Calibrated NVFP4 quantization of deepreinforce-ai/Ornith-1.0-9B for vLLM — with the MTP draft head included as a bf16 sidecar for lossless speculative decoding out of the box.

10.4 GB (from 19 GB bf16) · ~1.5× faster than bf16+MTP under identical load · full release-gate parity vs bf16 · coherence-verified to 60K context. Quantized and gate-verified by protoLabs on RTX PRO 6000 Blackwell (sm120), vLLM 0.22.1.

Serve

bash

vllm serve protoLabsAI/Ornith-1.0-9B-NVFP4 \
  --reasoning-parser qwen3 --tool-call-parser qwen3_xml --enable-auto-tool-choice \
  --speculative-config '{"method":"mtp","num_speculative_tokens":1}'

sm120 (workstation Blackwell) notes: VLLM_USE_FLASHINFER_SAMPLER=0 required; if the FlashInfer NVFP4 JIT fights your CUDA install, VLLM_NVFP4_GEMM_BACKEND=cutlass (or marlin) — quality verified identical on the cutlass path. Drop --speculative-config to serve without MTP.

Quality — release gate vs bf16 (same suites, same judge, thinking-on, 8192-token budget)

axis                              bf16     NVFP4    delta
--------------------------------  -------  -------  ------
function-call (54, deterministic)  93%      96%     +3
reasoning-v2 (24, solver-graded)   0.726    0.684   -0.042
code-exec-v2 (8, exec-graded, x3)  0.391    0.405   +0.014
claw agentic (paired-29, judged)   0.819    0.791   -0.028

Claw outliers re-trialed ×3 on both sides before the verdict; bf16 numbers are our own published baselines, same harness.

Known regression (the honest caveat): T12_expense_report — the quant reproducibly (0/3) misses a duplicate-transaction-detection requirement that bf16 partially satisfies (0.70 ×3). One agentic judgment, categorical under quant. Everything else is parity or better.

Long-context coherence: adversarially probed at 4K/16K/32K/60K — needle recall perfect at every depth, zero degeneration flags (char-runs, n-gram loops, compression ratio, template leakage), hostile-judge clean. We don't publish tok/s at depths where a model babbles.

Speed — RTX PRO 6000 Blackwell, vLLM 0.22.1, client-side seeded benchmark

Same client, same seeds, both models with MTP (single-stream-only numbers tell you nothing about load — full methodology in the protoLabs benchmark notes):

regime (ISL/OSL)   C    bf16+MTP   NVFP4+MTP   speedup
----------------  ---   --------   ---------   -------
chat 1k/1k          1     82.7       132.2      +60%
chat 1k/1k          8    620.8       907.1      +46%
context 8k/1k       1     73.1       108.1      +48%
context 8k/1k       8    385.9       598.0      +55%

Decode-at-depth (C=1): 105 tok/s @4K → 75 @16K → 56 @32K → 37 @64K — a 2.8× fade where dense transformers typically fade ~10× by 50K (the hybrid DeltaNet trunk keeps constant-size state on 24 of 32 layers). At 64K the 4.1 s TTFT is prefill physics — long context here is a decode story, not a first-token one.

MTP acceptance on the NVFP4 target: 0.76 on real text (vs 0.762 on bf16 — the quant costs the draft head nothing). Benchmark-table numbers above use random-data prompts where acceptance drops to ~0.31 for both sides equally; real-text throughput runs higher.

Recipe (provenance — reproduce in ~30 min)

llm-compressor 0.10.1, NVFP4 preset (E2M1 weights, 16-elem blocks, E4M3 scales + FP32 tensor scale; W4A4, dynamic-local activations), 512 × ultrachat calibration @2048.
Kept bf16: all linear_attn.* (DeltaNet — low-precision activations corrupt the hybrid SSM), vision tower, lm_head, embeddings. 128 attention/MLP linears quantized.
MTP sidecar: model-mtp.safetensors (15 tensors, bf16) — verified against the base model: spec decode verifies every drafted token, output distribution unchanged.
Pipeline + verification scripts: protoLabsAI/protoLab → experiments/quantize/.

Need a different quant?

Open a Community discussion — size/format requests usually ship within 48h. GGUF (llama.cpp) builds of this exact quant: Ornith-1.0-9B-MTP-GGUF (6.6 GB NVFP4+MTP mixed — smaller than Q8_0, tensor-core accelerated on Blackwell). All benchmark rows: protoLabsAI/lab-benchmarks · charts at protolabs.studio/lab.