Virtualization Review

What GPU You Really Need for AI Workloads

GPU memory (VRAM) is the critical limiting factor that determines which AI models you can run, not GPU performance. Total VRAM requirements are typically 1.2-1.5x the model size due to weights, KV ...
Semiconductor Engineering

Dynamic KV Cache Scheduling in Heterogeneous Memory Systems for LLM Inference (Rensselaer Polytechnic Institute, IBM)

A new technical paper titled “Accelerating LLM Inference via Dynamic KV Cache Placement in Heterogeneous Memory System” was published by researchers at Rensselaer Polytechnic Institute and IBM. “Large ...
VentureBeat

Nvidia’s new technique cuts LLM reasoning costs by 8x without losing accuracy

Researchers at Nvidia have developed a technique that can reduce the memory costs of large language model reasoning by up to eight times. Their technique, called dynamic memory sparsification (DMS), ...
Semiconductor Engineering

AI Inference Needs A Mix-And-Match Memory Strategy

Interactive LLMs (chat, copilots, agents) with strict latency targets Long‑context reasoning (codebases, research, video) with massive KV (key value) cache footprints Ranking and recommendation models ...
InfoWorld

Snowflake open sources SwiftKV to reduce inference workload costs

SwiftKV optimizations developed and integrated into vLLM can improve LLM inference throughput by up to 50%, the company said. Cloud-based data warehouse company Snowflake has open-sourced a new ...