Discussion on KV Cache and Memory Tiering Benchmarks
AccBalanced · x · 2026-07-11
This repost delves into KV cache management and memory/storage tiering in AI inference, focusing on how future tiering hierarchies should be designed.
The quoted content highlights an Oracle Cloud WEKA AMG benchmark. Instead of traditional synthetic stress tests, it evaluates real-world agent swarm workloads on modern models. The results claim a 10x improvement in tokens per GPU per watt under high concurrency and strict real-world SLOs. This is used to discuss the relationship between DRAM bandwidth, NVLink, and node-level memory bandwidth in Hopper systems.
More from Infra
- HBM demand shifts: low-stack inference plans shelved, Rubin to go 12-high only — BenBajarin · 2026-10-26
- CircuitHub unveils The Grid, a factory-scale robot making PCBs in the US twice as fast as Shenzhen — garrytan · 2026-10-26
- Ubicloud publishes "Fixing Tool-Call Accuracy in vLLM" — petrusenko_max · 2026-10-26
- Meta approves nonvoting Class C shares as 2026 capex guidance hits $135B–$145B — thione · 2026-10-26
- Unsloth Ships MiniMax-M3.1-Flash Quants: 2-bit Retains 91.5% Accuracy, Runs in 150GB RAM — danielhanchen · 2026-10-26
- Engineer with thousands of fabricated devices pours cold water on photonic crystal modulator yields — jwt0625 · 2026-10-26