DeepSeek (GitHub) Notify me• Jun 26, 2026code_release

DeepSeek ships DeepSpec: DeepSpec: a full-stack codebase for training and evaluating speculative decoding algorithms

Liang Wenfeng(guest)

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Executive Summary

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End-to-End Speculative Decoding Framework Practical Implementation and Resource Intensity

9 quotes

Concerns Raised

Significant storage requirement for the target cache (e.g., ~38 TB), posing a practical barrier.
High computational hardware requirements, with default configurations assuming an 8-GPU node.
Complexity of the multi-stage workflow (data prep, training, evaluation) may be challenging for new users.

Opportunities Identified

Accelerating inference for large language models, reducing latency and computational cost.
Fine-tuning draft models for domain-specific tasks to achieve higher performance gains.
Provides a standardized framework for researching and developing new speculative decoding algorithms.

Key Themes

Research Findings9

The target cache generated during DeepSpec's data preparation stage can be approximately 38 TB for the default Qwen/Qwen3-4B setting.

DeepSpec is a full-stack codebase for training and evaluating draft models for speculative decoding.

The default training configurations and scripts for DeepSpec assume a single node with 8 GPUs.

DeepSpec's evaluation script runs against benchmarks including gsm8k, math500, aime25, humaneval, mbpp, livecodebench, mt-bench, alpaca, and arena-hard-v2.

The released DeepSpec checkpoints were trained on open-perfectblend data generated by the corresponding target model in non-thinking mode.

For domain-specific use cases, DeepSpec draft models should be fine-tuned to achieve better results, especially if the target model is expected to run in thinking mode.

DeepSpec includes implementations for three draft model algorithms: DSpark, DFlash, and Eagle3.

The DeepSpec codebase is released under the MIT License.

The DeepSpec repository supports the Qwen3 and Gemma model families as target models.

Topics

Speculative Decoding LLM Inference Optimization Model Acceleration Draft Models Target Models DeepSpec DSpark DFlash Eagle3 Qwen3 Gemma Open Source AI AI Infrastructure MLOps Model Training AI Benchmarking gsm8k humaneval

Processed Jul 28, 2026Daily intelligence brief → yt-dlp + mlx-whisper + Gemini

DeepSeek ships DeepSpec: DeepSpec: a full-stack codebase for training and evaluating speculative decoding algorithms

Continue your research

Concerns Raised

Opportunities Identified

Key Themes

End-to-End Speculative Decoding Framework

Practical Implementation and Resource Intensity

Open Source and Extensibility

Standardized Benchmarking and Reproducibility

Research Findings9

Topics