Local AI · Recipe · 2× DGX Spark

Inkling-Small 276B-A12B NVFP4 with SGLang on 2× DGX Spark

Inkling-Small 276B-A12B NVFP4 with SGLang on 2× DGX Spark: 33 tok/s according to github.com (dataset as of Nov 3, 2026).

by drowzeys

33tok/sPeak

1 request, best value reported by source, context 1m

Source: github.com
Intelligence (original model) · Artificial Analysis28with thinking

Engine

Engine
SGLang
Quantization
NVFP4 weights + NVFP4 KV (eigener Port)
Model family
Inkling-Small
Context
1,048,576
Parameters
276B MoE / 12B active
Creator
drowzeys
GitHub stars
10
Repo updated
Aug 17, 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

  • Peak (decode)33 tok/scontext 1M · ~33 tok/s, accept ~3.5/8 Source 

What you need

Hardware
2 × NVIDIA DGX Spark (GB10)
Engine
SGLang
Context
1,048,576 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

← 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.