deepseek: DeepSpec: Open-Source Codebase for Speculative
Full-stack toolkit for training and evaluating speculative decoding algorithms
★ 4.0 / 5 iKeep scoreScreenshots
Quick facts
Overview
DeepSpec is a comprehensive codebase for developing draft models used in speculative decoding. It provides utilities for data preparation, model training, and performance evaluation across multiple benchmarks.
Getting started with GitHub - deepseek-ai/DeepSpec: DeepSpec: a full-stack codebase for training and evaluating speculative decoding algorithms
- Clone the repositoryStart by cloning the DeepSpec repository from GitHub using 'git clone https://github.com/deepseek-ai/DeepSpec'.
- Install dependenciesInstall the required dependencies by following the instructions in the repository's README or requirements.txt file.
- Prepare dataUse the provided utilities to prepare your datasets or use the included benchmark datasets like GSM8K and HumanEval.
- Train modelRun the configurable training scripts to train draft models on your prepared data or benchmarks.
- Evaluate performanceUse the included evaluation metrics to assess the performance of your speculative decoding models.
Pricing
| Plan | Price | Note |
|---|---|---|
| Open-Source | Free | — |
💡 Indicative pricing — check the official rates at github.com · Updated 7/16/2026
Original pricing screenshotKey features
- End-to-end pipeline for speculative decoding research
- Supports multiple draft model architectures
- Includes benchmark datasets (GSM8K, HumanEval, etc.)
- Configurable training scripts for different hardware setups
- Large-scale target cache preparation (up to 38TB)
- Evaluation metrics for speculative decoding performance
Pros
- Complete implementation from data prep to evaluation
- Supports popular models like Qwen3-4B
- Detailed documentation for each workflow stage
- Optimized for multi-GPU training
Cons
- Requires significant storage for target caches
- Assumes access to high-end GPU hardware
- Steep learning curve for ML beginners
Use cases
- Researchers developing speculative decoding techniques
- Teams optimizing LLM inference speeds
- ML engineers benchmarking draft models
- Academic projects in efficient NLP
The verdict
DeepSpec is a powerful research tool for teams serious about optimizing language model inference through speculative decoding, though its hardware demands make it best suited for well-resourced organizations.
✓ Who should use it
ML research teams with access to GPU clusters who need to rigorously evaluate speculative decoding approaches.
✕ Who should skip it
Casual users or those without access to high-performance computing resources.
Categories
Community
- GitHub Issues ↗Official issue tracker and support community
Great for
Featured in
Frequently asked questions
Is GitHub - deepseek-ai/DeepSpec: DeepSpec: a full-stack codebase for training and evaluating speculative decoding algorithms free?
GitHub - deepseek-ai/DeepSpec: DeepSpec: a full-stack codebase for training and evaluating speculative decoding algorithms is free to use.
What is GitHub - deepseek-ai/DeepSpec: DeepSpec: a full-stack codebase for training and evaluating speculative decoding algorithms best for?
AI researchers and ML engineers working on efficient language model inference
Does GitHub - deepseek-ai/DeepSpec: DeepSpec: a full-stack codebase for training and evaluating speculative decoding algorithms have an API?
No, GitHub - deepseek-ai/DeepSpec: DeepSpec: a full-stack codebase for training and evaluating speculative decoding algorithms does not offer a public API.
Can I self-host GitHub - deepseek-ai/DeepSpec: DeepSpec: a full-stack codebase for training and evaluating speculative decoding algorithms?
Yes, GitHub - deepseek-ai/DeepSpec: DeepSpec: a full-stack codebase for training and evaluating speculative decoding algorithms can be self-hosted.
Who should use GitHub - deepseek-ai/DeepSpec: DeepSpec: a full-stack codebase for training and evaluating speculative decoding algorithms?
ML research teams with access to GPU clusters who need to rigorously evaluate speculative decoding approaches.
Reviews 4.0 ★ · 1 review
DeepSpec delivers exactly what it promises - a complete, well-documented toolkit for speculative decoding research. The inclusion of multiple benchmark datasets and configurable training scripts makes it particularly valuable for comparing different approaches. While the storage requirements are substantial, the tradeoff is justified by the comprehensive evaluation capabilities. This is clearly built by researchers for researchers, with thoughtful touches like multi-GPU support and detailed configuration options. The main limitation is the steep learning curve, but for teams serious about optimizing LLM inference speeds, DeepSpec provides an excellent foundation.
No reviews yet. Be the first to review GitHub - deepseek-ai/DeepSpec: DeepSpec: a full-stack codebase for training and evaluating speculative decoding algorithms.
Write a review
Related tools
