simonw
nico
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
zackify
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.
elAhmo
How is it great that it takes 1 minute for initial prompt processing?
nico
Have you tried using mlx or Simon Wilson’s llm?
asimovDev
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
jabroni_salad
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.
nico
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
segmondy
People have all sorts of hardware, TPS is meaningless without the full spec of the hardware, and GPU is not the only thing, CPU, ram speed, memory channel, PCIe speed, inference software, partial CPU offload? RPC? even OS, all of these things add up. So if someone tells you TPS for a given model, it's meaningless unless you understand their entire setup.
ivanvanderbyl
I’ve been playing around with Zed, supports local and cloud models, really fast, nice UX. It does lack some of the deeper features of VSCode/Cursor but very capable.
mrshu
ra-aid works pretty well with Ollama (haven't tried it with Devstral yet though)
lis
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.
ericb
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?
lis
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.
zackify
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.
davedx
I couldn’t run it on my 16gb MBP (I tried, it just froze up, probably lots of swapping), they say it needs 32gb
ics
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.
null
rahimnathwani
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.
oofbaroomf
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.
AstroBen
extraordinary.. or suspicious that the benchmarks aren't doing their job
sagarpatil
They are referring to SWE bench lite. Just want to make sure you are too.
svantana
Where did you get that idea? In the post they are repeatedly referring to SWEBench-Verified and nothing else.
sagarpatil
Sorry. I was wrong.
falcor84
Just to confirm, are you referring to Claude 3.7?
oofbaroomf
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.
ttoinou
And it is a very good LLM. Some people complain they don't see an improvement with Sonnet 3.7
Deathmax
Also known as Claude 3.5 Sonnet V2 on AWS Bedrock and GCP Vertex AI
SkyPuncher
> colloquially referred to as Claude 3.6
Interesting. I've never heard this.
moffkalast
The model formerly known as Claude 3.6 Sonnet?
dismalaf
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.
gunalx
mistral small 3.1 is also apache
solomatov
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.
resource_waste
[flagged]
solomatov
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.
Havoc
They're all quite easy to strip of protections and I don't think anyone doing unethical stuff is big on following licenses anyway
orbisvicis
I'm not sure what you're trying to imply... only rogue software developers use devstral?
portaouflop
TIL Open Source is only used for unethical purposes
simonw
What's different between the ethics of Mistral and Gemma?
Philpax
I think their point was more that Gemma open models have restrictive licences, while some Mistral open models do not.
dismalaf
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.
sofixa
> 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.
johnQdeveloper
*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.
throwaway314155
> 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.
johnQdeveloper
https://ollama.com/library/devstral
I believe its just a HTTP wrapper and terminal wrapper around llama.cpp with some modifications/fork.
throwaway314155
Does ollama have support for cpu offloading?
CSMastermind
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.
Ancapistani
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.
idonotknowwhy
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 :)
christophilus
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).
tripplyons
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".
zackify
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
pram
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
bicepjai
Do you also have tokens per second metric ?
discordance
How many tokens per second are you both getting?
klooney
AMD is going to be off the beaten path, you're likely to have more success/less boring plumbing trouble with nVidia.
lolinder
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.
kookamamie
Not quite, but I do have an Ada 6000, which has 48GB.
karolist
RTX Pro 6000 Blackwell has 96GB VRAM.
snitty
I think your options are generally:
0) A desktop PC with one or more graphics cards, or 1) A Mac with Apple Silicon
ddtaylor
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.
tough
you can use constrained outptus for enforcing tool schemas any model can get it with a lil help
qwertox
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.
Havoc
>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.
dismalaf
Pretty sure the EU paid for some supercomputers that AI startups can use and Mistral is partner in that program.
jgtrosh
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...)
jwr
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.
NitpickLawyer
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.
jwr
I didn't actually even test tool calling. I have two test cases that I use for all models: one is a floating-point equality function, which is quite difficult to get right, and another is a core.async pack-into-batches! function which has the following docstring:
"Take items from `input-ch` and group them into `batch-size` vectors. Put these onto `output-ch`. Once items
start arriving, if `batch-size` items do not arrive within `inactivity-timeout`, put the current incomplete
batch onto `output-ch`. If an anomaly is received, passes it on to `output-ch` and closes all channels. If
`input-ch` is closed, closes `output-ch`.
If `flush-predicate-fn` is provided, it will get called with two parameters: the currently accumulated
batch (guaranteed to have at least one item) and the next item. If the function returns a truthy value, the
batch will get flushed immediately.
If `convert-batch-fn` is provided, it will get called with the currently accumulated batch (guaranteed to
have at least one item) and its return value will be put onto `output-ch`. Anomalies bypass
`convert-batch-fn` and get put directly onto `output-ch` (which gets closed immediately afterwards)."
I ask the model to review the code and tell me if there are improvements that can be made. Big (online) models can do a pretty good job with the floating point equality function, and suggest something at least in the ballpark for the async code. Small models rarely get everything right, but some of their observations are good.
desdenova
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?
tasuki
> "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...
ics
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.
Miraste
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.
visarga
> 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.
thatcherc
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/
Etheryte
This doesn't do anything to answer the main question of what models they can actually run.
tuesdaynight
LM Studio will tell you if a specific model is small enough for your available RAM/VRAM.
jwr
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.
twotwotwo
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.
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!