d4rkp4ttern7 hours ago
You can use llama.cpp server directly to serve local LLMs and use them in Claude Code or other CLI agents. I’ve collected full setup instructions for Gemma4 and other recent open-weight LLMs here, tested on my M1 Max 64 GB MacBook:
https://pchalasani.github.io/claude-code-tools/integrations/...
The 26BA4B is the most interesting to run on such hardware, and I get nearly double the token-gen speed (40 tok/s) compared to Qwen3.5 35BA3B. However the tau2 bench results[1] for this Gemma4 variant lag far behind the Qwen variant (68% vs 81%), so I don’t expect the former to do well on heavy agentic tool-heavy tasks:
peder6 hours ago
Did you have any Anthropic vs OpenAI specification issues with Claude Code? I have been using mlx_vlm and vMLX and I get 400 Bad Request errors from Claude Code. Presumably you're not seeing those issues with llama-server ?
d4rkp4ttern4 hours ago
Correct, no issues because since at least a few months, llama.cpp/server exposes an Anthropic messages API at v1/messages, in addition to the OpenAI-compatible API at v1/chat/completions. Claude Code uses the former.
selectodude4 hours ago
I’ve jumped over to oMLX. A ton of rough edges but I think it’s the future.
vlowther2 hours ago
Same. Opencode + oMLX (0.3.4) + unsloth-Qwen3-Coder-Next-mlx-8bit on my M5 Max w 128GB is the sweet spot for me locally. The prompt decode caching keeps things coherent and fast even when contexts get north of 100k tokens.
tatrions6 hours ago
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seifbenayed199211 hours ago
Local models are finally starting to feel pleasant instead of just "possible." The headless LM Studio flow is especially nice because it makes local inference usable from real tools instead of as a demo.
Related note from someone building in this space: I've been working on cloclo (https://www.npmjs.com/package/cloclo), an open-source coding agent CLI, and this is exactly the direction I'm excited about. It natively supports LM Studio, Ollama, vLLM, Jan, and llama.cpp as providers alongside cloud models, so you can swap between local and hosted backends without changing how you work.
Feels like we're getting closer to a good default setup where local models are private/cheap enough to use daily, and cloud models are still there when you need the extra capability.
SeriousM9 hours ago
How does cloclo differ from pi-mono?
seifbenayed19922 hours ago
pi-mono is a great toolkit — coding agent CLI, unified LLM API, web UI, Slack bot, vLLM pods.
cloclo is a runtime for agent toolkits. You plug it into your own agents and it gives them multi-agent orchestration (AICL protocol), 13 providers, skill registry, native browser/docs/phone tools, memory, and an NDJSON bridge. Zero native deps.
hackerman7000011 hours ago
The real story here isn't Gemma 4 specifically, it's that the harness and the model are now fully decoupled. Claude Code, OpenCode, Pi, Codex all work with any backend. The coding agent is becoming a commodity layer and the competition is moving to model quality and cost. Good for users, bad for anyone whose moat was the harness
satvikpendem4 hours ago
Sounds like the exact opposite, models are being commoditized while the harness and tooling around a model is what actually gets significant gains, especially with RL around specific models.
For example, this article was posted recently, Improving 15 LLMs at Coding in One Afternoon. Only the Harness Changed [0].
bckr4 hours ago
I think it’s ALL getting commoditized. The winners here are engineers (who are onboard with the agentic surge) and, hopefully, users who get more and better software.
vkou33 minutes ago
> hopefully, users who get more and better software.
Users are definitely going to get more software and more features and redesigns in the software they use, but I have strong doubts that it's going to get better.
If pre-LLM developer productivity was used to build all sorts of deranged anti-user promo-padding bullshit, imagine how much more of it we can do with a 2x more productive employee base.
chappyasel3 hours ago
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Havoc3 hours ago
You could always point Claude Code and open code at a local http endpoint
jeremie_strand9 hours ago
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trvza day ago
ollama launch claude --model gemma4:26bgcampos18 hours ago
You need to increase the context window size or the tool calling feature wont work
mil2217 hours ago
For those wondering how to do this:
OLLAMA_CONTEXT_LENGTH=64000 ollama serve
Codex also works:
ollama launch codex --model gemma4:26bdatadrivenangela day ago
It's amazing how simple this is, and it just works if you have ollama and claude installed!
pshirshova day ago
For some reason, that doesn't work for me, claude never returns from some ill loop. Nemotron, glm and qwen 3.5 work just fine, gemma - doesn't.
trvz21 hours ago
Since that defaults to the q4 variant, try the q8 one:
ollama launch claude --model gemma4:26b-a4b-it-q8_0pshirshov20 hours ago
Even tried gemma4:31b and gemma4:31b with 128k context (I have 72GiB VRAM). Nothing. I'm cursed I guess. That's ollama-rocm if that matters (I had weird bugs on Vulkan, maybe gemma misbehaves on radeons somehow?..).
UPD: tried ollama-vulkan. It works, gemma4:31b-it-q8_0 with 64k context!
alfiedotwtf11 hours ago
The default context is 128k for the smaller Gemma 4’s and 256k for the bigger ones, so you’re cutting off context and it doesn’t know how to continue.
Bump it to native (or -c 0 may work too)
pshirshov8 hours ago
In that case the model descriptor on ollama.com is incorrect, because it defaults to 16k. So I have to manually change that to 64/128k. I think you are talking about maximum context size.
trvz7 hours ago
No, the default context in Ollama varies by the memory available: https://docs.ollama.com/context-length
martinalda day ago
Just FYI, MoE doesn't really save (V)RAM. You still need all weights loaded in memory, it just means you consult less per forward pass. So it improves tok/s but not vram usage.
functional_dev3 hours ago
This confused me at first as well.. inactive experts skip compute, but weights are sill loaded. So memory does not shrink at all.
I found this visualisation helpful - https://vectree.io/c/sparse-activation-patterns-and-memory-e...
IceWrecka day ago
It does if you use an inference engine where you can offload some of the experts from VRAM to CPU RAM. That means I can fit a 35 billion param MoE in let's say 12 GB VRAM GPU + 16 gigs of memory.
Yukonv21 hours ago
With that you are taking a significant performance penalty and become severely I/O bottlenecked. I've been able to stream Qwen3.5-397B-A17B from my M5 Max (12 GB/s SSD Read) using the Flash MoE technique at the brisk pace of 10 tokens per second. As tokens are generated different experts need to be consulted resulting in a lot of I/O churn. So while feasible it's only great for batch jobs not interactive usage.
IceWreck20 hours ago
> So while feasible it's only great for batch jobs not interactive usage.
I mean yeah true but depends on how big the model is. The example I gave (Qwen 3.5 35BA3B) was fitting a 35B Q4 K_M (say 20 GB in size) model in 12 GB VRAM. With a 4070Ti + high speed 32 GB DDR5 ram you can easily get 700 token/sec prompt processing and 55-60 token/sec generation which is quite fast.
On the other hand if I try to fit a 120B model in 96 GB of DDR5 + the same 12 GB VRAM I get 2-5 token/sec generation.
zozbot23420 hours ago
Your 120B model likely has way more active parameters, so it can probably only fit a few shared layers in the VRAM for your dGPU. You might be better off running that model on a unified memory platform, slower VRAM but a lot more of it.
zozbot23420 hours ago
10 tok/s is quite fine for chatting, though less so for interaction with agentic workloads. So the technique itself is still worthwhile for running a huge model locally.
charcircuita day ago
You never need to have all weights in memory. You can swap them in from RAM, disk, the network, etc. MOE reduces the amount of data that will need to be swapped in for the next forward pass.
martinald21 hours ago
Yes you're right technically, but in reality you'd be swapping them the (vast?) majority in and out per inference request so would create an enormous bottleneck for the use case the author is using for.
zozbot23420 hours ago
With unified memory, reading from RAM to GPU compute buffer is not that painful, and you can use partial RAM caching to minimize the impact of other kinds of swapping.
mikkupikku8 hours ago
In practical terms, is this kind of architecture available to consumers except through Apple?
the_pwner2248 hours ago
AMD Strix Halo. Available in the Framework desktop, various mini PCs, and the Asus Rog Flow Z13 "gaming tablet." The Z13 is still at $2700 for 128 GB which is an incredible deal with today's RAM prices.
There's also the Nvidia DGX Spark.
charcircuit19 hours ago
You don't have to only have the experts being actively used in VRAM. You can load as many weights as will fit. If there is a "cache miss" you have to pay the price to swap in the weights, but if there is a hit you don't.
drob5184 hours ago
Seems like this might be a great way to do web software testing. We’ve had Selenium and Puppeteer for a long time but they are a bit brittle with respect to the web design. Change something about the design and there’s a high likelihood that a test will break. Seems like this might be able to be smarter about adapting to changes. That’s also a great use for a smaller model like this.
robot_jesus10 minutes ago
Yeah. I think that's an interesting use case. Especially if I can kick it off or schedule it when I'm not actively working. Inference speed (especially with tool calling involved) won't be great on my machines, but if I schedule nightly usability tests of dev sites while I sleep, that could be really cool.
edinetdb17 hours ago
Claude Code has become my primary interface for iterating on data pipeline work — specifically, normalizing government regulatory filings (XBRL across three different accounting standards) and exposing them via REST and MCP.
The MCP piece is where the workflow gets interesting. Instead of building a client that calls endpoints, you describe tools declaratively and the model decides when to invoke them. For financial data this is surprisingly effective — a query like "compare this company's leverage trend to sector peers over 10 years" gets decomposed automatically into the right sequence of tool calls without you hardcoding that logic.
One thing I haven't seen discussed much: tool latency sensitivity is much higher in conversational MCP use than in batch pipelines. A 2s tool response feels fine in a script but breaks conversational flow. We ended up caching frequently accessed tables in-memory (~26MB) to get sub-100ms responses. Have you noticed similar thresholds where latency starts affecting the quality of the model's reasoning chain?
tatrions15 hours ago
Interesting question. I've seen the threshold land around 300-500ms per tool call in practice. Below that, multi-step chains feel fluid. Above it the compounding gets you -- a 20-step chain at 2s/call is 40s wall time minimum, and I've noticed models tend to generate more filler reasoning between slow tool calls that just bloats context without adding value.
Your caching approach sounds right. The other thing that made a big difference for me was reducing round trips -- bundling related data into a single tool response (table + schema + metadata in one call vs three separate calls) helped more than speeding up individual calls.
mjlee7 hours ago
I find MCP beneficial too, but do be aware of token usage. With a naive implementation MCP can use significantly more input tokens (and context) than equivalent skills would. With a handful of third party MCPs I’ve seen tens of thousands of tokens used before I’ve started anything.
Here’s an article from Anthropic explaining why, but it is 5 months old so perhaps it's irrelevant ancient history at this point.
https://www.anthropic.com/engineering/code-execution-with-mc...
chappyasel3 hours ago
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vbtechguyopa day ago
Here is how I set up Gemma 4 26B for local inference on macOS that can be used with Claude Code.
canyon289a day ago
This is a nice writeup!
ttul5 hours ago
I could see a future in which the major AI labs run a local LLM to offload much of the computational effort currently undertaken in the cloud, leaving the heavy lifting to cloud-hosted models and the easier stuff for local inference.
dominotw4 hours ago
wouldnt that be counter to their whole business model?
ttul3 hours ago
I don't think so. Acquiring hardware for inference is a chokepoint on growth. If they can offload some inference to the customer's machine, that allows them to use more of their online capacity to generate money.
Imanari5 hours ago
How well do the Gemma 4 models perform on agentic coding? What are your impressions?
jonplacketta day ago
So wait what is the interaction between Gemma and Claude?
unsnap_bicepsa day ago
lm studio offers an Anthropic compatible local endpoint, so you can point Claude code at it and it'll use your local model for it's requests, however, I've had a lot of problems with LM Studio and Claude code losing it's place. It'll think for awhile, come up with a plan, start to do it and then just halt in the middle. I'll ask it to continue and it'll do a small change and get stuck again.
Using ollama's api doesn't have the same issue, so I've stuck to using ollama for local development work.
keerthikoa day ago
Claude Code is fairly notoriously token inefficient as far as coding agent/harnesses go (i come from aider pre-CC). It's only viable because the Max subscriptions give you approximately unlimited token budget, which resets in a few hours even if you hit the limit. But this also only works because cloud models have massive token windows (1M tokens on opus right now) which is a bit difficult to make happen locally with the VRAM needed.
And if you somehow managed to open up a big enough VRAM playground, the open weights models are not quite as good at wrangling such large context windows (even opus is hardly capable) without basically getting confused about what they were doing before they finish parsing it.
unsnap_bicepsa day ago
I use CC at work, so I haven't explored other options. Is there a better one to use locally? I presumed they were all going to be pretty similar.
jaggederesta day ago
If you want to experiment with same-harness-different-models Opencode is classically the one to use. After their recent kerfluffle with Anthropic you'll have to use API pricing for opus/sonnet/haiku which makes it kind of a non-starter, but it lets you swap out any number of cloud or local models using e.g. ollama or z.ai or whatever backend provider you like.
I'd rate their coding agent harness as slightly to significantly less capable than claude code, but it also plays better with alternate models.
blitzar21 hours ago
I am hopeful the leaked claude code narrows the capability, perhaps even googles offering will be viable once they borrow some ideas from claude.
andhuman12 hours ago
I have good experience with Mistral Vibe.
satvikpendem17 hours ago
OpenCode
storusa day ago
Can't you use Claude caveman mode?
tatrions15 hours ago
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aplomb102621 hours ago
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mbesto21 hours ago
I don't get why I would use Claude Code when OpenCode, Cursor, Zed, etc. all exist, are "free" and work with virtually any llm. Seems like a weird use case unless I'm missing something.
superb_dev20 hours ago
From my experience, Claude Code is just better. Although I recently started using Zed and it’s pretty good
blitzar21 hours ago
previously I have found claude code to be just better than the alternatives, using large models or local. It is, however, closer now and not much excuse for the competition after the claude code leak. Personally, I will be giving this a go with OpenCode.
panagathon16 hours ago
> I don't get why I would use Claude Code when OpenCode, Cursor, Zed, etc. all exist, are "free" and work with virtually any llm. Seems like a weird use case unless I'm missing something.
I'm with you on this. I've tried Gemma and Claude code and it's not good. Forgets it can use bash!
However, Gemma running locally with Pi as the harness is a beast.
bdangubic21 hours ago
this is like asking why use intellij or vscode or … when there is vim and emacs
NamlchakKhandro19 hours ago
No it's more like, why use a Microsoft paid for distro of nvim when lazyvim, astronvim exist
asymmetric21 hours ago
Is a framework desktop with >48GB of RAM a good machine to try this out?
pshirshov20 hours ago
Only for chat sessions, not for agentic coding. It's just too slow to be practical (10 minutes to answer a simple question about a 2k LoC project - and that's with a 5070 addon card).
ac2912 hours ago
This article is about a MoE model with only 4B active parameters, it shouldn't take 10 minutes to answer a question about a small project.
I measured a 4bit quant of this model at 1300t/s prefill and ~60t/s decode on Ryzen 395+.
nl15 hours ago
Doesn't the framework desktop have a Ryzen 395 AI? That's a unified memory architecture like the Macs.
pshirshov5 hours ago
Ah, forgot to add, it's not really "unified" you have to explicitly specify your allocations. You may have a reasonably good 48gb chunk assigned to the GPU, but that DDR5 is 5-10 times slower than GDDR/HBM and the GPU itself isn't stellar.
So, framework laptops are great for chatting but nearly useless in agentic coding.
My Radeon W7900 answers a question ("what is this project") in 2 minutes, it takes my Framework 16 with 5070 addon around 11 minutes without the addon - around 23 (qwen 3.5 27b, claude code)
pshirshov8 hours ago
That's discrete DDR5, it's not as fast as your regular VRAM.
janalsncm11 hours ago
Qwen3-coder has been better for coding in my experience and has similar sizes. Either way, after a bunch of frustration with the quality and price of CC lately I’m happy there are local options.
AbuAssar10 hours ago
omlx gives better performance than ollama on apple silicon
Someone1234a day ago
Using Claude Code seems like a popular frontend currently, I wonder how long until Anthropic releases an update to make it a little to a lot less turn-key? They've been very clear that they aren't exactly champions of this stuff being used outside of very specific ways.
nerdixa day ago
I don't think there is any incentive to do so right now because the open models aren't as good. The vast majority of businesses are going to just pay the extra cost for access to a frontier model. The model is what gives them a competitive advantage, not the harness. The harness is a lot easier to replicate than Opus.
There are benefits too. Some developers might learn to use Claude Code outside of work with cheaper models and then advocate for using Claude Code at work (where their companies will just buy access from Anthropic, Bedrock, etc). Similar to how free ESXi licenses for personal use helped infrastructure folks gain skills with that product which created a healthy supply of labor and VMware evangelists that were eager to spread the gospel. Anthropic can't just give away access to Claude models because of cost so there is use in allowing alternative ways for developers to learn how to use Claude Code and develop a workflow with it.
deskamess19 hours ago
Are the Claude Code (desktop) models very different from what Bedrock has? I thought you could hook up VSCode (not Claude Desktop) to Bedrock Anthropic models. Are there features in Claude Desktop that are not in VSCode/cli?
chvida day ago
Is it not about the same as using OpenCode?
And is running a local model with Claude Code actually usable for any practical work compared to the hosted Anthropic models?
falcor8421 hours ago
Well, if they did, it would probably be shooting themselves in the foot, seeing that the Claude Code source is out there now, and people are waiting for an excuse to "clean-room" reimplement and fork it
moomina day ago
Right now it suits them down to the ground. You pay for the product and you don’t cost their servers anything.
phainopepla2a day ago
You don't pay anything to use Claude Code as a front end to non-Anthropic models
quinnjha day ago
so no subscription is needed?
kenmacd19 hours ago
not to use the cli tool. You can install it and change the settings to point to pretty much any other model.
It's an okay-enough tool, but I don't see a lot of point in using it when open sources tools like Pi and OpenCode exist (or octofriend, or forge, or droid, etc).
alfiedotwtf11 hours ago
Yet Codex specifically aims out to be compatible with all backends! Up until Gemma 4 though it’s been pretty solid, but totally fails with unknown tool (I’m guessing a template issue)
wyrea day ago
I think CC is popular because they are catering to the common denominator programmer and are going to continue to do that, not because CC is particularly turn-key.
jedisct14 hours ago
Running Gemma 4 with llama.cpp and Swival:
$ llama-server --reasoning auto --fit on -hf unsloth/gemma-4-26B-A4B-it-GGUF:UD-Q4_K_XL --temp 1.0 --top-p 0.95 --top-k 64
$ uvx swival --provider llamacpp
Done.
aetherspawn19 hours ago
Can you use the smaller Gemma 4B model as speculative decoding for the larger 31B model?
Why/why not?
MeetRickAI19 hours ago
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tiku5 hours ago
I hate that my M5 with 24 gb has so much trouble with these models. Not getting any good speeds, even with simple models.
meidad_g6 hours ago
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techpulselab19 hours ago
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meidad_ga day ago
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maxbeech9 hours ago
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aplomb102619 hours ago
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inzlab19 hours ago
awesome, the lighter the hardware running big softwares the more novelty.
NamlchakKhandro19 hours ago
I don't know why people bother with Claude code.
It's so jank, there are far superior cli coding harness out there
loveparade19 hours ago
What do you recommend? I've tried both pi and opencode and both are better than claude imo, but I wonder if there are others.
tarruda18 hours ago
Codex is the best out-of-box experience, especially due to its builtin sandboxing. Only drawback is that its edit tool requires the LLM to output a diff which only GPTs are trained to do correctly.
loveparade18 hours ago
Interesting, I don't like codex exactly because of its built-in sandboxing. If I need a sandbox I rather do a simple bwrap myself around the agent process, I prefer that over the agent cli doing a bunch of sandboxing magic that gets in my way.
prettyblocks17 hours ago
how is codex sandbox different from /sandbox on claude code?
dimgl19 hours ago
Vagueposting in Hacker News?
z0mghii19 hours ago
Can you elaborate what is jank about it?
threethirtytwo10 hours ago
it has visual artifacts when inferencing.
smcleod8 hours ago
Did you try the MLX model instead? In general MLX tends provide much better performance than GGUF/Llama.cpp on macOS.