Local AI · Recipe · 1× DGX Spark

Ling-3.0-flash NVFP4 with vLLM on 1× DGX Spark

Ling-3.0-flash NVFP4 with vLLM on 1× DGX Spark: 21.4 tok/s according to github.com (dataset as of Nov 3, 2026).

by r0b0tlab

21.4tok/sMixed

1 request, mixed prompt set, with MTP (per recipe)

Source: github.com
Intelligence (original model) · Artificial Analysis25with thinking

Engine

Engine
vLLM vllm-ling-v3 @ 92c104112
Quantization
NVFP4 (ModelOpt, nur Experten) + FP8 KV
Model family
Ling-3.0-flash
Context
24,576
Creator
r0b0tlab
GitHub stars
1
Repo updated
Aug 8, 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)21.4 tok/sBase AR, CUDA graphs + VLLM_COMPILE, C1 Source 
  • Peak (decode)33.5 tok/sMTP scale 1 + CUDA graphs, C1; 73.2 % acceptance; 32.8 in der vollen Suite; Prompt nicht angegeben, thinking off Source 

What you need

Hardware
1 × NVIDIA DGX Spark (GB10)
Engine
vLLM vllm-ling-v3 @ 92c104112
Context
24,576 tokens

Related recipes

1× DGX Spark
71.8tok/sEveryday

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

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

24GB pack: IQ2_XXS experts (2.0625 bits), NVFP4 Mamba projections, Q8_0 attention/latent projections/lm_head; bf16 KVTensorFold tensorfold-native v1.1.6 (eea9dc9c) + recipe patches/ (Bolt lane backend, libbolt.so CUDA forward pass), LiLiCorr draftsWeights Repo Updated Nov 1, 2026

Details
1× DGX Spark
59.4tok/sEveryday

1 request, chat prompt (20 prompts, thinking off), no drafting (default profile) Source 

Intelligenceno independent value
  • Custom kernel required

EXL3 6.08 bpw (28,3 GB), FP16 KV; nur Textpfad (Vision-Tower wird von ExLlamaV3 nicht ausgeführt)ExLlamaV3 1.5.0 (Fork r0b0tlab/exllamav3-r0b0t, Branch minicpm-v4.7-dspark @ 5ddae7d12448), torch 2.14.0+cu130Weights Repo Updated Oct 14, 2026

Details
1× DGX Spark
55.6tok/sEveryday

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

Intelligence40with thinking
  • Custom kernel required
  • License unclear

NVIDIA FP4-QAD-Export (FP4-Local @b9ce18a6): Routed Experts NVFP4 (Gruppe 16), Attention/Shared Expert MXFP8, alle 512 Experten mit 10 pro Token; n-gram-Tabelle per pread von der SSD; FP8 KVTensorFold tensorfold-native (Zig) v1.1.4 (eab85828) + Patches 0001–0017, Image tensorfold-qwen38:v1.1.4-eab8582 (GHCR v1.1.4-eab8582-4baf16137ecd) auf nvcr.io/nvidia/pytorch:26.07-py3, MTP-DraftsWeights Repo Updated Oct 30, 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.