Local AI · Recipe · 1× DGX Spark

Nemotron-3.5-Bolt 74B-A9B 24GB pack with TensorFold on 1× DGX Spark

Nemotron-3.5-Bolt 74B-A9B 24GB pack with TensorFold on 1× DGX Spark: 71.8 tok/s according to github.com (dataset as of Nov 3, 2026).

by MiaAI-Lab

71.8tok/sEveryday

1 request, prose prompt (sampled, thinking off), with LiLiCorr (per recipe)

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

≤ 3 bit: quantization may cost quality

Install: Nemotron-3.5-Bolt 74B-A9B 24GB pack with TensorFold on 1× DGX Spark

Command
git clone https://github.com/MiaAI-Lab/Nemotron-3.5-Bolt-TensorFold-DGX-Spark.git && cd Nemotron-3.5-Bolt-TensorFold-DGX-Spark && ./start.sh

Full instructions in the README
Repo

Engine

Engine
TensorFold tensorfold-native v1.1.6 (eea9dc9c) + recipe patches/ (Bolt lane backend, libbolt.so CUDA forward pass), LiLiCorr drafts
Quantization
24GB pack: IQ2_XXS experts (2.0625 bits), NVFP4 Mamba projections, Q8_0 attention/latent projections/lm_head; bf16 KV
Model family
Nemotron-3.5-Bolt-74B-A9B
Context
262,144
Parameters
74B MoE / 9B aktiv
Creator
MiaAI-Lab
GitHub stars
11
Repo updated
Nov 1, 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)71.8 tok/sProsa, gesampelt (Temperatur 1,0, top_p 0,95, seeded), Thinking aus, 1 Client, 256 Tokens, Median aus 3 Läufen, TensorFold bench_concurrent.py, 2026-10-27, LiLiCorr-Drafts; TTFT 50–60 ms. Greedy: 73,8 Source 
  • Peak (decode)137 tok/sCode, greedy, 1 Client (gesampelt 136,1); Aggregat bei 16 Clients greedy: 506,8 Source 

Prefill by context

  • 21K2,485 tok/sFrischer Random-Word-Prompt, max_tokens 1 (Prompt-Cache greift nicht), NV4_FP4=1 (Default); Tabelle: 2.485 bis 2.486 tok/s, TTFT 8,4 s; 126k: 1.969 tok/s Source 

What you need

Hardware
1 × NVIDIA DGX Spark (GB10)
Engine
TensorFold tensorfold-native v1.1.6 (eea9dc9c) + recipe patches/ (Bolt lane backend, libbolt.so CUDA forward pass), LiLiCorr drafts
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
  • License unclear

Sources

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