# Supported models

FreeToken loads HF safetensors checkpoints directly. The checkpoints below are known-good — the prebuilt kernels are tuned
for them; other checkpoints of the same architectures work too.

| Model | HF checkpoints |
|---|---|
| DeepSeek-V4 | [deepseek-ai/DeepSeek-V4-Flash-0731](https://huggingface.co/deepseek-ai/DeepSeek-V4-Flash-0731) |
| GLM-5.3-Flash | [RedHatAI/GLM-5.3-Flash-NVFP4](https://huggingface.co/RedHatAI/GLM-5.3-Flash-NVFP4) |
| GLM-5.2 | [nvidia/GLM-5.2-NVFP4](https://huggingface.co/nvidia/GLM-5.2-NVFP4) |
| GLM-4.7 | [nvidia/GLM-4.7-NVFP4](https://huggingface.co/nvidia/GLM-4.7-NVFP4) |
| Qwen3.8-Flash-Next | [Qwen/Qwen3.8-Flash-Next-FP8](https://huggingface.co/Qwen/Qwen3.8-Flash-Next-FP8), [RadixArk/Qwen3.8-Flash-Next-NVFP4](https://huggingface.co/RadixArk/Qwen3.8-Flash-Next-NVFP4), [nvidia/Qwen3.8-Flash-Next-NVFP4](https://huggingface.co/nvidia/Qwen3.8-Flash-Next-NVFP4) |
| Qwen3.6 / Qwen3.5 MoE | [Qwen/Qwen3.6-35B-A3B](https://huggingface.co/Qwen/Qwen3.6-35B-A3B) ([-FP8](https://huggingface.co/Qwen/Qwen3.6-35B-A3B-FP8)), [nvidia/Qwen3.6-35B-A3B-NVFP4](https://huggingface.co/nvidia/Qwen3.6-35B-A3B-NVFP4), [Qwen/Qwen3.5-35B-A3B](https://huggingface.co/Qwen/Qwen3.5-35B-A3B) ([-FP8](https://huggingface.co/Qwen/Qwen3.5-35B-A3B-FP8)) |
| Qwen3.8 / Qwen3.6 dense | [Qwen/Qwen3.8-27B](https://huggingface.co/Qwen/Qwen3.8-27B) ([-FP8](https://huggingface.co/Qwen/Qwen3.8-27B-FP8)), [RadixArk/Qwen3.8-27B-NVFP4](https://huggingface.co/RadixArk/Qwen3.8-27B-NVFP4), [Qwen/Qwen3.6-27B](https://huggingface.co/Qwen/Qwen3.6-27B) ([-FP8](https://huggingface.co/Qwen/Qwen3.6-27B-FP8)), [nvidia/Qwen3.6-27B-NVFP4](https://huggingface.co/nvidia/Qwen3.6-27B-NVFP4) |
| Qwen3-MoE | [Qwen/Qwen3-30B-A3B](https://huggingface.co/Qwen/Qwen3-30B-A3B) |
| gpt-oss | [openai/gpt-oss-120b](https://huggingface.co/openai/gpt-oss-120b), [openai/gpt-oss-20b](https://huggingface.co/openai/gpt-oss-20b) |
| Gemma-4 | [google/gemma-4-26B-A4B-it](https://huggingface.co/google/gemma-4-26B-A4B-it), [nvidia/Gemma-4-26B-A4B-NVFP4](https://huggingface.co/nvidia/Gemma-4-26B-A4B-NVFP4), [google/gemma-4-12B-it](https://huggingface.co/google/gemma-4-12B-it), [nvidia/Gemma-4-31B-IT-NVFP4](https://huggingface.co/nvidia/Gemma-4-31B-IT-NVFP4) .. |
| MiniMax-M2.5 | [nvidia/MiniMax-M2.5-NVFP4](https://huggingface.co/nvidia/MiniMax-M2.5-NVFP4) |
| Muse-Glimmer | [meta-models/Muse-Glimmer-30B](https://huggingface.co/meta-models/Muse-Glimmer-30B), [RedHatAI/Muse-Glimmer-30B-NVFP4](https://huggingface.co/RedHatAI/Muse-Glimmer-30B-NVFP4) |

## MoE strategies

`ft serve --moe-strategy {auto,fused,offload,cpu,hybrid}` (`--moe-backend` is the deprecated old spelling):

- **fused** — experts resident on GPU (needs the VRAM); never auto-selected.
- **offload** — experts live in host RAM, an LRU cache of expert slots on GPU;
  misses stream over PCIe.
- **cpu** — misses are computed on the CPU instead of fetched.
- **hybrid** — per step, fetches some misses over PCIe and computes the rest on
  CPU, overlapped. Run `ft bench bw` once per machine to calibrate the split.
- **auto** — dense models always resolve to `fused`; MoE models resolve to
  `offload`, upgraded to `hybrid` when a cached `ft bench bw` profile
  recommends it.

## Notes

- `ft checkpoint` conversion is optional — it pre-converts a checkpoint into
  FreeToken's fast-load format, and `ft serve --model` auto-detects the result.
- FTW files converted by builds before the quantization refactor may fail to load;
  see [ftw-hotfix.md](ftw-hotfix.md) for the affected checkpoints and the repair tool.
- DeepSeek-V4 checkpoints must keep the `inference/config.json` subdir — the
  authoritative model args are read from there.
- Qwen3.8-Flash-Next keeps a 47.7 GiB PLE n-gram table pinned in host RAM.
- Multimodal checkpoints are served text-only.