arxiv arXiv cs.AI · 51d ago · research

UltraQuant: 4-bit KV Caching for Context-Heavy Agents

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UltraQuant enables 4-bit KV caching for context-heavy agents, reducing P50 time-to-first-token by 3.47x in late rounds and boosting output throughput by 1.63x over FP8 KV baseline. It achieves this using FP8 queries, FP4 KV tensors, UE8M0 group scales, and native scaled-MFMA on AMD CDNA4 GPUs, with optimizations for decode-attention kernels and robust design choices like asymmetric K/V treatment and Walsh-Hadamard rotation.

Importance 3/3 New feature vs. leaders New harness with differentiators arXiv cs.AI Microsoft Research Hugging Face NVIDIA AI agents Inference efficiency Training methods

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arxiv arXiv cs.LG · 51d ago · 1 view

UltraQuant: 4-bit KV Caching for Context-Heavy Agents

UltraQuant introduces a 4-bit KV caching method tailored for context-heavy agent workloads. It achieves 3.47x reduction in P50 time-to-first-token in late rounds and 1.63x higher output throughput compared to FP8 KV caching, using FP8 queries, FP4 KV tensors, and native AMD CDNA4 scaled-MFMA support.

media r/LocalLLaMA · 50d ago · 3 views

Fixing Long-Context Decode Cliff on Radeon R9700 with vLLM 0.22.1

A long-context decode performance cliff on AMD Radeon AI PRO R9700 (RDNA4) was resolved by enabling AITER Unified Attention in vLLM 0.22.1. The fix involves relaxing a CDNA gate to include RDNA4, disabling other attention backends, and using bf16 KV cache, resulting in significant speedups across all context lengths. FP8 KV is ineffective on this hardware, and the model's native 262K context is fully achievable with bf16, offering ~2.9× concurrency without needing FP8.

arxiv arXiv cs.LG · 51d ago · 3 views

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lab Hugging Face Blog · 30d ago

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arxiv arXiv cs.LG · 52d ago

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