121–130 of 848 posts
I hope really badly that we'll get a new 35B A3B or similar MoE model! I also miss the Qwen 3 Coder Next, which was 80B A3B, there are quite a few use cases where a non-dense model Also alternate link for viewing the images without signing in: https://xcancel.com/Alibaba_Qwen/status/2088280182356611304
Since it might be helpful to some, here's my current commandline for llama.cpp running on an RTX 4090 with my monitor moved to the iGPU to free up all of its VRAM. llama-server -m Qwen3.8-27B-IQ4_NL.gguf --mmproj mmproj-BF16.gguf -c 170000 --parallel 1 -ngl -1 --cache-type-k q8_0 --cache-type-v q8_0 -b 1024 -ub 512 --flash-attn on --no-context-shift --no-mmproj-offload --spec-type draft-mtp --spec-draft-n-max 5 --spe…
I hope really badly that we'll get a new 35B A3B or similar MoE model! I also miss the Qwen 3 Coder Next, which was 80B A3B, there are quite a few use cases where a non-dense model Also alternate link for viewing the images without signing in: https://xcancel.com/Alibaba_Qwen/status/2088280182356611304
Same here! Qwen3.6-35B-A3B is the only local model I've found that runs reasonably on my iGPU. Looks like me and and my noisily-wheezing laptop will be sitting out this upgrade.
Since it might be helpful to some, here's my current commandline for llama.cpp running on an RTX 4090 with my monitor moved to the iGPU to free up all of its VRAM. llama-server -m Qwen3.8-27B-IQ4_NL.gguf --mmproj mmproj-BF16.gguf -c 170000 --parallel 1 -ngl -1 --cache-type-k q8_0 --cache-type-v q8_0 -b 1024 -ub 512 --flash-attn on --no-context-shift --no-mmproj-offload --spec-type draft-mtp --spec-draft-n-max 5 --spe…
Thanks for posting! Have you had any success with running without kv cache quantization? Is there a noticeable difference in quality without any? I would assume that would eat into context but 170k is pretty generous!
But maybe someone else has experience to share there
" The user wants me to explore the repository at XXXX and report back. Let me start by understanding the project structure, reading the CLAUDE.md file, and getting a general overview of what this repository is.
Let me start by reading the main project documentation and exploring the directory structure.
I'll take a look around this repo. Let me start by getting a lay of the land.
read resource CLAUDE.md (ctrl+o to expand)
ENOENT: no such file or directory, access 'XXXX/CLAUDE.md'"
If the benchmarks don't lie, this is getting very close to Opus 4.6 capability - which was the turning point for me for when AI was "good enough" that it became very hard to justify not using it. I'm sure there's some benchmaxxing going on, and some things you get only with a a larger model. But I'm feeling pretty confident if not by Gemma 5 than by mid 2028 we'll have local models that are almost always as good as O…
What kind of things you only get with a larger model?
I don't want to try to take my best educated guess at what the best design is BEFORE implementation - especially if you're designing a feature for a codebase you're not an expert in, you don't know like the back of your hand (i.e. one that is mostly or entirely LLM generated).
What sounds good on paper - often times becomes unideal in practice when you get to the reality of implementation.
It may not be worth re-architecting your entire system to get to a "pure" design that would be the best - all things considered.
Instead, I'd like the model to independently design many plausible and coherent good solutions, then implement each of them, then intelligently pick the few winners (after its fixed any bugs that could be causing promising solutions to look artificially bad) - unless there's an obvious one - and then give me the data I need to make an informed decision on which one to go with, all before I even look at the design or implementation.
You're not getting this from a one shot prompt from a 30B model today. You can't even really get it from Sol or Fable - IME. But you can get somewhat close.
Any tips on the best approach at running this at an M4 Max 128GB? Token throughput was a bit slow with the last 27B one (MLX), ended up using the A3B variant but if I could get this one to reasonable speed I'd much prefer it.
I'm benchmarking the 3.8 model now, it seems it is better (near-perfect score on my E-mail spam filtering benchmark, best of any model I tested, ever). But it is slow.
One thing I would recommend is keeping an eye on MTP parameters. I tested and benchmarked extensively, and I use `--spec-draft-n-max 2` with llama.cpp. Longer sequences actually decrease overall performance.
As for running, I ended up using llama.cpp and its llama-server, with a bunch of scripts written by AI, because I got tired of LM Studio not implementing the image-related parameters which made gemma4 useless for OCR.
Earlier quoted context omitted.
What kind of things you only get with a larger model?
Similar to the way they asked Sol to solve Erdos problems, that's what I want my model to do for programming. I don't want to try to take my best educated guess at what the best design is BEFORE implementation - especially if you're designing a feature for a codebase you're not an expert in, you don't know like the back of your hand (i.e. one that is mostly or entirely LLM generated). What sounds good on paper - ofte…