Likely soon we'll see nvme offloading for ngrams as well. They're just an index, so that should be plenty fast for what it does. LLama.cpp support should come soon as well, and they might do some things with offloading first.
nVME paging the n-gram table (in BF16 for now).
Still working at it. Prefill sucks still but decode is about 12 tok/sec and the model weights fit nicely in the 128GB Spark memory in nvfp4 quant while paging the ngram stuff from disk.
(EDIT: merged to main. 80tok/sec prefill, 12 tok/sec decode, ~80GiB resident, the rest paged)
https://huggingface.co/unsloth/Qwen3.8-Flash-Next-GGUF https://unsloth.ai/docs/models/qwen3.8-next
> You will need at least 75 GB of RAM or unified memory to run the model. Its smallest 1-bit quantized version is larger than usual because of the model’s architecture so 1-bit isn't really 1-bit at all. However, this also means the quantization is less aggressive, allowing the model to retain more of its original accuracy than more heavily quantized models.
Lots of RAM required even for the 1-bit, which is already downloadable. Interested to see how well this one works compared to Ornith1.5-35B-A3B I've been running (and quite happy about).
Edit: but llama-cpp does not yet support it.
On branch of https://github.com/rdaum/eider (for DGX Spark). ~12 tok/sec decode without speculative decoding (will come later)
Still actively working on this. Prefill currently sucks. Will merge to main by end of day.
EDIT: This has now landed on main. Still haven't done MTP speculative decoding boost, but:
80tok/sec prefill, 12 tok/sec decode. ~90GiB or so resident. n-grams paged from disk.