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DeepSeek-V4.1-Flash-1017 EXL3 2.9 bpw (LibertAI) with TensorFold on 2× DGX Spark

DeepSeek-V4.1-Flash-1017 EXL3 2.9 bpw (LibertAI) with TensorFold on 2× DGX Spark: 59.2 tok/s according to github.com (dataset as of Nov 3, 2026).

by bertholomus

59.2tok/sEveryday

1 request, prose prompt (temperature 0, thinking off, single run), with DSpark (per recipe)

Source: github.com
Intelligence (original model) · Artificial Analysisno independent value

≤ 3 bit: quantization may cost quality

Instructions in the README Repo

Engine

Engine
TensorFold (Elevatormusic/TensorFold @07aa02585f64: TensorFold 0.6.6 line + BertholomusAI DeepSeek-V4.1 family), Launcher Elevatormusic/DeepSeek-V4.1-Flash-1017-EXL3-TensorFold-2x-DGX-Spark, TP=2 über ConnectX-7, DSpark (MTP_DRAFTS=5), MEM_FLOOR_GIB=0
Quantization
EXL3 2.9 bpw (LibertAI, Revision afc7aa0a)
Model family
DeepSeek-V4.1-Flash-1017
Context
262,144
Parameters
~763B (HF-API safetensors total der Basis deepseek-ai/DeepSeek-V4.1-Flash-1017: 763.205.315.794)
Creator
bertholomus
GitHub stars
0
Repo updated
Oct 19, 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)59.2 tok/sProsa, erster Prompt-Satz (Client des gepinnten Skript-Repos), 1 Stream, Temperatur 0, Thinking aus, Einzellauf, leerer Server, warme Engine, DSpark-Tiefe 5 (MTP_DRAFTS=5); zweiter Prompt-Satz: 60,0; Streuung laut README ~10 % Source 
  • Peak (decode)144 tok/sStructured, erster Prompt-Satz, 1 Stream, Einzellauf; Code 97,4; Aggregat 4 Streams über 90 s: 120,9 Source 

Prefill by context

  • 128K1,979 tok/sKalter 128K-Prompt, Einzellauf; 8K / 32K / 64K: 2.079 / 1.779 / 2.086 tok/s Source 

What you need

Hardware
2 × NVIDIA DGX Spark (GB10)
Engine
TensorFold (Elevatormusic/TensorFold @07aa02585f64: TensorFold 0.6.6 line + BertholomusAI DeepSeek-V4.1 family), Launcher Elevatormusic/DeepSeek-V4.1-Flash-1017-EXL3-TensorFold-2x-DGX-Spark, TP=2 über ConnectX-7, DSpark (MTP_DRAFTS=5), MEM_FLOOR_GIB=0
Context
262,144 tokens

Notes

What matters before you rebuild it.

  • ≤ 3 bit: quantization may cost qualityAt 3 bit and below the model may answer noticeably worse than the original. The intelligence number refers to the original.
  • Custom kernel required
  • vision
  • tool-calling

Sources

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