INT8 weight and activation (W8A8) quantization of Qwen/Qwen3.8-27B — a dense hybrid model (GatedDeltaNet linear-attention + periodic full-attention) with a native vision tower and an MTP head.

~30 GB, so it needs two 24 GB cards (tensor-parallel 2) but leaves a comfortable KV cache. Pick this over the INT4-W4A16 build when you want the fastest prefill and have the VRAM; pick INT4 when you want the fastest decode or a single-card fit.

Quantization

Symmetric INT8 weights, per-output-channel, MSE observer + INT8 dynamic per-token activations (llm-compressor), calibrated on 256 × 2048-token Magicoder samples.
Quantized: all MLP, the full-attention self_attn, and the GatedDeltaNet out_proj.
Kept bf16 (quality-sensitive): GatedDeltaNet in_proj_a/in_proj_b, lm_head, embeddings, vision tower, MTP head.
Format: compressed-tensors (int-quantized). Full recipe: recipe.yaml.

The MTP head is included (mtp.safetensors, bf16) and is listed in quantization_config.ignore, so vLLM loads it unquantized and speculative decoding works out of the box. Quantizers silently drop this head — if you re-quantize the base yourself, graft it back or spec-decode will accept 0 %.

Evaluation

GSM8K: 96.8 % (242/250). Served with vLLM tp2 and scored over the OpenAI API — thinking enabled, the base model's recommended sampling (temperature=1.0, top_p=0.95, top_k=20), default reasoning_effort, max_tokens=24576, and the question suffixed with "Put your final numeric answer inside \boxed{}". The INT4-W4A16 build of the same model scores 96.4 % (241/250) under the identical protocol — the two are indistinguishable at this sample size, i.e. INT8 buys no measurable accuracy over INT4 here.

Also verified over the API on 2 × RTX 3090 (tp2): Chinese/English generation and translation coherent, vision path working (image input through the native VL tower), reasoning_effort and enable_thinking passthrough, and MTP speculative decoding at mean accept length ≈ 2.5 / 3.0 with K=2. MMLU-Pro has not been run.

Throughput

2 × RTX 3090, tensor-parallel 2, single stream, 16k context, prefix caching disabled, MTP K=2:

Build	decode	prefill
INT8-W8A8 (this)	73 tok/s	4408 tok/s
INT4-W4A16	90 tok/s	2276 tok/s

INT8 weights are twice the bytes of INT4, so decode — which is weight-bandwidth-bound — is slower; the INT8 activation path makes prefill much faster.

Usage (vLLM)

bash

vllm serve Avesed/Qwen3.8-27B-INT8-W8A8 \
  --tensor-parallel-size 2 --trust-remote-code --reasoning-parser qwen3

Add --speculative-config '{"method":"mtp","num_speculative_tokens":2}' to use the MTP head.

Sampling follows the base model: thinking temperature=1.0, top_p=0.95, top_k=20; non-thinking temperature=0.7, top_p=0.80, top_k=20, presence_penalty=1.5.

Quantized with vllm-ampere-optimized/quantize.