GitHub - drumih/turbo-fieldfare: Gemma 4 26B-A4B inference in ~2 GB of RAM on any M-series MacBook 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 - drumih/turbo-fieldfare: Gemma 4 26B-A4B inference in ~2 GB of RAM on any M-series MacBook 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 - drumih/turbo-fieldfare: Gemma 4 26B-A4B inference in ~2 GB of RAM on any M-series MacBook is best for ai developers working with gemma models on apple silicon macs, 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 - drumih/turbo-fieldfare: Gemma 4 26B-A4B inference in ~2 GB of RAM on any M-series MacBookTurboFieldfare is a remarkable technical achievement that brings efficient AI inference to standard M-series MacBooks, making powerful models accessible without specialized hardware. It's particularly valuable for AI developers constrained by RAM limitations.
Pick it if: Unmatched efficiency in running large AI models on standard Mac hardware with minimal RAM requirements.
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 - drumih/turbo-fieldfare: Gemma 4 26B-A4B inference in ~2 GB of RAM on any M-series MacBook | 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 | macOS | Linux, macOS, Windows |
| Best for | AI developers working with Gemma models on Apple Silicon Macs | Developers and researchers needing to run massive models on limited hardware |
| Not ideal for | Those needing cross-platform compatibility or working with non-Gemma AI models. | Those needing byte-exact reproducibility or who don't have sufficient disk space for the expert files. |
GitHub - drumih/turbo-fieldfare: Gemma 4 26B-A4B inference in ~2 GB of RAM on any M-series MacBook
- Extremely efficient RAM usage for AI inference
- Native Apple Silicon optimization
- Open-source and transparent implementation
- Limited to macOS platforms
- Requires macOS 26 or later
- Specialized for Gemma 4 models
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 - drumih/turbo-fieldfare: Gemma 4 26B-A4B inference in ~2 GB of RAM on any M-series MacBook 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. 🐦?
Unmatched efficiency in running large AI models on standard Mac hardware with minimal RAM requirements.. 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. 🐦: The only solution that can run 74
What's the main difference between GitHub - drumih/turbo-fieldfare: Gemma 4 26B-A4B inference in ~2 GB of RAM on any M-series MacBook 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 - drumih/turbo-fieldfare: Gemma 4 26B-A4B inference in ~2 GB of RAM on any M-series MacBook is a artificial intelligence tool best for ai developers working with gemma models on apple silicon macs. GitHub - JustVugg/colibri: Run GLM-5.2 (744B MoE) on a 25GB-RAM consumer machine — pure C, zero
Which is cheaper, GitHub - drumih/turbo-fieldfare: Gemma 4 26B-A4B inference in ~2 GB of RAM on any M-series MacBook 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 - drumih/turbo-fieldfare: Gemma 4 26B-A4B inference in ~2 GB of RAM on any M-series MacBook: 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. 🐦: open-source. Both offer a
Can I self-host GitHub - drumih/turbo-fieldfare: Gemma 4 26B-A4B inference in ~2 GB of RAM on any M-series MacBook 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 - drumih/turbo-fieldfare: Gemma 4 26B-A4B inference in ~2 GB of RAM on any M-series MacBook: 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.