A reference CUDA worker that serves Meta's official Muse Glimmer 30B GGUF through llama-server on Runpod Serverless load-balancing endpoints or manual Pods, exposing a real OpenAI-compatible API.
GitHub · Local & open models
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Been building this for a few months, mostly for myself, and it just got a proper release so figured I'd post it. It's a native GGUF inference runtime with OpenAI/Anthropic-compatible APIs and a chat UI. The whole point is one consumer NVIDIA card + lots of RAM: MoE models that don't fit in VRAM run their experts on the CPU, or split with a hot set cached on the card. It figures out what fits at startup instead of you guessing offload layer counts. Runs Qwen 3.x dense and MoE (incl. Qwen3.8-Flash-Next), DeepSeek-V4-Flash, Ling 3.0, K2-Horizon, Gemma 4, Laguna, Muse Glimmer. Image input via mmproj on the Qwen models. Also does Z-Image-Turbo image gen next to a chat model on the same card. Numbers from my laptop (5070 Ti 12 GB, 60 GB RAM): - Qwen3.8-Flash-Next IQ1_S: ~35 tok/s decode, ~475 tok/s prefill - Qwen3.8-27B IQ2_XXS: ~40 tok/s - DeepSeek-V4-Flash: 6-7 tok/s (that's basically the DRAM bandwidth limit) - Z-Image 1024x1024 in ~15 s with a 35B loaded beside it Stuff I think is neat: - Kernels are compiled at runtime by NVRTC, so no CUDA toolkit in the wheel and no nvcc. Same kernel source compiles as plain C++ for a CPU-only backend. - KV cache in f16 / q8_0 / TurboQu
Reddit post · Local & open models
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