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llama.cpp quantization: run models bigger than your VRAM

Use low-bit quantized GGUF models and CPU plus GPU hybrid inference to run models larger than your VRAM.

Who it is forAn ML hobbyist with an 8 GB GPU who wants to try a mid-size model.

The problemFull-precision models do not fit consumer GPUs.

The outcomeA larger model running with partial GPU acceleration.

What you need first

  • macOS, Linux or Windows
  • Enough RAM or VRAM for the chosen GGUF model

Steps

  1. Install llama.cpp with the backend for your GPU (CUDA, Metal, Vulkan, HIP and more)
  2. Choose a 4-bit or lower quantized GGUF of the model
  3. Run it with llama cli and let layers spill to CPU
  4. Check the performance troubleshooting doc if it is slow

Prompt for your agent

Run a 4-bit GGUF of <model> with llama.cpp on a machine with <VRAM> GB VRAM using CPU+GPU hybrid inference. Report tokens per second and confirm the model loaded fully.

Caveat. Lower-bit quantization reduces memory use but can reduce output quality.

Use this in your AI agent

Ask your agent

Using the Deduped MCP, get the setup steps for ggml-org/llama.cpp and "Fit a bigger model on limited hardware"

Add Deduped to Claude Code once

claude mcp add --transport http deduped https://dedupedai.com/mcp

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