Qwen3-30B-A3B-speculator.eagle3

Model Overview

• Verifier: Qwen3-30B-A3B
• Speculative Decoding Algorithm: EAGLE-3
• Model Architecture: Eagle3Speculator
• Release Date: 3/12/2026
• Version: 1.0
• Model Developers: RedHat

This is a speculator model designed for use with Qwen3-30B-A3B with reasoning enabled. If interested in non-reasoning use cases, please use RedHatAI/Qwen3-30B-A3B-Instruct-2507-speculator.eagle3.

This model is based on the EAGLE-3 speculative decoding algorithm. It was trained using the speculators library on a combination of the Magpie-Align/Magpie-Pro-300K-Filtered and the HuggingFaceH4/ultrachat_200k datasets. The model was trained with thinking enabled. This model should be used with the Qwen3-30B-A3B chat template, specifically through the /chat/completions endpoint.

Use with vLLM

vllm serve Qwen3-30B-A3B \
  -tp 1 \
  --speculative-config '{
    "model": "RedHatAI/Qwen3-30B-A3B-speculator.eagle3",
    "num_speculative_tokens": 5,
    "method": "eagle3"
  }'

Evaluations

Model / run: Qwen3-30B-A3B-speculator.eagle3 vLLM: 0.15.0
Training data: Magpie + UltraChat; responses from the Qwen/Qwen3-235B-A22B model (with reasoning enabled).

Acceptance lengths (draft length)

Dataset	k=1	k=2	k=3	k=4	k=5
HumanEval	1.81	2.44	2.90	3.21	3.44
math_reasoning	1.84	2.50	3.02	3.41	3.70
qa	1.69	2.15	2.44	2.61	2.72
question	1.76	2.32	2.71	2.93	3.09
rag	1.74	2.25	2.60	2.82	2.97
summarization	1.66	2.05	2.30	2.43	2.51
translation	1.72	2.21	2.53	2.74	2.87

Details

Configuration

• Model: Qwen3-30B-A3B
• Data: Magpie + UltraChat — responses from Qwen3-30B-A3B model (reasoning)
• temperature: 0.0
• vllm: 0.15.0
• backend: vLLM chat_completions
• rate-type: throughput
• max-seconds per run: 300
• hardware: 8× GPU (tensor parallel 8)
• Benchmark data: RedHatAI/speculator_benchmarks
• vLLM serve: --no-enable-prefix-caching, --max-num-seqs 64, --enforce-eager

Command

GUIDELLM__PREFERRED_ROUTE="chat_completions" \
GUIDELLM__MAX_CONCURRENCY=128 \
guidellm benchmark \
  --target "http://localhost:8000/v1" \
  --data "RedHatAI/speculator_benchmarks" \
  --data-args '{"data_files": "HumanEval.jsonl"}' \
  --rate-type throughput \
  --max-seconds 300