Currently on my 9 year old laptop with a GTX 1070 Max-Q 8GB vram, 16 gb ddr4 ram and i7 8750h. I am running a finetuned Qwen 3.5-35B-A3B (Tiel-Coder-35B-A3B-GGUF-MTP) with 4-bit quant on a llamacpp fork (Turboquant) with 131k context at around 16-19 tk/s and 350-500 tk/s prefil using Pi harness. It is amazing for writing scripts and doing medium coding tasks and summarizing text for me. After around 100k context it goes down to 9 tk/s but that is to be expected.
If you ever ask any frontier LLM or these types of benchmarks, they would probably say that the best model I can run on my system is a crappy 9B model or something, which is just completely false. So I really wonder how much more you can pull out from more powerful systems and much better open weight models.
I'm personally sitting on an extremely limited Geforce GTX 1660 6GB, and would like to see how far one could go even with such a crappy card.
At least it was quite amazing to see the difference between CPU-based and even this crappy GPU-based inference when trying out some tiny models in jan.ai. It's still like orders of magnitude faster at least.
Use Vulkan build of llama.cpp to run low parameter MoE models in your IGPU and CUDA llama.cpp to run dense models in your DGPU.
Ofcourse you need to set your expectations right on what problem you want to solve, I've not been happy with using quantized models for coding even on a decent laptop; where as for NLP, ASR, STT etc. work great with this setup.
I might write more about this setup soon.
I tried llama.cpp with offloading "all what could be" to my GPU (GTX 750 Ti 2 GB). I could run local Qwen2.5-Coder quite fast (I do not remember but something like 25-30 t/s). However this model generates bad code (I tried Python and JavaScript). So, for my setup it is better to chat with free ChatGPT and do copying-pasting between web chat and code editor.
Is it even worth for trying something on my GPU? What do you think?
And yes, I use Claude Code (Opus 5.5) and Codex in my job but for personal stuff I would prefer trying free solutions (it is the reason I tried local LLM). I do not want to buy subs, at leats for now.
I am running on a Ryzen 5950X, 64GB DDR4 Ram and a Radeon RX 6700 XT with 12GB... I don't expect wonders, but I have between 11 and 120 (!) t/s.
I told it an open ended question: "What would you improve in this file at {location} in {bigger project}" and the context window seems to be too small. It's running in circles.
I must be doing something wrong.
Again, I am not expecting wonders and I am totally willing to accept that "I am holding it wrong".
llama-server \ -m ~/models/tiel-coder/Tiel-Coder-35B-A3B-MTP-UD-IQ4_XS.gguf \ -ngl 20 \ -c 16384 \ --jinja \ --alias tiel-coder
using it in opencode
{ "$schema": "https://opencode.ai/config.json", "provider": { "llamacpp": { "npm": "@ai-sdk/openai-compatible", "name": "Local llama.cpp", "options": { "baseURL": "http://127.0.0.1:8080/v1" }, "models": { "tiel-coder": { "name": "Tiel-Coder 35B A3B", "limit": { "context": 16384, "output": 4096 } } } } }, "model": "llamacpp/tiel-coder" }
in my use case, the model might need to analyze a given task first then generate and execute a database query and finally send the results back to the client. to what extent could your setup handle this kind of workflow and how accurate are the results?
good job.
I don't think in general people care about long list of models, they just care about the best one.
Also dual gpu is common setup, there should be ie. 2x 4090.
- multi GPU configuration (hetero and homo)
- PCIe gen chooser (3.0/4.0/5.0)
- is nvlink enabled (2 cards, 2 x 2 cards, 3 x 2 cards)
- is PCIe bifurcated or CPU native (threadripper pro for example)
Depending on who you sign up for, that's within the introductory credits (usually like 5-10$) so it's actually $0.
It won't win benchmarks of course, but 20 tps generation is okay and letting it chug away on my local kubernetes cluster to troubleshoot is pretty nice. First start is pretty slow though, parsing is around 100-200 tps. Which is crap for long questions or harnesses like opencode which start around with thousands of tokens. Once cache is warm, pretty enjoyable.
I think it could have way more use cases where it's just 'good enough'. Email categorization, voice commands for the home. 1 year ago this was still a very miserable experience, but qwen 9B + better cache quantization and more tricks in the latest llm engines makes it viable.
Will it do difficult architectural refactors by just giving a one-shot prompt like opus allows, nope, not gonna happen.
Yeah simple, but menial refactors where IDEs don't have clear cut refactors yet. Or languages like CUE which has awesome tooling, but not so great refactors in its lsp yet.
Unless of course he's driving some weird lambo that dumps 200k of context into their chats. Who knows, I feel like we're on a yellow submarine.
I wished there was such a tool before picking my hardware, it's useful. But the estimates needs to be somewhat correct.