Crypto Briefing

Reiner Pope: Batch size dramatically impacts AI latency and cost, kv cache is key for autoregressive models, and efficient inference can save resources | Dwarkesh

Batch size has a significant impact on both latency and cost in AI model training and inference. Estimating inference time ...
InfoQ

Google’s TurboQuant Compression May Support Faster Inference, Same Accuracy on Less Capable Hardware

Google Research unveiled TurboQuant, a novel quantization algorithm that compresses large language models’ Key-Value caches ...

Graid Technology Launches Agentic AI Storage Portfolio to Eliminate KV Cache Bottlenecks

From edge inference to NVIDIA STX, purpose-built KV cache infrastructure for consistent performance at scale. SUNNYVALE, CA / ...

Google's new TurboQuant algorithm speeds up AI memory 8x, cutting costs by 50% or more

Within 24 hours of the release, community members began porting the algorithm to popular local AI libraries like MLX for ...
Seeking Alpha

Penguin Solutions Introduces Industry's First Production-Ready CXL-Based KV Cache Server

Penguin Solutions MemoryAI KV cache server, an 11TB memory appliance, enables efficient deployment of enterprise-scale AI inference Penguin Solutions MemoryAI KV cache server is the industry's first ...
SDxCentral

DDN, Google Cloud claim Lustre KV cache trick boosts AI inference throughput by 75%

Unveiled at Google’s annual Next event, the pair showcased using Managed Lustre as a shared cache layer across inference ...
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 ...