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cs.LG2026
UE5M3 FP4 Block Scaling for Stable Language Model Pretraining
Robert Hu, Carlo Luschi, Paul Balanca
Stable 4-bit floating-point (FP4) pretraining is difficult because the E2M1 payload represents only a narrow range of magnitudes. NVIDIA's Transformer Engine \nv{} recipe addresses…
cs.LG2026
A Practical Investigation of Training-free Relaxed Speculative Decoding
Guoxuan Xia, Luka Ribar, Paul Balanca
Speculative decoding accelerates sampling from an autoregressive LLM by using a faster auxiliary model to draft tokens which are then verified in parallel by the LLM. Standard spec…
cs.LG2026
MXNorm: Reusing MXFP block scales for efficient tensor normalisation
Callum McLean, Luke Y. Prince, Alexandre Payot +2
Matrix multiplication performance has long been the major bottleneck to scaling deep learning workloads, which has stimulated the design of new accelerators that use increasingly l…