Local AI · Recipe · 1× DGX Spark

Qwen3.8-27B NVFP4 with TandemLLM on 1× DGX Spark

Qwen3.8-27B NVFP4 with TandemLLM on 1× DGX Spark: 49.9 tok/s according to github.com (dataset as of Sep 29, 2026).

by 0xBakeer

49.9tok/sMixed

1 request, benchmark prompt set, with DFlash2 drafter + StairCut (per recipe)

Source: github.com

Intelligence (original model) · Artificial Analysis34with thinking no thinking 20

Repo

Engine: TandemLLM v0.3.0 (eigene PyTorch/Triton/CUDA-Engine, StairCut)
Quantization: NVFP4 (GPTQ) alle Projektionen + FP8-Head (über Qwen FP8-Checkpoint)
Model family: Qwen3.8-27B
Context: 262,144
Parameters: 27B dense
Creator: 0xBakeer
GitHub stars: 30
Repo updated: Oct 4, 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

Mixed (decode)49.9 tok/scontext 256 in / 256 out · Serve-single-i256-o256-v1, Mittel über 50 Requests, thinking off, temp 0, Lookup-Store aus; Paper-Messung (v0.2.0-staircut); plain greedy 13.69 Source
Peak (decode)90.5 tok/scontext 32K · Teacher-forced Bench, 25 Workloads (fresh text, edits, quotations, copies), StairCut-Mittel Source

What you need

Hardware: 1 × NVIDIA DGX Spark (GB10)
Weights: Main weights0xBakeer/TandemLLM-Qwen3.8-27B-NVFP4 Apache-2.0
BaseQwen/Qwen3.8-27B-FP8 Apache-2.0
Draft model0xBakeer/TandemLLM-Qwen3.8-27B-DFlash2-b16 Apache-2.0
Draft model0xBakeer/TandemLLM-Qwen3.8-27B-DFlash2-b8 Apache-2.0
Engine: TandemLLM v0.3.0 (eigene PyTorch/Triton/CUDA-Engine, StairCut)
Context: 262,144 tokens

Notes

What matters before you rebuild it.

Custom kernel required
tool-calling
vision

Sources

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