HPE storage reuses AI inference cache to speed multi-agent workloads
HPE is positioning its Alletra MP X10000 storage system as a way to retain AI inference cache that would otherwise be removed from scarce GPU memory. Reusing this stored state can reduce repeated calculations, potentially cutting response times and improving accelerator use in multi-agent workloads.
In testing validated by Kamiwaza, using NVIDIA H200 GPUs and the Nemotron 70B model with more than 30 concurrent agents, cache offload delivered up to 20 times faster time to first token and 17 times higher effective inference throughput than running without it. The gains depend on factors such as context length, concurrency, cache hit rates and data transfer overhead, so they are not guaranteed for every workload.
In short
- HPE storage can retain AI inference cache outside GPU memory.
- A Kamiwaza-validated test reported up to 20 times faster first tokens.
- Results depend on workload and cache reuse.
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The case for
Moving inference cache to storage could help multi-agent systems retain useful state when GPU memory is scarce, avoiding repeated calculations and making accelerators more productive. The reported tests show substantial gains in a demanding setup, and even workload-dependent improvements could make larger or more responsive deployments practical.
The case against
The results come from a specific test configuration, and the benefits depend on cache hits, context length, concurrency and transfer overhead; other workloads may see smaller gains or none. Storing and retrieving cache also adds system complexity and resource demands, so teams would need broader comparisons to establish whether it improves overall performance and cost in their own deployments.
Explained simply
AI models generate replies through a process called inference, which uses powerful graphics processing units (GPUs). When several AI agents work at once, they may repeatedly process the same information, while the limited memory on those chips can force useful working data to be discarded.
HPE makes storage systems, including Alletra MP X10000, which can keep that working data outside GPU memory for possible reuse. This stored information, known as an inference cache, may reduce repeated calculations; how much it helps depends on the workload and how often the saved data is needed.
The approach is relevant as organisations run more complex AI systems with multiple agents, where speed and efficient use of computing hardware can affect cost and responsiveness. Any performance improvement varies with factors such as workload size, how much information is reused and the time needed to move it between storage and GPUs.
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Originally published by The Register as “HPE puts storage to work on AI’s expensive memory problem”.