Model option: AWQ or quota? | Generated by AI

Home 2026.07

LibreChat was already cloned successfully earlier — no action needed there.

Which option do you want for the model? Quick summary:

  1. Smaller AWQ model (~4 GB) — I’ll switch to something like Qwen2.5-7B-Instruct-AWQ which fits easily. Available now.

  2. Bump RunPod volume quota to 20+ GB — If you can increase it in RunPod dashboard, I’ll re-download the full Gemma 4 26B AWQ.

  3. Stream-load from HF — Launch SGLang with the model path pointing to the original HuggingFace repo so it loads on-demand, no full download needed (but needs enough VRAM for the FP16 model which is 50GB — won’t fit 16GB VRAM).

  4. Use the original Gemma 4 FP16 with SGLang’s quantization — Load google/gemma-4-26B-A4B-it and SGLang quantizes on-the-fly to fit VRAM, but still needs the full ~50 GB download first.

Options 1 or 2 are the most realistic. Which way?

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