AI/ML

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.

Published KV cache speedup claims run from sixteen to seventy times, but they come from tests that disable the GPU's own cache and drive traffic from a load generator with no human pauses, which measures a component rather than a fleet. MinIO rebuilt the benchmark around real agentic session behavior, and on the same GPUs MemKV delivered 2.4× the tokens, 2.59× the completed turns, and 4.5× better tail latency on returning turns.

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.

Worker A gathers evidence, publishes an accepted handoff, starts another edit, and is deleted mid-sentence with the unfinished tail left visible. Worker B starts in a fresh runtime with no session state, verifies the last accepted boundary in the same authorized AIStor Memory Workspace, discards the unchecked tail, and continues the work rather than restarting it.

Personal AI agents become useful as they learn you, but that familiarity should not require one agent accumulating your entire life. AIStor Memory gives each agent a bounded relationship with its own learned history, active workspace, and credential scope, all under your control.

Every agent begins with the experience your organization has already earned.

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.

Until recently, data that stayed on-premises was data that Databricks couldn't reach. If your analytics and AI workloads ran in Databricks, and your most valuable data lived on-prem, you had two options: build and maintain a replication pipeline to copy data into the cloud, or accept that certain datasets simply wouldn't participate in your cloud analytics.Both options carry real costs. But a third option now exists: Databricks querying on-premises data directly, with no copies and no pipelines, through the open Delta Sharing protocol embedded natively in MinIO AIStor.

Modern AI architecture rests on a comfortable assumption: when AI slows down, the fix is more compute or a better model. Bigger GPUs. Denser clusters. New architectures. That assumption is now costing organizations real money.

Learn how to build a vector database lab with Milvus and AIStor

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

Delta Sharing + AIStor enables zero-copy access to on-prem data from Databricks without duplication

GPUDirect RDMA bypasses CPU for direct GPU-to-storage S3 data paths, boosting AI factory performance

KV cache offload treats GPU memory as tiered hierarchy using object storage for overflow

Federal agencies need unified data governance and infrastructure before AI algorithms

Introducing ExaPOD—MinIO's 1-exabyte reference architecture with Intel, Solidigm & Supermicro for exascale AI

Automate cluster deployments with MCP—AIStor MCP server cuts demo environment setup from hours to minutes using Claude

Solve sovereign AI challenges for government—on-prem infrastructure ensures secure, compliant AI under national control