This is my upgraded rig:
- Ryzen9 5950x with 64gb DDR4
- Dual NVIDIA RTX A4000 (16+16GB VRAM)
to whom read my previous posts, i jumped the gun and upgraded my server, it was worthwhile and somewhat cheap given i already had the two GPUs and the DDR4 RAM.
Anyway, i am currently running Qwen3.6-35B-A3B-UD-Q5_K_XL all in VRAM with 65536 context and pretty happy with speed (80-90t/s) and overall responses (mostly chat).
I would like to experiment with something beefier, with CPU offload, that i can run with my llama.cpp. Of course t/s is not a goal here, but precision and accuracy of responses is.
I tried to find a good model with claude and gemini, but always got short. Once the model suggested fully crashed my server (guess fill up RAM and ended up in a swap loop), more then once i ended up chasing non existent models. Pretty annoying.
Considering i would only use between 32 and 48GB or system RAM, can you suggest (preferably with links to HF) some models?
I like qwen3.6, but open to anything.
I’m fond of this model. In “thinking” mode
gemma-4-31B-it-uncensored-heretic-BF16.gguf
Edit:
There’s also this beast I’ve been curious about. https://github.com/JustVugg/colibri using deep seek v4 flash. 280B-13b moe.
+1 for llmfan46’s heretic variants. I use one of his uncensored Qwen 3.6 35B-A3B variants as my default model.
rpDungeon’s Luchador models (gemma derived) are also quite interesting – they tend to have better prose quality for creative writing tasks.
I’ve been curious to try experimenting with using Rudo in particular for making more interesting NPC interactions in a text adventure for a while now, but haven’t gotten to it yet.
What are you wanting to do with the model?
Agentic development with that setup, you can easily run a good quant of Qwen3.6-27B at full context. unsloth/Qwen3.6-27B-MTP Q5_K_XL and use a fixed template
Creative writing? Probably need a different model. I’ve heard good things about Gemma 4 31B.
Both of those are dense models so won’t be as fast as Qwen3.6-35B-A3B. Also play around with MTP, quants, KV cache quant, KV caching, etc.
The RAM size will limit you on larger models unless you stream from storage.
More chats i guess, maybe also agents in the future, i am keep to explore that but not yet there.
It’s funny; I was just reading about someone who went the other way
https://bitworking.org/news/2026/05/surprising-things-i-learned-putting-together-a-home-brain/
At a certain point, it becomes less about parameters and more about tools supporting those parameters. Something like Pithagoras, Understory, MCP tools etc.
https://github.com/thecodacus/pithagoras/
https://github.com/thecodacus/understory
This is very interesting, i have saved your comment, it feels too soon for my understanding of it all, but both understory and pithagoras feels less obscure than what would have been, to me, only a few weeks ago.
For sure. Feel free to ask questions, too.
The TL;DR I will leave you with is this; some of what we consider as “smarts” in a LLM has traditionally done by brute force - bigger GPU , more parameters.
The alternative approach is to make the llm do less by itself, but instead, call on other tools. That way, you can squeeze out much more from a smaller llm or weaker hardware, so long as the llm is obedient at tool calling.
Think of it like doing arithmetic in your head vs using a calculator. Both provide the answer, but the latter requires much less brain power.
Qwen 3.8 27b is about to come out and you should absolutely try that when Unsloth has a Q6_K version… It will be the best local coding model at that size.
I am very fond of this specific model for 32 GB of VRAM and it will probably be the best agentic thing you can run including your ram.
https://huggingface.co/SC117/Ornith-1.0-35B-MTP-APEX-GGUF
Go into files and pick balanced if you’re doing complex agentic work with nuance or pick quality if you want to fit the model and 262k context in VRAM.
If you really want to mess with cpu offloading, try unsloth/Laguna-S-2.1-GGUF at q4_k_m. Its the perfect shape and size to work on your setup. Look into getting dflash working on it. You should be pushing 40tks by my estimate.
Keep an eye out for Ling-3.0-flash-GGUF to be officially supported. It’s similar in shape to Laguna but it’s supposed to be much smarter
someone should release something that fits in 96-128gb. Hy 3, mimo, deepseek flash are all close but 256gb (or at least more than 128gb for q4) for high context is requried. Qwen is already at 3.8, but I don’t think have updated their smaller models. I assume you’ve tried q8 version of 3.6, and prefer the larger context window?
