Run Qwen3-Next-80B on 8GB GPU at 1tok/2s throughput

In plain words

This project enables running the Qwen3-Next-80B model on a graphics card with only 8GB of memory, achieving approximately one token per two seconds of processing speed. It is designed for developers and enthusiasts who want to use a large language model on resource-constrained hardware. The approach makes a substantial model accessible to users with modest GPU capabilities, available through a GitHub repository.

written from the facts on this page · September 2026

From the sources

oLLM is a lightweight Python library for large-context LLM inference, built on top of Huggingface Transformers and PyTorch. It enables running models like gpt-oss-20B , qwen3-next-80B or Llama-3.1-8B-Instruct on 100k context using ~$200 consumer GPU with 8GB VRAM. No quantization is used—only fp16/bf16 precision. Supported GPUs : NVIDIA (with additional performance benefits from kvikio and flash-attn ), AMD, and Apple Silicon (MacBook). git clone https://github.com/Mega4alik/ollm.git cd ollm pip install --no-build-isolation -e . # for Nvidia GPUs with cuda (optional): pip install kvikio-cu{cuda_version} Ex, kvikio-cu12 # speeds up the inference 💡 Note voxtral-small-24B requires additional…from github.com

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