GitHub - deepseek-ai/DeepSpec: DeepSpec: a full-stack codebase for training and evaluating speculative decoding algorithms vs GitHub - JustVugg/colibri: Run GLM-5.2 (744B MoE) on a 25GB-RAM consumer machine — pure C, zero deps, experts streamed from disk. Tiny engine, immense model. 🐦
GitHub - deepseek-ai/DeepSpec: DeepSpec: a full-stack codebase for training and evaluating speculative decoding algorithms vs GitHub - JustVugg/colibri: Run GLM-5.2 (744B MoE) on a 25GB-RAM consumer machine — pure C, zero deps, experts streamed from disk. Tiny engine, immense model. 🐦: GitHub - JustVugg/colibri: Run GLM-5.2 (744B MoE) on a 25GB-RAM consumer machine — pure C, zero deps, experts streamed from disk. Tiny engine, immense model. 🐦 scores higher in our editorial review for artificial intelligence. GitHub - deepseek-ai/DeepSpec: DeepSpec: a full-stack codebase for training and evaluating speculative decoding algorithms is best for ai researchers and ml engineers working on efficient language model inference, while GitHub - JustVugg/colibri: Run GLM-5.2 (744B MoE) on a 25GB-RAM consumer machine — pure C, zero deps, experts streamed from disk. Tiny engine, immense model. 🐦 suits developers and researchers needing to run massive models on limited hardware. Below is the full side-by-side on features, pricing and who each fits.
Winner: GitHub - JustVugg/colibri: Run GLM-5.2 (744B MoE) on a 25GB-RAM consumer machine — pure C, zero deps, experts streamed from disk. Tiny engine, immense model. 🐦 edges ahead on editorial score, but pick based on fit — see the table below.
GitHub - deepseek-ai/DeepSpec: DeepSpec: a full-stack codebase for training and evaluating speculative decoding algorithmsDeepSpec 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.
Pick it if: Its comprehensive, production-ready implementation of the entire speculative decoding pipeline sets it apart from research prototypes.
GitHub - JustVugg/colibri: Run GLM-5.2 (744B MoE) on a 25GB-RAM consumer machine — pure C, zero deps, experts streamed from disk. Tiny engine, immense model. 🐦Colibri is an impressive technical achievement that makes massive MoE models accessible on consumer hardware, though with some trade-offs in exact output consistency.
Pick it if: The only solution that can run 744B parameter models on consumer-grade hardware with pure C.
| GitHub - deepseek-ai/DeepSpec: DeepSpec: a full-stack codebase for training and evaluating speculative decoding algorithms | GitHub - JustVugg/colibri: Run GLM-5.2 (744B MoE) on a 25GB-RAM consumer machine — pure C, zero deps, experts streamed from disk. Tiny engine, immense model. 🐦 | |
|---|---|---|
| Category | Artificial Intelligence | Artificial Intelligence |
| Score | 4.0/5 | 4.5/5 |
| Pricing | open-source | open-source |
| Public API | ✕ No | ✕ No |
| Free tier | ✓ Yes | ✓ Yes |
| Self-host | ✓ Yes | ✓ Yes |
| Platforms | Linux | Linux, macOS, Windows |
| Best for | AI researchers and ML engineers working on efficient language model inference | Developers and researchers needing to run massive models on limited hardware |
| Not ideal for | Casual users or those without access to high-performance computing resources. | Those needing byte-exact reproducibility or who don't have sufficient disk space for the expert files. |
GitHub - deepseek-ai/DeepSpec: DeepSpec: a full-stack codebase for training and evaluating speculative decoding algorithms
- Complete implementation from data prep to evaluation
- Supports popular models like Qwen3-4B
- Detailed documentation for each workflow stage
- Optimized for multi-GPU training
- Requires significant storage for target caches
- Assumes access to high-end GPU hardware
- Steep learning curve for ML beginners
GitHub - JustVugg/colibri: Run GLM-5.2 (744B MoE) on a 25GB-RAM consumer machine — pure C, zero deps, experts streamed from disk. Tiny engine, immense model. 🐦
- Extremely efficient memory usage for large models
- No dependencies - simple deployment
- Validated against transformers for accuracy
- Not byte-identical to non-speculative greedy in practice
- Requires significant disk space (~370GB for experts)
Frequently asked questions
Is GitHub - deepseek-ai/DeepSpec: DeepSpec: a full-stack codebase for training and evaluating speculative decoding algorithms better than GitHub - JustVugg/colibri: Run GLM-5.2 (744B MoE) on a 25GB-RAM consumer machine — pure C, zero deps, experts streamed from disk. Tiny engine, immense model. 🐦?
Its comprehensive, production-ready implementation of the entire speculative decoding pipeline sets it apart from research prototypes.. GitHub - JustVugg/colibri: Run GLM-5.2 (744B MoE) on a 25GB-RAM consumer machine — pure C, zero deps, experts streamed from disk. Tiny engine, immense model. 🐦: Th
What's the main difference between GitHub - deepseek-ai/DeepSpec: DeepSpec: a full-stack codebase for training and evaluating speculative decoding algorithms and GitHub - JustVugg/colibri: Run GLM-5.2 (744B MoE) on a 25GB-RAM consumer machine — pure C, zero deps, experts streamed from disk. Tiny engine, immense model. 🐦?
GitHub - deepseek-ai/DeepSpec: DeepSpec: a full-stack codebase for training and evaluating speculative decoding algorithms is a artificial intelligence tool best for ai researchers and ml engineers working on efficient language model inference. GitHub - JustVugg/colibri: Run GLM-5.2 (744B MoE) on a
Which is cheaper, GitHub - deepseek-ai/DeepSpec: DeepSpec: a full-stack codebase for training and evaluating speculative decoding algorithms or GitHub - JustVugg/colibri: Run GLM-5.2 (744B MoE) on a 25GB-RAM consumer machine — pure C, zero deps, experts streamed from disk. Tiny engine, immense model. 🐦?
GitHub - deepseek-ai/DeepSpec: DeepSpec: a full-stack codebase for training and evaluating speculative decoding algorithms: open-source. GitHub - JustVugg/colibri: Run GLM-5.2 (744B MoE) on a 25GB-RAM consumer machine — pure C, zero deps, experts streamed from disk. Tiny engine, immense model. 🐦: o
Can I self-host GitHub - deepseek-ai/DeepSpec: DeepSpec: a full-stack codebase for training and evaluating speculative decoding algorithms or GitHub - JustVugg/colibri: Run GLM-5.2 (744B MoE) on a 25GB-RAM consumer machine — pure C, zero deps, experts streamed from disk. Tiny engine, immense model. 🐦?
GitHub - deepseek-ai/DeepSpec: DeepSpec: a full-stack codebase for training and evaluating speculative decoding algorithms: yes, self-hosting is supported. GitHub - JustVugg/colibri: Run GLM-5.2 (744B MoE) on a 25GB-RAM consumer machine — pure C, zero deps, experts streamed from disk. Tiny engine, immense model. 🐦: yes, self-hosting is supported.