Local AI · Recipe · 2× DGX Spark

GLM-5.3-Flash NVFP4 with vLLM on 2× DGX Spark (256k context, with MTP)

by MiaAI-Lab

No speed value is documented for this recipe, so this page is not indexed.

Intelligence (original model) · Artificial Analysis42with thinking

Engine

Engine
vLLM
Quantization
NVFP4 weights + fp8_e4m3 KV
Model family
GLM-5.3-Flash
Context
262,144
Creator
MiaAI-Lab
GitHub stars
90
Repo updated
Sep 24, 2026

Measurements

Every sourced value of this recipe, each with its condition and source. Bars relative to the largest value in the group.

Time to first token

Shorter is better.

What you need

Hardware
2 × NVIDIA DGX Spark (GB10)
Engine
vLLM
Context
262,144 tokens

Related recipes

4× DGX Spark
68tok/sEveryday

1 request, generation at 32K context (speed table; measured only as a path of four Sparks inside a ring of eight, not as a ring of four), thinking always on (measured before the profile default high, i.e. template default max), with MTP3 (per recipe) Source 

Intelligence42with 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
60.4tok/sEveryday

Prose decode, 1 request; quality (FP8 KV, 4-bit dense): GSM8K 98.8% / HumanEval 95.7% / HumanEval+ & MBPP+ 86.5% 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 v0.6.0 + Recipe v1.10 (README: 96 Patches; gepinntes Image v0.6.0-e66c657a433d mit 107 Patches: DFlash2+Copy-Drafts, 4-bit dense, FP8-KV, RoCE-One-Shot-All-Gather, Shared KV-Pool, optionaler Spill-Tier, Vision, Tool-Calling, Anthropic /v1/messages; PARALLEL=8 opt-in)Weights Repo Updated Oct 25, 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.