Alphabet's Google has unveiled its KV cache quantization compression technology, TurboQuant, promising dramatic reductions in ...
Batch size has a significant impact on both latency and cost in AI model training and inference. Estimating inference time ...
Within 24 hours of the release, community members began porting the algorithm to popular local AI libraries like MLX for ...
A technical paper titled “HMComp: Extending Near-Memory Capacity using Compression in Hybrid Memory” was published by researchers at Chalmers University of Technology and ZeroPoint Technologies.
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 ...
Large-scale applications, such as generative AI, recommendation systems, big data, and HPC systems, require large-capacity and high-speed memory and are changing the power-law locality, which ...
What happens when cache doubles across all cores? A desktop processor design focuses on reducing memory bottlenecks in ...
Unveiled at Google’s annual Next event, the pair showcased using Managed Lustre as a shared cache layer across inference ...
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