Performance

CoreWeave's LOTA benchmarks are transparent and well run, but the warm-cache headline numbers measure local NVMe inside the GPU nodes, not the object store behind them. Using the same open-source Warp tool with no cache at all, MinIO AIStor delivered roughly 33.5 GiB/s of durable, erasure-coded GET throughput per storage node over plain TCP, making the case for a faster store rather than another layer in front of it.

Every coding assistant, enterprise chatbot, and agent loop resends the same tool schemas, system instructions, and policy text on every turn, and the GPU rebuilds all of it into attention state before it can emit a single output token. Prompt caching computes that prefix once and reuses the KV state, and MemKV takes it from a per-process optimization to a shared NVMe-backed tier that survives routing across replicas, HBM eviction, worker restarts, and concurrency.

Cache hits are not the outcome. Part 3 turns the architecture from Parts 1 and 2 into an evaluation framework: capture an honest tail-latency baseline first, treat MinIO's published 53-second-to-703-millisecond TTFT result as a proof point to reproduce rather than a business-case input, and follow the measurement chain from repeated context through prefix reuse and avoided recompute to unit economics, ending in a buyer checklist that judges a shared context tier on P99 TTFT and jitter rather than throughput or capacity.

A prefix cache that only helps one process is useful, but requests move across replicas, HBM fills, sessions spill, and workers restart, so reuse that lives inside a single worker is not a fleet architecture. Part 2 works through what the serving stack needs once KV state leaves local GPU memory: a tier that is larger than HBM, fast enough that restore beats recompute, shared across workers, and reachable through the runtime's own KV transfer path, which is memory behavior at cluster scope rather than storage.

Agentic AI applications resend the same project rules, tool schemas, and document context on every turn, so an expensive GPU fleet spends much of its time rebuilding a prefix it has already processed. Part 1 of three reframes prompt caching as an operating-margin lever rather than a model feature, maps prompt caching, prefix caching, KV cache, and KV cache offload to the business questions each one answers, and argues that reusable context needs a memory path rather than ordinary enterprise storage.

Learn how AIStor, MemKV, and a secure data fabric form the three-layer GPU stack that slashes inference latency.

Learn how MinLZ enables fast, selective searches of compressed data without decompression, dramatically reducing I/O and accelerating queries on object storage.

Solidigm and MinIO put high-density QLC NVMe object storage under real load and disproved the long-held assumption that density costs performance. The tested node is the same building block that scales directly into MinIO's ExaPOD reference architecture, so the path from a single pod to exascale runs on identical hardware and software.

Not every semantic search problem needs a vector database. NVIDIA cuVS runs GPU vector search in memory; MinIO AIStor persists every artifact from raw documents to indexes. Full pipeline, working code.

MinIO MemKV eliminates the recompute tax in GPU inference clusters with shared petabyte-scale context memory.

106 million vectors indexed 12x faster. AIStor with NVIDIA cuVS and GPUDirect RDMA rewrites the benchmark.

Legacy storage talks to the AI factory. AIStor lives inside it — running natively on NVIDIA BlueField-4 Vera

MinIO is Hadoop HDFS's logical successor—faster, cloud-native, S3-compatible with better economics & simplicity

Inline metadata and erasure coding solve small file performance challenges at scale

MinIO + AMD: Unlocking AI/ML performance with EPYC processors & NVMe drives for high-throughput workloads

How MinIO handles small file performance with erasure coding and inline metadata

Databases on object storage—Druid, ClickHouse, Snowflake, Teradata leverage MinIO for disaggregated scale & performance

Guide to benchmarking MinIO performance with WARP tool measuring throughput and latency

Flexible erasure code configuration for custom data and parity drive ratios per deployment

WARP-speed AI data storage—measure MinIO performance with distributed S3 benchmarking for ML infrastructure validation

Overlay networks with MinIO—Docker & Kubernetes CNI enable secure multi-host container communication for distributed apps

Replication strategies deep dive—compare batch replication, mc mirror, site & bucket replication for different use cases

Internal cluster network architecture and best practices for distributed MinIO scaling

MinIO Day 2 operations—administration, monitoring, scaling, updates & troubleshooting for production cluster management