Keep GPUs fed. Stop recomputing results you've already got.
AI hits storage in three places: loading training data, writing checkpoints and, for LLM inference, holding KV cache that no longer fits in GPU memory. We handle all three with the same storage engineering we do for HPC.
Talk to an AI storage engineerWhat we offer
- GPU HBMActive KV cache for running requestsfastest · smallest
- CPU DRAMHot offload, local to the inference nodeµs
- Local NVMeLarger offload, survives evictiontens of µs
- Shared storageParallel / distributed FS, shared across nodeslargest · shared
Prefill once, reuse everywhere: KV cache spills down the tiers and is shared across inference nodes instead of being recomputed.
AI storage assessment
When GPUs wait on storage during training or inference, we analyse the data path and give you a KV-cache and GPUDirect plan, with sizing.
LLM inference: KV cache
Integrate and tune LMCache so long contexts and repeated prompts reuse cached KV instead of re-running prefill.
GPUDirect Storage
CI that builds and tests against NVIDIA GPUDirect Storage (direct NVMe-to-GPU DMA), with an automated DOCA compatibility matrix per distro and kernel.
Training data & checkpoints
Small-file reads tuned so data loaders keep pace with GPUs; bursty parallel checkpoint writes absorbed by the filesystem tier.
Other solutions
Lustre storage
Design, deploy, automate, tune and benchmark Lustre, then keep it running with ongoing support.
eBPF observability
Find the bottleneck in live systems without redeploying, and get custom eBPF tools built for your stack.
Storage slower than your compute?
Send us your topology, workload and a benchmark run. We will get back to you.