RedHatAI/Qwen3.8-27B-speculator.dspark

Model Overview

• Model Architecture: DSparkDraftModel
  • Target Model Architecture: Qwen3_5ForConditionalGeneration
  • Input: Text
  • Output: Text
• Model Optimizations:
  • Speculative Decoding Algorithm: DSpark
  • Draft Model: 5-layer Qwen3-style backbone
  • Maximum Draft Length: 8 tokens
  • Auxiliary Target Layers: 4, 12, 20, 28, 36, 44, 52, and 60
  • Markov Head: Vanilla, rank 256
  • Confidence Head: Enabled with Markov features
• Configured Maximum Context Length: 262,144 tokens
• Release Date: 2026-09-21
• Version: 1.0
• Model Developers: Red Hat AI

This is a DSpark speculator model for Qwen3.8-27B. It was trained using the Speculators library (version 0.8.0.dev221).

DSpark extends DFlash with a Markov head for modeling intra-block token dependencies and a confidence head for predicting per-position acceptance. The 5-layer draft model consumes auxiliary hidden states from layers 4, 12, 20, 28, 36, 44, 52, and 60 of the target model and proposes up to 8 tokens per decoding step.

Training Details

This checkpoint was warm-started from an earlier Qwen3.8-27B DSpark checkpoint and trained on a Qwen3.8-27B-regenerated version of Open PerfectBlend. Data preparation produced 1,739,710 8,192-token training examples with a 99/1 train-validation split. Hidden-state extraction used four independent vLLM data-parallel replicas, while four FSDP ranks trained the draft model.

The released checkpoint is from global step 107,520, or approximately 75% of the planned one-epoch run. It used the full verifier vocabulary, sample_from_anchor=true, and up to 1,024 anchors per sequence.

Key training configuration

train:
  speculator_type: dspark
  seed: 42
  verifier:
    verifier_name_or_path: Qwen/Qwen3.8-27B
  data:
    total_seq_len: 8192
    train_data_ratio: 0.99
    max_anchors: 1024
  loss:
    loss_fn: '{"ce":0.1,"tv":0.9}'
  optimizer:
    optimizer: muon
    lr: 0.001
    muon_lr: 0.001
  trainer:
    epochs: 1
    checkpoint_freq: 0.05
    fsdp_shard: true
  dflash:
    block_size: 8
    sample_from_anchor: true

Hidden-state extraction used vLLM 0.29.0 with data-parallel size 4, tensor-parallel size 1, and target layer IDs 4 12 20 28 36 44 52 60. Training used Speculators commit 876e2b6.

Model Specifications

Base Model	Qwen/Qwen3.8-27B
Chat Template	Qwen/Qwen3.8-27B (use /chat/completions)
Architecture	DSparkDraftModel
Format	Safetensors, bfloat16
License	Apache 2.0
Draft Layers	5
Target Layer IDs	4, 12, 20, 28, 36, 44, 52, 60
Draft Vocab Size	248,320
Mask Token ID	248,077
Training Sequence Length	8,192
Maximum Anchors	1,024
Confidence Head	Vanilla Markov, rank 256
Training Hardware	8x NVIDIA H200: 4 extraction + 4 FSDP training GPUs
Validation Hardware	1x NVIDIA H100

Deployment

vllm serve Qwen/Qwen3.8-27B \
  --speculative-config '{"model":"RedHatAI/Qwen3.8-27B-speculator.dspark","num_speculative_tokens":8,"method":"dspark"}'

Acceptance Rates

Evaluation used vLLM 0.29.0, a dense Qwen/Qwen3.8-27B verifier.

Per-position acceptance rates:

Dataset	Acceptance Length	Pos 0	Pos 1	Pos 2	Pos 3	Pos 4	Pos 5	Pos 6	Pos 7
HumanEval	4.42	82.5%	65.4%	51.4%	40.9%	33.0%	27.0%	22.7%	19.5%
math_reasoning	5.74	89.6%	79.9%	70.1%	61.2%	53.7%	45.9%	39.6%	33.8%
qa	4.22	79.0%	62.0%	49.1%	39.1%	31.3%	25.2%	19.9%	15.9%
question	3.80	78.0%	58.2%	43.0%	31.9%	24.1%	18.6%	14.6%	11.6%
rag	4.81	83.9%	69.6%	57.3%	47.4%	39.4%	32.9%	27.3%	22.9%
summarization	3.94	81.8%	63.3%	48.1%	35.2%	25.3%	18.1%	12.8%	9.4%
tool_call	3.76	77.9%	57.6%	42.4%	31.4%	23.6%	18.0%	14.1%	11.2%
translation	4.55	83.4%	68.1%	55.5%	44.6%	35.7%	28.7%	22.2%	17.3%
writing	3.77	77.7%	57.6%	42.6%	31.7%	23.9%	18.4%	14.4%	11.4%

Long-Context Acceptance

Evaluation of RedHatAI/Qwen3.8-27B-speculator.dspark using 8 speculative tokens on MRCR 8-Needle.

Prompt length	Unique requests	Mean acceptance length
4K–8K	29	4.745
8K–16K	24	4.041
16K–32K	21	4.627
32K–65K	25	4.484
65K–131K	23	4.345
131K–262K	26	4.843
262K–524K	24	4.671
524K–1M	38	4.400
1M+	14	4.297
Overall	224	4.492

• Maximum successful prompt length: 1,114,783 tokens

Performance Eval Against MTP

Across HumanEval, math reasoning, and RAG, DSpark consistently delivers higher throughput and better interactivity than MTP with the same eight speculative tokens. The advantage is strongest at low-to-medium concurrency—particularly for math reasoning—and remains positive at concurrency 128, though it narrows for RAG. Both methods scale smoothly, while DSpark sustains lower per-token latency as load increases.