Thread

1. hecturchi · 1791134845 · focus · HN ↗

   - Tiny context size or hours to load it

   - Hard to benefit from thinking and preserve thinking given token cost.

   - Low quants reduce accuracy heavMTP draft can make it make the same mistakes all the time when calling tools, formatting output or following basic guidelines. Otherwise 2x-4x slower.

   - K/V quants probably quantized too make things less accurate.

   Useful would be combinations with:

   - Full context size so it can code and think a bit.

   - Draft MTP <= 2 so it doesn't trip

   - Q4 quants or better so its accurate

   - q8 cache or better so it stays accurate.

   - 20 token/s so it finishes while reviewing previous step.

   - 1000 tokens/s context load so compactions don't waste 10+ minutes.

   - And enough left RAM for 50+ context checkpoints so that it can progress quuckly.

   Closest you have is Qwen3.6-35B-A3B-MTP.

   Latest gens (Qwen3.8 and co.) are just too big for low specs. 27B dense models seem to be ok for integrated >=92 GiB RAM.

   Source: I have low specs and tried them all for agentic use + coding.

   1. ohyes · 1791136979 · focus · HN ↗

      I’m using qwen3.8 quants (q3) effectively on a 5070 ti. A lot of it is about guardrails, but you also have to figure out how far (and in what ways) you can push a given model.

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