Local AI · Recipe · 1× DGX Spark

Qwen3.8-Flash-Next NVFP4 (NVIDIA QAD, FP4-Local) with TensorFold on 1× DGX Spark

Qwen3.8-Flash-Next NVFP4 (NVIDIA QAD, FP4-Local) with TensorFold on 1× DGX Spark: 55.6 tok/s according to github.com (dataset as of Nov 3, 2026).

by MiaAI-Lab

55.6tok/sEveryday

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

Source: github.com

Intelligence (original model) · Artificial Analysis40with thinking

Install: Qwen3.8-Flash-Next NVFP4 (NVIDIA QAD, FP4-Local) with TensorFold on 1× DGX Spark

Command

git clone https://github.com/MiaAI-Lab/Qwen3.8-Flash-Next-Single-DGX-Spark-TensorFold.git && cd Qwen3.8-Flash-Next-Single-DGX-Spark-TensorFold && ./start.sh

Full instructions in the README Repo

Engine: TensorFold tensorfold-native (Zig) v1.1.4 (eab85828) + Patches 0001–0017, Image tensorfold-qwen38:v1.1.4-eab8582 (GHCR v1.1.4-eab8582-4baf16137ecd) auf nvcr.io/nvidia/pytorch:26.07-py3, MTP-Drafts
Quantization: NVIDIA FP4-QAD-Export (FP4-Local @b9ce18a6): Routed Experts NVFP4 (Gruppe 16), Attention/Shared Expert MXFP8, alle 512 Experten mit 10 pro Token; n-gram-Tabelle per pread von der SSD; FP8 KV
Model family: Qwen3.8-Flash-Next
Context: 262,144
Parameters: 125B MoE / 6B aktiv, plus 51B n-gram-Embedding und 4B MTP (lt. HF-Card); 10 von 512 Experten pro Token
Creator: MiaAI-Lab
GitHub stars: 364
Repo updated: Oct 30, 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

Everyday (decode)55.6 tok/scontext 256K · Prosa (Schritt-für-Schritt-Erklärung einer Hash-Map), sparkDash, Temperatur 0,7, Thinking an, PARALLEL 5, FP8 KV, Vision an; TTFT 136 ms; frühere Einzelläufe 46–55 tok/s Source

Decode · parallel requests

Sums over all streams are marked; per request it is correspondingly less.

5 parallel requests156 tok/sPeak (decode) · context 256K · Prosa ×5 aggregiert (35,0 pro Request); ×2 95,4 / ×4 145,1; Code 1 Anfrage 110,5, Code ×5 217,6 Source

Prefill by context

16k (16.421 Tokens)2,839 tok/sKalt, ohne Prompt-Cache-Treffer, TTFT 5,95 s; 4k 3.050 / 8k 2.956 / 32k 2.735 / 64k 2.230 / 128k 2.269 tok/s Source

Time to first token

Shorter is better.

Prompt not stated136 msProsa, 1 Anfrage Source

What you need

Hardware: 1 × NVIDIA DGX Spark (GB10)
Weights: Main weightsnvidia/Qwen3.8-Flash-Next-FP4-Local other
BaseQwen/Qwen3.8-Flash-Next other
Engine: TensorFold tensorfold-native (Zig) v1.1.4 (eab85828) + Patches 0001–0017, Image tensorfold-qwen38:v1.1.4-eab8582 (GHCR v1.1.4-eab8582-4baf16137ecd) auf nvcr.io/nvidia/pytorch:26.07-py3, MTP-Drafts
Context: 262,144 tokens

Notes

What matters before you rebuild it.

Custom kernel required
vision
tool-calling
License unclear

Sources

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Engine

Measurements

Decode · 1 request

Decode · parallel requests

Prefill by context

Time to first token

What you need

Notes

Sources

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Qwen3.8-Flash-Next INT4-AutoRound (v5.2) with vLLM on 2× DGX Spark

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