DeepSpec is a broad codebase designed for training and evaluating speculative decoding algorithms, addressing the challenge of generating accurate responses from incomplete or ambiguous inputs.
Source: Official README and linked documentation View on GitHub →DeepSpec is being discussed because its coverage to speculative decoding, addressing the need for algorithms in scenarios where input data is incomplete or ambiguous. Its unique technical choices, such as supporting multiple draft models and extensive data preparation utilities, set it apart in the field.
Source: Official README and linked documentationDeepSpec includes utilities for downloading and splitting training data, regenerating answers, and building a large target cache, which is crucial for training and evaluating speculative decoding algorithms.
Source: Official README and linked documentationThe codebase supports training of draft models against cached target outputs and evaluation on benchmark tasks, providing a full-stack solution for speculative decoding research.
Source: Official README and linked documentationDeepSpec includes three draft models (DSpark, DFlash, Eagle3) and supports various target models like Qwen and Gemma, offering flexibility in model selection and training.
Source: Official README and linked documentationThe architecture of DeepSpec is modular, with distinct components for data preparation, model training, and evaluation. It employs a clear separation of concerns, utilizing design patterns like dependency injection for flexibility and scalability. Data flows through the system in a linear fashion, starting from data preparation to training and finally evaluation.
Source: Official repository tree and dependency filestorchtransformersnumpyPyYAMLtqdmtensorboardmatplotlibtritontyping_extensionssentencepiecesafetensorsprettytabledatasetsopenaiDeepSpec is suitable for researchers and developers in the field of speculative decoding, particularly in scenarios involving incomplete or ambiguous input data, such as question answering systems, language modeling, and natural language processing tasks.
Source: Official README and linked documentationAs of 2026-09-23, the official repository does not have a GitHub Release or a release package with a version number; do not make up a version number for it. The GitHub API shows the most recent push time of the main branch is 2026-07-09.
Source: github.com/deepseek-ai/DeepSpec/releasesstatus: docs-only | checked_at: 2026-09-23 | environment: Check deepseek-ai/DeepSpec official README, requirements.txt, data preparation documentation, license, notice and GitHub APIs; no models downloaded or GPU tasks running | limitations: No local installation or runtime verification was performed. Unverified dependency installability, SGLang/vLLM service, cache actual volume, GPU memory, training convergence, checkpoint quality, acceptance rate, or end-to-end acceleration | observed_output: The official repository does not have GitHub Releases; README lists DSpark, DFlash, Eagle3, the default script assumes a stand-alone 8 GPU; the data document warns Qwen3-4B that the default target cache is about 38 TB
Source: github.com/deepseek-ai/DeepSpec/releases (1) · github.com/deepseek-ai/DeepSpec/blob/main/scripts/data/README.md (2)DeepSpec is clearly organized around DSpark, DFlash, Eagle3 and their paper checkpoints, and is suitable for reproducing results according to DeepSeek's training settings. SpecForge is a broader training to SGLang service framework, the official documentation also covers online/offline, split topology, FSDP and a variety of hardware. The former is more direct when you want to reproduce DeepSpec paper settings; when long-term access to the SGLang training and deployment tooling is required, SpecForge should be evaluated first.
Source: github.com/deepseek-ai/DeepSpec (1) · github.com/sgl-project/SpecForge (2)DeepSpec is a tool for those involved in speculative decoding research, offering a configurable platform for training and evaluating algorithms. Its broad features and modular design make it suitable for both academic research and practical applications in natural language processing and related fields.
The official repository does not have GitHub Releases; README lists DSpark, DFlash, Eagle3, the default script assumes a stand-alone 8 GPU; the data document warns Qwen3-4B that the default target cache is about 38 TB No local installation or runtime verification was performed.
The review environment was: Check deepseek-ai/DeepSpec official README, requirements.txt, data preparation documentation, license, notice and GitHub APIs; no models downloaded or GPU tasks running DeepSpec behavior outside that scope still needs a local check.