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MXFP4

3 articles · Page 1 of 1
Alibaba Cloud Infrastructure
Alibaba Cloud Infrastructure
Jul 28, 2026 · Artificial Intelligence

Day0 Adaptation of Kimi K3 on Alibaba Cloud Lingjun Zhenwu M890 Supernode

On July 27, Alibaba Cloud announced that its Lingjun Zhenwu M890 supernode instance has been successfully adapted to run the 2.8‑trillion‑parameter Kimi K3 model, achieving a 35 % reduction in first‑token latency, a 1.8× increase in decode throughput, and support for up to 1 M token context through joint chip, software‑stack and framework optimizations.

KDAKimi K3Lingjun Zhenwu M890
0 likes · 6 min read
Day0 Adaptation of Kimi K3 on Alibaba Cloud Lingjun Zhenwu M890 Supernode
Machine Heart
Machine Heart
May 27, 2026 · Artificial Intelligence

AMD Paper Finds FP4 Training Instability Is Not Due to Randomness, 9‑10% Speedup

The authors demonstrate that FP4 training instability stems from structural micro‑scaling errors in the weight‑gradient path rather than insufficient randomness, and show that a deterministic Hadamard rotation restores convergence, delivering a 9‑10% end‑to‑end speedup on native FP4 hardware (AMD MI355X) while incurring only 8‑9% token overhead.

Deterministic HadamardFP4MXFP4
0 likes · 10 min read
AMD Paper Finds FP4 Training Instability Is Not Due to Randomness, 9‑10% Speedup
Data Party THU
Data Party THU
Sep 4, 2025 · Artificial Intelligence

How MXFP4 Quantization Lets a 1200‑Billion‑Parameter LLM Run on a Single 80GB GPU

This article analyzes the memory bottleneck of massive language models, explains the mathematical modeling of memory requirements, evaluates traditional sharding limits, and details how GPT‑OSS’s MXFP4 quantization combined with Mixture‑of‑Experts reduces memory, bandwidth, and compute demands enough to fit a 1200‑billion‑parameter model onto an 80 GB GPU with minimal accuracy loss.

FP4LLMMXFP4
0 likes · 11 min read
How MXFP4 Quantization Lets a 1200‑Billion‑Parameter LLM Run on a Single 80GB GPU