Ornith-1.5-35B-A3B-int4-AutoRound-MTP

Ornith-1.5-35B-A3B quantized to INT4 (AutoRound W4A16) with a working native MTP head — runs the full MoE on consumer 24 GB cards

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A production-ready INT4 quantization of the ornith-ai/Ornith-1.5-35B-A3B mixture-of-experts model, built and verified for vLLM — with multi-token prediction (MTP) that actually works and the full 262K context intact.

This isn't a generic weight-shrink. The 35B/≈3B-active MoE was quantized with AutoRound on a code/reasoning corpus, and — critically — the MTP fusion layer was kept in BF16 so speculative decoding loads AND contributes instead of silently doing nothing. We verified output quality at the raw token level, not just throughput.

⚡ Who this is for — speed over depth. With only ~3B parameters active per token, this is a latency/throughput-first model: it answers fast and holds long context cheaply, but it does not match a dense model of comparable total size on breadth-of-knowledge and hard reasoning. Pick it when responsiveness and concurrency matter more than maximum world knowledge — coding assistants, agentic tool-loops, high-QPS serving.

Why this quant

• 🚀 ~3.5× smaller — about 20 GB vs. ~70 GB in BF16 — the whole 35B MoE fits on 2–4× RTX 3090 (24 GB) instead of datacenter cards.
• ⚡ MTP speculative decoding works — on code workloads we measure ~85–92% draft acceptance and ~2.6 accepted tokens per step (1-layer MTP head, num_speculative_tokens 1–3). Many INT4 MoE quants load MTP but verify nothing; this one doesn't.
• 🧩 Runs the MoE across 4 GPUs via expert parallelism. Plain tensor-parallel ≥ 4 on W4A16 MoE currently mis-shards in vLLM (#41511); use --enable-expert-parallel (details below). --tensor-parallel-size 2 also works out of the box.
• 📏 Full 262,144-token context — fits comfortably (KV budget ≈ 1M tokens on 2× 24 GB with fp8 KV, ~4 full-context requests in parallel).
• 🔌 Standard compressed-tensors / auto_round format for vLLM ≥ 0.26 — no forked runtime, no patches.
• 🧠 Reasoning, tool-calling and vision preserved — thinking mode, qwen3_xml tool calls, and the vision tower are intact.

Benchmarks

Methodology: our streaming prefill+generation harness — real chat completions, a fixed ~8,200-token prompt, 512-token generations, temperature=0, thinking off, on 4× RTX 3090 (PCIe Gen4 x4, no NVLink). We report per-request decode speed, server-aggregate throughput, and time-to-first-token (TTFT) at increasing concurrency. Numbers are from our hardware; different harnesses aren't directly comparable.

TP=2 (2 GPUs) + MTP (spec 3), 8K prompt — single-stream is fastest here (less inter-GPU traffic):

On 2× RTX 3090 the useful concurrency ceiling is ~32 (aggregate ~360 tok/s); single-user decode peaks near ~195 tok/s.

TP=4 + expert-parallel + MTP (spec 3), 8K prompt — higher aggregate under heavy parallel load:

MTP quantization approach

Getting MTP to work in an INT4 MoE checkpoint takes one required exception: the fusion layer (mtp.fc) is kept in BF16. Quantize it and vLLM's MTP loader chokes (or silently skips the head). The rest of the MTP draft block (its 256 MoE experts) is quantized along with the model. lm_head, embeddings, norms, router gates and the vision tower are kept in higher precision (standard AutoRound behavior).

Quick start (vLLM ≥ 0.26)

This is a MoE — for tensor-parallel ≥ 4 you MUST add --enable-expert-parallel, otherwise the W4A16 MoE mis-shards and produces garbage (vLLM #41511).

2 GPUs (recommended — fastest single-stream, full 262K context, frees 2 cards)

vllm serve biMEMO/Ornith-1.5-35B-A3B-int4-AutoRound-MTP \
  --served-model-name Ornith-1.5-35B-A3B-int4-AutoRound-MTP \
  --tensor-parallel-size 2 --disable-custom-all-reduce \
  --max-model-len 262144 \
  --gpu-memory-utilization 0.92 \
  --kv-cache-dtype fp8_e4m3 \
  --max-num-batched-tokens 4096 \
  --enable-chunked-prefill --enable-prefix-caching \
  --enable-auto-tool-choice --tool-call-parser qwen3_xml \
  --reasoning-parser qwen3 \
  --speculative-config '{"method":"mtp","num_speculative_tokens":3}' \
  --trust-remote-code

4 GPUs (all cards, expert-parallel — higher aggregate throughput)

vllm serve biMEMO/Ornith-1.5-35B-A3B-int4-AutoRound-MTP \
  --served-model-name Ornith-1.5-35B-A3B-int4-AutoRound-MTP \
  --tensor-parallel-size 4 --enable-expert-parallel \
  --max-model-len 200704 \
  --gpu-memory-utilization 0.92 \
  --kv-cache-dtype fp8_e4m3 \
  --enable-chunked-prefill --enable-prefix-caching \
  --enable-auto-tool-choice --tool-call-parser qwen3_xml \
  --reasoning-parser qwen3 \
  --speculative-config '{"method":"mtp","num_speculative_tokens":3}' \
  --trust-remote-code

Notes:

• On GPUs without working P2P (e.g. PCIe-only pairs) add --disable-custom-all-reduce to avoid a custom all-reduce CUDA error at init.
• This model uses hybrid linear (Mamba-style) + full attention; if you hit Mamba cache align: block_size must be <= max_num_batched_tokens, raise --max-num-batched-tokens (e.g. 4096).
• To turn thinking off per request, pass chat_template_kwargs={"enable_thinking": false}.

Compatibility notes

• vLLM: verified on v0.26.0 (TP2 and TP4+expert-parallel).

License

Inherits the base model's MIT license (ornith-ai/Ornith-1.5-35B-A3B). Quantized by MinerNinja.