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AI Inference at GPU Speed
The Challenge: KV Cache Exhausts GPU Memory. Recomputation Burns the Rest.
AI inference demands are exploding. Large language models serve millions of concurrent users. Agentic AI holds multi-step reasoning across sessions. RAG pipelines pull millions of embeddings per query. Long context windows exceed 1 million tokens. Every inference request now hits storage.
The infrastructure hasn’t caught up. At production concurrency, KV cache blows past GPU HBM. Without a fast offload tier, the system either recomputes from scratch, burning GPU dollars to redo finished work, or stops scaling entirely. Most GPU clusters run at less than 50% utilization. The GPUs aren’t slow. They’re starving for data.
Teams ask for more GPUs to solve a problem that more GPUs won’t fix. The budget grows, but the bottleneck doesn’t move.Teams try three paths, but none solves the underlying problem. First, optimize the model: quantization, distillation, smaller models, smarter batching. Right instinct, but it has a ceiling. It doesn't solve KV cache at production concurrency, doesn't share context across nodes, and doesn't help the agentic state. Second, limit context and concurrency: shrink the context window, cap users. Keeps the system from falling over, but every KV cache eviction still triggers a full recompute. Users get slower responses, competitive advantage disappears.
Third, local NVMe on GPU servers is the most common approach today, but it has the clearest limitations. Fast on a single node, but can't share context across servers. Workload shifts, context is gone. The moment you scale beyond one machine, it breaks. It also carries a hidden cost: drives co-located with GPUs increase overall compute server power and cooling requirements, making disaggregated storage the better path from an infrastructure efficiency perspective. All three hit the same wall: the storage layer.
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The AIStor Solution: Storage That Feeds GPUs at Wire Speed
AIStor sits behind your AI Inference platform as the high-performance data tier. KV cache offload with RDMA. Shared persistent context across the entire GPU cluster. Model weight and embedding storage. All delivered at wire speed through a software-defined architecture that runs on NVMe drives and integrates with standard high-speed networking fabrics. Nothing about your inference platform changes. Your teams keep using the same tools and the same APIs they already know. AIStor replaces the slowest, most expensive part of the current architecture with something that actually keeps GPUs saturated.In production configurations, AIStor delivers roughly 45 gibibytes per second of read and write throughput per node on 400 gigabit Ethernet, with approximately 5.3 microsecond client-to-server read latency. This is not generic object storage repurposed for inference. It is purpose-built for feeding GPUs. Throughput scales linearly. Add nodes, get proportional performance. No cliffs. No metadata bottlenecks. A single namespace from petabytes to exabytes with deterministic hashing that points directly to the data.
How It Works: Drop-In Architecture
On the left, your data sources. Model repositories, vector databases, RAG corpora, and KV cache state all flow through your inference environment just like they do today. In the middle, your ML serving platform. In the middle, your ML serving platform, orchestrated by NVIDIA Dynamo across vLLM, SGLang, and NVIDIA NIM. It still handles inference and model management. On the right, AIStor. It sits behind your platform as the high-performance inference context tier. KV cache offload over RDMA. Shared persistent context across the entire cluster. Model and embedding storage.
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The integration is native. Dynamo orchestrates inference across SGLang, TensorRT-LLM, and vLLM, with KVBM managing KV cache placement and NIXL handling the data transfer. AIStor runs as a single binary. No maintenance windows, no disruption to inference workloads. The key business question becomes straightforward: how many storage nodes do I need to keep my GPUs fully utilized? With AIStor’s performance density, the answer is far fewer than the alternatives.
KV Cache Offload: The Core Inference Bottleneck, Solved
KV cache is the working memory of inference. At high concurrency, thousands of simultaneous users, it blows past GPU high-bandwidth memory. Without a fast offload tier, the system either recomputes from scratch or stops scaling. This is the single biggest infrastructure constraint in inference today.AIStor provides sub-millisecond KV cache retrieval at wire speed over RDMA. Evicted cache entries are offloaded to AIStor. When a request needs that context again, AIStor delivers it back to the GPU at near-local speed. No recomputation. No wasted GPU cycles. Sessions survive memory pressure and node failures because context is persistent and shared across the entire cluster, not trapped on a single server’s local NVMe.
Economics: Fewer Servers, Less Power, Lower Cost
AIStor delivers wire-speed throughput on commodity NVMe hardware. That means fewer servers to buy, power, cool, and manage. Organizations achieve up to 40% lower total cost of ownership compared to traditional inference storage architectures. The pricing model is straightforward: capacity-based, with no per-operation fees, egress charges, or per-seat licensing. Every feature is included in the subscription. Predictable costs at any scale.The economics compound at scale. GPU clusters cost millions. If those GPUs run at 30-50% utilization because storage can’t keep up, every idle GPU-hour is wasted capital expenditure. Getting GPU utilization from 40% to 90%+ doesn’t just improve throughput; it also reduces latency. It fundamentally changes the ROI calculation on GPU infrastructure. The storage investment pays for itself by unlocking the compute investment you’ve already made.
Why MinIO AIStor
AIStor is inference storage built differently. Native integration with leading inference orchestration frameworks enables automatic KV cache placement and high-speed data transfer across the most widely adopted open-source inference servers. A single binary with zero external dependencies and standard DPU-accelerated networking fabrics. Wire-rate throughput at up to 40% lower total cost of ownership compared to purpose-built storage appliances. Every feature is included in a single subscription with capacity-based pricing, no per-operation fees, and no egress charges. Support comes directly from the engineers who designed and built AIStor, available 24x7x365 through SUBNET with no tiered escalation. The architecture that works at petabytes works at exabytes. The storage layer is no longer the bottleneck.
