Ornith-1.5-35B-A3B-NVFP4-W4A16

Ornith-1.5-35B-A3B-NVFP4-W4A16

NVFP4 W4A16 weight-only quantization of ornith-ai/Ornith-1.5-35B-A3B (revision e4dfb35a93d4b6822a811a7676f3488514abe7e2), produced with NVIDIA Model Optimizer (dev 913f5e224) using the official w4_nvfp4 recipe preset. FP4 weights (block-16, E4M3 block scales) with BF16 activations, plus an FP8-E4M3 KV cache cast. The bundled MTP head is kept in BF16 and served with SGLang EAGLE/nextn speculative decoding.

• Size: 23 GB on disk (vs 67 GB BF16 source) — 2.9× compression
• Architecture: Qwen3_5MoeForConditionalGeneration (35.1B params, hybrid GDN linear attention + softmax attention + MoE, multimodal)
• Serving: SGLang ≥ 0.5.6.post3 (5a7b26c63) on Blackwell-class NVIDIA GPUs; validated on DGX Spark GB10/SM121
• Quality: parity with the BF16 baseline on a 200-question eval suite (GSM8K-flex / HumanEval / IFEval / agentic coding) — full results below

This checkpoint is a self-quantization by r0b0tlab. It is an independent community artifact and is not affiliated with, endorsed by, or produced by Ornith AI or NVIDIA.

Quantization details

Evaluation — 200-question suite (greedy, thinking disabled)

Fixed scorer (final-numeric-answer extraction). n = 80/40/40/20 per family; ±5–11 pp noise bands apply at these sample sizes.

No measurable quality loss. Speculative decoding with the BF16 MTP head (K=1 draft): mean accept length 1.737, accept rate 0.737 over 964 decode batches. Decode throughput on DGX Spark (GB10): 63–77 tok/s single-request with CUDA graphs (vs 38.6 tok/s for an experts-only variant).

Per-tensor NVFP4 reconstruction cosines vs BF16: experts 0.9956–0.9973; dense/GDN projections 0.967–0.986 (expected for plain max calibration on outlier-rich projections; no end-to-end effect observed on this suite).

Serving with SGLang

Validated command line (SGLang 0.5.6.post3.dev9218+g5a7b26c63, FlashInfer 0.6.17, torch 2.13.0+cu130):

python -m sglang.launch_server \
  --model-path r0b0tlab/Ornith-1.5-35B-A3B-NVFP4-W4A16 \
  --served-model-name Ornith-1.5-35B-A3B \
  --trust-remote-code \
  --attention-backend triton \
  --moe-runner-backend marlin \
  --context-length 32768 \
  --kv-cache-dtype fp8_e4m3 \
  --mem-fraction-static 0.80 \
  --speculative-algorithm EAGLE \
  --speculative-num-steps 1 \
  --speculative-eagle-topk 1 \
  --speculative-num-draft-tokens 2 \
  --port 8000

Thinking-off protocol: pass chat_template_kwargs: {"enable_thinking": false} in chat completion requests.

A ready-to-run Docker image with a pre-warmed FlashInfer JIT cache and a one-command compose file is available at github.com/r0b0tlab/ornith-15-35b-nvfp4-w4a16-sm121-sglang.

Known runtime notes (all root-caused during bring-up)

• Warm the FlashInfer JIT cache before first boot. First use of the dense FP4 GEMM modules JIT-compiles ~18 CUTLASS kernels; doing this with the model already resident can OOM smaller-memory systems. The provided container ships the cache pre-warmed.
• --moe-runner-backend auto may resolve to flashinfer_trtllm, which is not implemented for NVFP4 MoE in this SGLang version — pin marlin (as above).
• FROZEN_KV_MTP is not implemented for the qwen3_5 MTP head; use --speculative-algorithm EAGLE (the draft architecture is auto-remapped to Qwen3_5ForCausalLMMTP).

How to verify

curl -s http://127.0.0.1:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "Ornith-1.5-35B-A3B",
    "messages": [{"role": "user", "content": "What is 19*23? Answer with just the number."}],
    "temperature": 0, "max_tokens": 32,
    "chat_template_kwargs": {"enable_thinking": false}
  }'
# expect "437"

Reproduction scripts (200-question suite, scorer, audits) live in the GitHub repo above; the full evidence trail (serve logs, quant logs, audit JSON) is described there.

Notes and limitations

• 200-question in-house suite; not a substitute for full public benchmarks.
• Evaluated thinking-off, greedy decoding, 32K context window.
• Dense/GDN per-tensor cosines are below the expert band (see above); out-of-distribution sensitivity beyond this suite was not probed.
• Throughput numbers are single-node DGX Spark (GB10/SM121, 121 GB unified); other Blackwell parts were not tested.

Credits and attribution

• Base model: ornith-ai/Ornith-1.5-35B-A3B by Ornith AI (MIT license).
• Quantization: NVIDIA Model Optimizer (ModelOpt) (dev @ 913f5e224) — w4_nvfp4 / kv_fp8_cast recipe units.
• Calibration data: abisee/cnn_dailymail (see the CNN/DailyMail papers) and nvidia/Nemotron-Post-Training-Dataset-v2 by NVIDIA.
• Inference engine: SGLang (0.5.6.post3.dev9218+g5a7b26c63) with FlashInfer 0.6.17 CUTLASS FP4 GEMM kernels and Marlin W4A16 MoE kernels.
• Hardware: NVIDIA DGX Spark (GB10, SM 12.1).

License

The base model is MIT-licensed; this derived quantization is distributed under MIT as well. Calibration datasets remain under their own licenses.

BibTeX

@misc{ornith15nvfp4w4a16,
  author  = {r0b0tlab},
  title   = {Ornith-1.5-35B-A3B-NVFP4-W4A16: NVFP4 weight-only quantization with BF16 MTP head},
  year    = {2026},
  url     = {https://huggingface.co/r0b0tlab/Ornith-1.5-35B-A3B-NVFP4-W4A16},
  note    = {Quantized from ornith-ai/Ornith-1.5-35B-A3B with NVIDIA ModelOpt}
}