this post was submitted on 07 Sep 2026
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It's kinda surprising,
I know specifically where one of the big ones hosts its models and its not there, but I guess they could have infrastructure in there.
They’re oversold though, especially prompt caching and the parameter count war
The US model is that they think more training compute and parameters will result in the winning model, while the Chinese are focusing on RL and parameters efficiency due to compute limits.
Considering which country is better at building power stations, that's a fascinating dichotomy.
The US going for brute force when the brute force is more available in China... Priceless irony.
The efficiency of Chinese models really is impressive. I generated sooo much code yesterday with Qwen3.6 35B-A3B running on an RTX 5060 Ti 16GB (+ a little CPU offloading). It got the jobs done at ~50 tokens/sec.
(It's not super complex code, just some scripts that I would not have taken to time to write manually.)
I'd love to upgrade to something with more VRAM, but even my current card has doubled in price since I bought it last year 😬
Show me your set up!
There's not really anything interesting to show. It's just a home server in a 13 year old desktop ATX case.
There's no desk, monitor, keyboard, or mouse... But also no cool server rack.
Function over form, and it sits in a spare bedroom out of sight.
EDIT: I found the receipt for the case. It's a Cougar Volant Black Steel mid tower, purchased in 2013. So my server just looks like this:
Gotta love the black slab.
I meant your LLM stack lol. I just have an RX 6800 XT in my main Linux PC for inference, but it has to share VRAM with the DE. Maybe I'll set it up for remote development from my laptop instead to free up VRAM.
What are you using? vLLM? llama.cpp? Which params? How much CPU offloading? Do you use draft models? Is it a MoE model? Have you tried llama-swap? Which agentic front-end are you using? I presume you set it up to access it without SSH'ing into the machine, did you do anything special or is it just a raw unsecured open port on the machine to the LAN?
Ohhh lol. Yeah it's Llama-swap, running llama.cpp for now, but might add vLLM to the llama-swap config to experiment with NVFP4.
I mainly use MoE models so I can get decent speed while using a 150-200k context window. My go-to model has been Qwen3.6 35B-A3B for a while. I tried Qwen3.8 27B, but it was too slow.
Gemma4 26B-A4B also runs nice and fast, but I generally get better results from Qwen3.6. I don't remember exactly how much CPU offloading is happening, but it's not much. As long as I can get like 40-50 tokens/sec, I'm usually satisfied enough.
For the coding harness, I've been running Pi in an Apple Container (sort of like Podman, but better isolation in a microvm). Though, I recently configured VS Code to use LLMs on my server, and it was actually pretty decent. Still need to explore a bit more, but so far VS Code's AI capabilities seem much better than they were a year ago (they seemed way behind, back then).
Also, I don't connect any harness directly to llama-swap. I have another container running Caddy, which acts as a gateway to AI providers. For other services (e.g. OpenRouter), the API key is injected in the Caddy container. I don't like having API keys or secrets anywhere where LLMs can read them. It's not so bad for my own self-hosted LLMs, but not cool to send secrets to a server owned by someone else.
How has tool use been for you? I struggled a lot with tool use with Gemma and Qwen, to the point where I needed to build a healing layer.
Regarding the coding harness, I was looking for something CLI-based or JetBrains-based, and I haven't had much luck getting my local llama.cpp models playing ball with OpenCode. They keep losing context and misusing tools.
I'm not too familiar with Apple containers as I'm running a full Linux stack, but I'll give Pi a try, seems interesting! Does it work for coding tasks or is it strictly an "orchestrator"?
eh, just
systemctl isolate multi-user.targetCorrect, that's how I would do it, but then I need another machine to act as a head.
If your MB has onboard graphics, maybe you could mask the GPU and just pass it off to a container running the LLMs I guess
Reads like American vs European/Asian cars
Always ready to try brute force first. And then some other, less palatable options if that doesn't work, like slightly less brute force.