Local AI · Recipe · 1× DGX Spark

Ornith-1.5-122B EXL3 4 bpw with TensorFold on 1× DGX Spark

Ornith-1.5-122B EXL3 4 bpw with TensorFold on 1× DGX Spark: 52.6 tok/s according to github.com (dataset as of Nov 3, 2026).

by MiaAI-Lab

52.6tok/sEveryday

1 request, prose prompt, with DFlash2 (per recipe)

Source: github.com
Intelligence (original model) · Artificial Analysisno independent value

Engine

Engine
TensorFold tensorfold-native (Zig 0.17.0, pinned d1018b31 + recipe patches/ for EXL3 in the Qwen3.5-MoE engine), image tensorfold-ornith:zig-d1018b3, DFlash2 drafts
Quantization
EXL3 EAQ v1 4 bpw (4.049 bpw trellis average per quant card), FP8 KV
Model family
Ornith-1.5-122B
Context
262,144
Parameters
122B MoE (Qwen3.5-MoE-Architektur); HF-API safetensors total der Basis: 125.086.497.008
Creator
MiaAI-Lab
GitHub stars
21
Repo updated
Oct 21, 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)52.6 tok/sProsa, 1 Stream, Thinking aus, DFlash2-Drafts (DRAFTS=1), FP8 KV, sparkDash; TTFT 164 ms. Ohne Drafts laut README etwa 35 Source 
  • Peak (decode)83.3 tok/sCode, 1 Stream (TTFT 224 ms); Aggregat bei 4 Streams: 111,0 Source 

Prefill by context

  • 32K1,955 tok/s32k-Prompt (32.807 Tokens), TTFT 16,99 s; 4k 2.450 / 128k 1.230 tok/s Source 

Time to first token

Shorter is better.

What you need

Hardware
1 × NVIDIA DGX Spark (GB10)
Engine
TensorFold tensorfold-native (Zig 0.17.0, pinned d1018b31 + recipe patches/ for EXL3 in the Qwen3.5-MoE engine), image tensorfold-ornith:zig-d1018b3, DFlash2 drafts
Context
262,144 tokens

Notes

What matters before you rebuild it.

  • Custom kernel required
  • Experimental
  • vision
  • tool-calling

Sources

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Details

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