Comment by trilogic
4 hours ago
Really, how so? Because we work with codebases daily, can you tell us a concrete example! In our case we work in consumer hardware (ish), 10 million ctx (1 million output, 1 million input proven, sometimes it loops or breaks at over 500k ctx byt at ~17tps linear). IT can read the full codebase, unleash agents, and write in disk editing and patching files creating a full app in 3-4 minutes. IT can do Web search and Rag pretty fast, it understands and fix the user query, sys prompts and adapt/fix them if needed on the fly. I am wondering what more do you do?
Edit: Forgot to mention that it can process images and pdf, and 100s of other files, it can even create presentations in code or mermaid, svg, charts js etc. Here a basic version of it: https://hugston.com/chat
how do you do 1mio context with qwen3.6 27b, that only supports 256k? and what hardware would you run that on? 2 * 3090 is afaik currently at max 256k context.
You can get all the Qwen 3.x models up to ~1 million tokens using YaRN with llama.cpp.[0]
Personally I am using `--no-context-shift` and feeding in context back in on failure at the harness level.
I have 2x1080ti + 1xTitanV that have a full 262,144 tokens context on 262,144 tokens with `-sm tensor` at 62.04 t/s which isn't so bad.
But I also have a 1x3090 running unsloth/Qwen3.6-27B-MTP-GGUF:UD-Q4_K_XL at 41.89 t/s but with only 130k context, but if you have a modular programming style both work pretty well.
But play with YaRN if you really need it.
[0]https://qwen.readthedocs.io/en/v3.0/run_locally/llama.cpp.ht...
3 replies →
You can increase the context window beyond its max trained context using RoPE scaling[0] which will require more VRAM.
But you can increase your context window for the same VRAM by quantizing the KV cache with FP8 (double the context) or TurboQuant (more than double)[1].
0: https://medium.com/@leannetan/extending-context-length-with-...
1: https://docs.vllm.ai/en/latest/features/quantization/quantiz...
We managed to increase the ctx for whatever llm model that is GGUFED, here the experimental tests: https://www.reddit.com/r/Hugston/