Local AI · Recipe · 1× Mac

Qwen3.8-Flash-Next 176.9B Q4_K with DwarfStar on 1× Mac

Qwen3.8-Flash-Next 176.9B Q4_K with DwarfStar on 1× Mac: 48.4 tok/s according to github.com (dataset as of Nov 3, 2026).

by antirez

48.4tok/sPeak

1 request, best value reported by source, context 32k

Source: github.com
Intelligence (original model) · Artificial Analysis40with thinking

Engine

Engine
DwarfStar (ds4) main@0aaea5a (2026-09-20)
Quantization
Q4_K; 69.74 GiB resident, 95.37 GiB BF16-n-grams auf SSD
Model family
Qwen3.8-Flash-Next
Context
—
Parameters
176.9B, davon 51.2B n-gram-Tabelle (Angabe 0xBakeer-README)
Creator
antirez
GitHub stars
24,199
Repo updated
Nov 2, 2026

Measurements

Every sourced value of this recipe, each with its condition and source. Bars relative to the largest value in the group.

Decode · 1 request

  • Peak (decode)48.4 tok/scontext 32K · Ohne MTP Source 

Prefill by context

What you need

Hardware
1 × Mac / Apple Silicon
Engine
DwarfStar (ds4) main@0aaea5a (2026-09-20)

Related recipes

2× DGX Spark
89.3tok/sEveryday

1 request, prose prompt, with MTP (per recipe) Source 

Intelligenceno independent value
  • Custom kernel required
  • License unclear

INT4-AutoRound (GPTQ int4, Top-5-Experten) + FP8 n-gram-Tabelle, FP8 KVTensorFold tensorfold-native (Zig, zig-flashnext db281878 + patches), Image ghcr.io/miaai-lab/qwen3.8-flash-dual-dgx-sparks-tensorfold:zig-db28187-f3e1300f4f62, TP=2 über CX7/RoCE, MTP + Copy-Drafts (QUANT=int4ar)Weights Repo Updated Oct 26, 2026

Details
2× DGX Spark
74tok/sEveryday

1 request, prose prompt (long story), thinking off, with MTP Source 

Intelligence40with thinking
  • Custom kernel required
  • License unclear

INT4-AutoRound (azampatti): int4-Experten, fp8-Seitenschichten; n-gram-Tabelle beim Laden fp8 → NVFP4, halb pro Box auf der GPU; KV-Pin 29G/Box (1.829.182 Tokens)vLLM 0.30 (Image myllmbox/qwen38-flash-next-cluster-vllm:v5.2; RecoverSSM, dynamischer Draft-Depth 3–7 in Full-CUDA-Graphs, sampled Drafts + Block-Verifikation, One-Shot-RoCE-All-Reduce, Dual-Rail, TP=2, 64 Seats) — Kit v5.2Weights Repo Updated Oct 29, 2026

Details

← Back to overview

Local AI in your company?

In a workshop we work out which models and which hardware fit your tasks, and build the first agent on your infrastructure.