This is a practical skill for running quantized LLMs when you're hitting memory limits. It covers GPTQ, a post-training quantization method that compresses models down to 4-bit precision with minimal accuracy loss. The guidance is useful: it tells you when to use GPTQ versus AWQ, with specific hardware examples like fitting 70B+ models on consumer GPUs like the RTX 4090. You get about 4× memory reduction and 3-4× inference speedup compared to FP16. The skill has solid traction with 282 installs and comes from a well-starred template repository. Good reference if you're optimizing deployment costs or working with limited hardware.
- skills.sh installs
- 279
- GitHub stars
- 33.2K
- last updated
- Aug 21, 2026
Install
$ npx -y skills add davila7/claude-code-templates --skill gptq --agent claude-codeInstalls into .claude/skills of the current project.
Files
This is a practical skill for running quantized LLMs when you're hitting memory limits. It covers GPTQ, a post-training quantization method that compresses models down to 4-bit precision with minimal accuracy loss. The guidance is useful: it tells you when to use GPTQ versus AWQ, with specific hardware examples like fitting 70B+ models on consumer GPUs like the RTX 4090. You get about 4× memory reduction and 3-4× inference speedup compared to FP16. The skill has solid traction with 282 installs and comes from a well-starred template repository. Good reference if you're optimizing deployment costs or working with limited hardware.
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- Repository
- github.com/davila7/claude-code-templates
- First seen
- Jun 3, 2026
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