would be interesting so we can take inspiration from you
The kind of coding that happens after the internet goes down. How important of a use case that is depends heavily on why the internet went down.
Super weird benchmarks
from what I gather it's finetuned to use OpenHand specifically so shows value on thsoe benchmark that target a whole system as a blackbox (i.e. agent + llm) more than directly target the llm input/outputs
Yup the 1st comment says this https://www.reddit.com/r/LocalLLaMA/comments/1kryybf/mistral...
Curious to check this out, since they say it can run on a 4090 / Mac with >32 GB of RAM.
"Can run" is pretty easy, it's pretty small and quantized. It runs at 3.7 tokens/second on pure CPU with AMD 8945HS.
I can run it without issue on a 6800 XT with 64GB of RAM.
I just ran it on a 24GB M2 air. Slow, but functional.
But how does it compare to deepcoder?
Impressive performance!
The SWE bench is super impressive of model of any size. However just providing one benchmark results and having to do partnership with OpenHands seems like they focused too much on optimizing the number.
I know it's not the recommended runner (OpenHands), but running this on Cline (ollama back-end), it seemed absolutely atrocious at file reading and tool calling.
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I feel this is part of a larger and very old business trend.
But do we need 20 companies copying each other and doing the same thing?
Like, is that really competition? I'd say competition is when you do something slightly different, but I guess it's subjective based on your interpretation of what is a commodity and what is proprietary.
To my view, everyone is outright copying and creating commodity markets:
OpenAI: The OG, the Coke of Modern AI
Claude: The first copycat, The Pepsi of Modern AI
Mistral: Euro OpenAI
DeepSeek: Chinese OpenAI
Grok/xAI: Republican OpenAI
Google/MSFT: OpenAI clone as a SaaS or Office package.
Meta's Llama: Open Source OpenAI
etc...
I don't think they're actually the same thing, I definitely feel like Claude is much better with code than ChatGPT is so there are clearly differences in the capabilities of these models. One analogy that I find helpful here is the idea that these AIs are like animals. Just like there are animals of the same family (meaning they're genetically related to some degree) they still adapt to different niches. I see all these AI companies ultimately creating models analogous to that.
Some AIs will be good at coding (perhaps in a particular language or ecosystem), some at analyzing information and churning out a report for you, and some will be better at operating in physical spaces.
Deepseek and Mistral are both more open source than Lllama
Will check it out. I like that we are all on the same page that Openness is a numerical value rather than a boolean, the challenge now is how to measure and define it, especially with ML
Well, Llama’s licence says that I am not allowed to use it. It does not take much to be more open then that.
I think this just indicates that OpenAI's branding and marketing efforts worked on you?
I think llama is less open source than this mistral release
>Devstral is light enough to run on a single RTX 4090 or a Mac with 32GB RAM, making it an ideal choice for local deployment and on-device use
This is still too much, a single 4090 costs $3k
> a single 4090 costs $3k
What a ripoff, considering that a 5090 with 32GB of VRAM also currently costs $3k ;)
(Source: I just received the one I ordered from Newegg a week ago for $2919. I used hotstocks.io to alert me that it was available, but I wasn’t super fast at clicking and still managed to get it. Things have cooled down a lot from the craziness of early February.)
That's probably because the 5000 series seems to be a big let-down. It's pretty much identical to the 4000 series in efficiency; they've only increased performance by massively increasing power usage.
When I needed 21 3090s and none were available but for ridiculously high prices, I bought Dell Alienware comps, stripped them out, and sold the rest. Definitely made my money back mining for crypto with those cards. Dell surprisingly has a lot of computers with great RTX cards in stock.
I can get the 5090 for 1700 euros on Amazon Spain. But there is 95% chance it is a scammy seller :P
I'm not sure where you are getting these prices from, but the cheapest 5090 I can find is 2755 on amazon Germany from the gigabyte store.
The vendor disappeared a few hours after my comment and now his Amazon store doesn't exist anymore, replaced by another vendor with no sales but normal prices :) . Even if the seller was written as a german company "Wundshop" it was only registered on Amazon Spain
I receive NXDOMAIN for that hostname.
It's hotstock.io, no plural.
If it runs on 4090, it also runs on 3090 which are available used for 600 EUR.
More like 700 € if you are lucky. Prices are still not back down from the start of the AI boom.
I am hopeful that the prices will drop a bit more with Intel's recently announced Arc Pro B60 with 24GB VRAM, which unfortunately has only half the memory bandwidth of the RTX 3090.
Not sure why other hardware makers are so slow to catch up. Apple really was years ahead of the competition with the M1 Ultra with 800 GB/s memory bandwidth.
I managed to install and run it on my Razer Edge Laptop with an Nvidia RTX 4080, using Ollama.
It works but the tokens per sec is very slow. It did complete a TypeScript task example succinctly.
A Mac Mini with 32GB RAM costs $999. Just start with the base model and don't upgrade the CPU, GPU, SSD, or Ethernet port.
> a single 4090 costs $3k
I hope not. Mine was $1700 almost 2 years go, and the 5090 is out now...
The 4090 went up in price for a while as the 5000 marketing percolated and people wanted an upgrade they could actually buy.
Is there an equivalence between gpu vram and mac ram?
For loading models, it's exactly the same. Mac ram is fully (more or less) shared between CPU/GPU.