W4A4 NVFP4 quant of ornith-ai/Ornith-1.5-9B for vLLM on Blackwell (sm120), with a distilled MTP draft head included. Upstream ships 1.5-9B as bf16, GGUF and MLX — there is no NVFP4 build anywhere else.

Built because someone asked for one.

11.2 GB for the quantized model, down from 17.9 GB bf16.
Vision intact and verified against the bf16 source, not just "it returned something".
MTP head ships with it (model-mtp.safetensors, 15 tensors, bf16) — Ornith-1.5-9B has none upstream, so this is our own KL-distilled head, the same one in Ornith-1.5-9B-MTP-GGUF.

What is and isn't quantized

Component	Precision	Why
LM attention + MLP linears (128)	NVFP4 W4A4	the win
Vision tower (333 tensors)	bf16	no sm120 W4A4 kernel for it
DeltaNet / GDN `linear_attn`	bf16	low-precision activations corrupt DeltaNet — standing finding on this arch
`lm_head`, `embed_tokens`	bf16	quantizing `lm_head` is the known vLLM NVFP4 crash
MTP head (`mtp.*`)	bf16	drafts only; the target verifies every token

Run

bash

vllm serve protoLabsAI/Ornith-1.5-9B-NVFP4 \
  --max-model-len 32768 --gpu-memory-utilization 0.30 \
  --enable-auto-tool-choice --tool-call-parser qwen3_xml \
  --reasoning-parser qwen3 --generation-config auto --trust-remote-code

--generation-config auto is load-bearing, not boilerplate. It picks up the model's own sampling defaults. The Ornith-1.5 family fails to terminate at low temperature — pinning a low temp will run it to your token cap producing nothing useful.

On sm120 also set VLLM_USE_FLASHINFER_SAMPLER=0 and VLLM_USE_TRITON_FP8_GEMM=1.

Budget your tokens. Ornith-1.5 thinks adaptively, and a short cap returns an EMPTY completion — all of it went to reasoning. We tripped this three separate times building this release: at max_tokens=400 the model returned 0 characters and finish_reason=length; the same prompt at 4096 returned a clean 724-character answer plus a 5046-character trace. If you get blank responses, raise the budget before suspecting the weights.

Speed

Concurrency-swept (vllm bench serve, random dataset, cache-cold), RTX PRO 6000 Blackwell, co-tenant lanes live on the box — so these are honest-but-not-quiet-GPU numbers:

regime          C   ttft p50   tpot p50   agg tok/s   goodput
chat 1k/1k      1       63ms      6.7ms       148.5      0.15
chat 1k/1k      8      296ms      7.3ms      1059.0      1.03
context 8k/1k   1      305ms      6.8ms       140.2      0.14
context 8k/1k   8     1297ms      9.2ms       775.4      0.69

MTP is worth turning on: 1.27x. Same three coherent prompts, greedy, same lane, only --speculative-config changed:

arm                 decode      acceptance
MTP off             151.1 t/s        —
MTP on (K=1)        192.5 t/s      0.804

bash

vllm serve protoLabsAI/Ornith-1.5-9B-NVFP4 \
  --speculative-config '{"method":"mtp","num_speculative_tokens":1}' ...

vLLM resolves the architecture to Qwen3_5MTP and shares the target's embedding and lm_head with the drafter — no separate draft model to wire up.

Note the 0.804 acceptance is on real prompts. Benchmarks that feed random tokens report far lower acceptance for any speculative decoder, because a draft head cannot predict noise — if you measure this lane with --dataset-name random you are measuring the dataset.

Vision: measured against the source, not asserted

A quantized VL checkpoint that loads fine and serves text perfectly can have a completely dead vision path — so "we ran an image through it" is not evidence. What matters is the difference from the source. Both served identically, n=20 per side, temperature 0.7:

probe                          bf16      NVFP4    p (Fisher, 2-sided)
---------------------------    -------   -------  -------------------
shapes (red circle/blue sq)    20/20     20/20    1.00
wordmark OCR, exact             1/20      1/20    1.00
wordmark OCR, token correct     7/20     13/20    0.11

No detectable loss. Note the wordmark row: both precisions score 1/20 exact. The bf16 model itself misreads the stylised "protoLabs" as "protocolabs" and invents a trailing digit ("VLM-429"). That is a base-model weakness on a hard glyph, not quantization damage — and it is exactly why the gate scores the difference rather than an absolute threshold. An earlier n=5 read showed bf16 2/5 vs NVFP4 0/5 and looked like real damage; at n=20 it vanished.

Scorecard

Discriminating frontier battery against this build. Judge-free except claw, which uses an independent cloud judge so a local model never grades itself:

axis            score   kind                detail
--------------  -----   ------------------  ------
function_call   0.963   schema-checked      52/54 · untagged 100% · in-proc 100% · ext 90%
claw            0.675   agentic/LLM-judged  10 tasks · robustness 1.00 · safety-clean
reasoning_hard  0.611   solver-verified     5/9 full-pass
livecodebench   0.115   exec-graded         hard-only, 30 problems, thinking-off

Judge reported 0 fallbacks, so the LLM-judged score is real rather than a dead judge defaulting to 0.5.

function_call 0.963 is the best result on our internal board — across ~26 scorecards spanning 9B to 397B, including dedicated coder models. For a 9B that is the reason to run this model: schema-correct tool calls, 100% on both the untagged and in-process suites.

LiveCodeBench 0.115 is a real weakness, and it is the model, not the quantization. 12 of 30 problems earned partial credit; none passed every test. The mechanism is budget exhaustion — 13 of 30 problems consumed the entire 32,768-token budget deliberating and never emitted working code. This matches what users independently report about the Ornith-1.5 family (failed one-shot HTML tasks, regressions versus Ornith-1.0, context exhaustion), and we measured the same signature on the 35B. Thinking-off does not rescue it: on the 35B we paired thinking-on against thinking-off on identical problems and thinking-on was worse (0.129 vs 0.329) while exhausting the budget on 6 of 7.

If code generation is your workload, this family is not the right pick at any precision. If tool calling is, it is excellent.

Release gate

completion   PASS   coherent, correct, terminates
tool call    PASS   qwen3_xml, correct name + parsed arguments
vision       PASS   5/5 shapes; parity vs bf16 at n=20 (table above)
census       PASS   128 LM linears packed; visual/linear_attn/mtp/lm_head packed = 0
                    MTP sidecar present, 15 tensors; no key-prefix mangling

Provenance & license

Base: ornith-ai/Ornith-1.5-9B (MIT) — dense Qwen3.5-9B-architecture hybrid (linear + full attention) VL fine-tune, trained with end-to-end RL self-improvement.
MTP head: grafted from Qwen/Qwen3.5-9B (Apache-2.0), then KL-distilled against Ornith-1.5-9B's own hidden states.
Quantized with llm-compressor (compressed-tensors NVFP4). MIT. Built by protoLabs.studio.