Local AI · Recipe · 2× DGX Spark

Step-3.7-Flash 198B NVFP4 with vLLM on 2× DGX Spark

Step-3.7-Flash 198B NVFP4 with vLLM on 2× DGX Spark: 13.2 tok/s according to github.com (dataset as of Nov 3, 2026).

by r0b0tlab

13.2tok/sMixed

1 request, mixed prompt set, no speculative decoding

Source: github.com
Intelligence (original model) · Artificial Analysis20with thinking

Engine

Engine
vLLM
Quantization
NVFP4 (ModelOpt 0.45.0) + FP8 KV
Model family
Step-3.7-Flash
Context
32,768
Parameters
~198B total / ~11B active MoE
Creator
r0b0tlab
GitHub stars
4
Repo updated
Jun 11, 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)13.2 tok/scontext 2048 in → 1024 out · Custom random c1 Source 
  • Peak (decode)16.5 tok/sLlama-benchy tg128 single-stream (peak 17.00) Source 

Prefill by context

What you need

Hardware
2 × NVIDIA DGX Spark (GB10)
Weights
Engine
vLLM
Context
32,768 tokens

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
2× DGX Spark
68tok/sEveryday

1 request, generation at 32K context (speed table, pair), thinking on, xhigh, with MTP3 (per recipe) Source 

Intelligence40with thinking
  • Custom kernel required
  • License unclear

NVFP4/MXFP8 CSF QAD (Local Inference Lab)vLLM (local-inference-lab Fork) via SparkRing (SIRCL-Collectives, switchless RDMA ueber ConnectX-7), Image Release 2026.10.2Weights Repo Updated Nov 2, 2026

Details
2× DGX Spark
67.4tok/sEveryday

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

Intelligence40with thinking
  • Custom kernel required
  • License unclear

NVFP4 (nvidia, Experten) + FP8 n-gram/MTP, 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-DraftsWeights Repo Updated Oct 26, 2026

Details
2× DGX Spark
63.9tok/sEveryday

1 request, prose prompt (temperature 0.7, thinking on), with DSpark (per recipe) Source 

Intelligenceno independent value
  • ≤ 3 bit: quantization may cost quality
  • Custom kernel required

EXL3 EAQ v1 2.9 bpw (Mia-AiLab; vision tower and DSpark draft blocks included)TensorFold native (Zig 0.17.0), BertholomusAI dsv41 family (bertholomus/TensorFold deepseek-v41-zig @fbbc971) + recipe patches, TP=2 over ConnectX-7 (RoCE), DSpark draftsWeights Repo Updated Nov 2, 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.