[mixermachine]

Nothing will come close to Opus 4.6 here. You will be able to fit a destilled 20B to 30B model on your GPU. Gpt-oss-20B is quite good in my testing locally on a Macbook Pro M2 Pro 32GB.

The bigger downside, when you compare it to Opus or any other hosted model, is the limited context. You might be able to achieve around 30k. Hosted models often have 128k or more. Opus 4.6 has 200k as its standard and 1M in api beta mode.

[zozbot234]

There are local models with larger context, but the memory requirements explode pretty quickly so you need to lower parameter count or resort to heavy quantization. Some local inference platforms allow you to place the KV cache in system memory (while still otherwise using GPU). Then you can just use swap to allow for even very long contexts, but this slows inference down quite a bit. (The write load on KV cache is just appending a KV vector per inferred token, so it's quite compatible with swap. You won't be wearing out the underlying storage all that much.)