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Local AI · Recipe · 2× DGX Spark

GLM-5.3-Flash NVFP4 with vLLM on 2× DGX Spark (128k context, with MTP)

GLM-5.3-Flash NVFP4 with vLLM on 2× DGX Spark (128k context, with MTP): 23 tok/s according to github.com (dataset as of Sep 29, 2026).

by Weschera

23tok/sEveryday

1 request, prose prompt, with MTP4

Source: github.com
Intelligence (original model) · Artificial Analysisno independent value
Instructions in the README Repo

Engine

Engine
vLLM
Quantization
NVFP4 (vcruz305-Quant, BF16-MTP-Head, 49 Shards; quantization_config.ignore für Layer 45), KV fp8_e4m3
Model family
GLM-5.3-Flash
Context
131,072
Creator
Weschera
GitHub stars
0
Repo updated
Aug 28, 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)23 tok/sBereich 22–24 tok/s, MTP4, thinking off, short/medium ctx → approx (Bereichsangabe) Source 
  • Peak (decode)36.5 tok/sStructured output/code, MTP4 Source 

What you need

Hardware
2 × NVIDIA DGX Spark (GB10)
Weights
Engine
vLLM
Context
131,072 tokens

Notes

What matters before you rebuild it.

  • Experimental
  • Custom kernel required

Sources

Related recipes

2× DGX Spark

GLM-5.3-Flash EXL3 with TensorFold on 2× DGX Spark (1M context, 4 streams)

by MiaAI-Lab

97.6tok/sEveryday

HumanEval 97.6% / GSM8K 98.0% with FP8 KV and 4-bit dense Source 

Intelligence42with thinking
  • ≤ 3 bit: quantization may cost quality
  • Custom kernel required
  • Non-commercial
  • License unclear

EXL3/TR3 4bpw routed experts + 4-bit dense, BF16 elsewhere, FP8 KVTensorFold v0.5.0 (52 patches: DFlash2+copy drafts, 4-bit dense, FP8 KV, RoCE one-shot all-gather, shared KV pool, vision, tool calling)Weights Repo Updated Sep 30, 2026

Details

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