Usage
hf download fraserprice/Ornith-1.0-397B-W4A16-AutoRound-DFlash --local-dir Ornith-1.0-397B
cd Ornith-1.0-397B
./serve-rtx-pro.sh
OpenAI-compatible server on port 8000: TP4, 262144 context, reasoning parser qwen3, tool parser
qwen3_xml. Runs on voipmonitor/vllm:eldritch-enlightenment-v2226f26-b12x15cd38c-cu132-20260629
(vLLM 0.11.2.dev + native support for the DFlash sliding-window drafter). First run pulls the image
and downloads the draft weights. Override via PORT, TP, MODEL_DIR, DRAFT_DIR, IMAGE.
Config notes
--attention-backend FLASHINFER: split-KV decode keeps the multi-token verify step at ~20 ms
per step regardless of context length.
num_speculative_tokens=12: ~40% faster decode than k=8 at concurrency 1, equal at 2.
k=16 (the draft's native block) is slower.
- Keep the KV cache bf16 and
--async-scheduling off; both alternatives lose speed and/or
acceptance on this hybrid-GDN architecture.
NCCL_P2P_LEVEL=SYS + VLLM_MARLIN_USE_ATOMIC_ADD=1: +13% decode, +40% prefill on 4× PCIe GPUs.
Benchmarks
4× RTX Pro 6000 Blackwell, TP4, streaming OpenAI requests, real coding prompts generated to
natural EOS (≤2048 tokens), 3 repeats. Vanilla = same image and quant, default vLLM serving per
the base model card, no speculative decoding. Accept = mean accepted tokens per verify step.
Table with columns: prompt, conc, decode tok/s, vanilla tok/s, uplift, accept, TTFT p50, ITL p50| prompt | conc | decode tok/s | vanilla tok/s | uplift | accept | TTFT p50 | ITL p50 |
|---|
| 1,000 | 1 | 197.7 | 121.2 | 1.63× | 4.20 | 152 ms | 21.0 ms |
| 10,000 | 1 | 139.5 | 119.9 | 1.16× |
Prefill throughput 6.7–8.7k tok/s.
Credits