Thread · HN Continuity

- 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.