GPTQModel: Faster AI
From the repo · “LLM model quantization (compression) toolkit with HW acceleration support for Nvidia, AMD, Intel GPU and Intel/AMD/Apple CPU via HF, vLLM, and SGLang.”
GPTQModel compresses large language models to reduce their size and improve deployment efficiency on various hardware platforms, including Nvidia, AMD, Intel, and Apple CPUs and GPUs.
- •When deploying AI models on edge devices with limited storage or computational resources
- •When reducing the latency and cost of inference in cloud-based AI applications
- •When optimizing AI model size and speed for real-time customer interactions, such as chatbots or voice assistants
python -m gptqmodel.quantize --model gpt2 --config quantization_config.json --hardware accelerator=nvidia
Skip the builder — one click puts this in Claude, Cursor, Antigravity and more.
Installs with a command or two; your AI agent can do it for you.
mkdir -p ~/.claude/skills/gptqmodel && curl -fsSL https://workflowstacks.com/api/skills/gptqmodel/claude-skill -o ~/.claude/skills/gptqmodel/SKILL.mdOpens the app with this repo with the prompt ready to go — no copy-paste needed.
What's inside — free to inspect
Read the entire source before you build — unlike paid marketplaces that hide it behind a buy button.
Skip the builder — one click puts this in Claude, Cursor, Antigravity and more.
Installs with a command or two; your AI agent can do it for you.
mkdir -p ~/.claude/skills/gptqmodel && curl -fsSL https://workflowstacks.com/api/skills/gptqmodel/claude-skill -o ~/.claude/skills/gptqmodel/SKILL.mdOpens the app with this repo with the prompt ready to go — no copy-paste needed.
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