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RakshitAralimatti 
posted an update Aug 8, 2025
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🤔 Ever wondered how OpenAI’s massive GPT‑OSS‑20B runs on just 16 GB of memory or how GPT‑OSS‑120B runs on a single H100 GPU?

Seems impossible, right?

The secret is Native MXFP4 Quantization it's a 4-bit floating-point format that’s making AI models faster, lighter, and more deployable than ever.

🧠 What’s MXFP4?

MXFP4, or Microscaling FP4, is a specialized 4-bit floating‑point format (E2M1) standardized by the Open Compute Project under the MX (Microscaling) specification. It compresses groups of 32 values using a shared 8-bit scale (E8M0), dramatically lowering memory usage while preserving the dynamic range perfect for compact AI model deployment.

💡 Think of it like this:

Instead of everyone ordering their own expensive meal (full-precision weights), a group shares a family meal (shared scaling). It’s cheaper, lighter, and still gets the job done.

✍️ I’ve broken all of this down in my first Medium blog:

What’s MXFP4? The 4-Bit Secret Powering OpenAI’s GPT‑OSS Models on Modest Hardware
Link - https://medium.com/@rakshitaralimatti2001/4-bit-alchemy-how-mxfp4-makes-massive-models-like-gpt-oss-feasible-for-everyone-573d6630b56c

HF - https://huggingface.co/blog/RakshitAralimatti/learn-ai-with-me
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I find it more appropriate to research how to run a large model on small hardware, rather than how to run a small model on small hardware.