Storage Throughput for LLM Inference: Memory and KV Cache Sizing

When engineering infrastructure for Large Language Model (LLM) inference, architecture teams frequently focus exclusively on GPU VRAM capacity and compute flops, treating storage as an afterthought. This assumption frequently collapses in enterprise production. While active forward passes execute inside GPU high-bandwidth memory (HBM), storage throughput directly dictates model cold-start latencies, horizontal pod autoscaling responsiveness, multi-LoRA dynamic adapter switching speed, and secondary-tier KV cache paging. Without adequate storage I/O performance, high-cost GPU clusters sit starved of weights during scaling spikes and node failovers.

Defining the Storage I/O Lifecycle in Production LLM Inference

In LLM inference, storage throughput measures the sustained sequential and random read speed required to load multi-gigabyte model weights into GPU VRAM, swap dynamic LoRA adapters, and page inactive KV cache states.

In high-availability LLM serving environments, storage throughput measures the sustained sequential and random read bandwidth required to ingest multi-gigabyte weight tensors and dynamic context matrices into GPU memory. The inference storage lifecycle encompasses three distinct operational phases:

During the initial deployment or autoscaling of an inference replica, the serving engine must read the complete model artifact from storage into host system RAM, followed by DMA transfer over PCIe into GPU VRAM. For a 70B parameter model stored in FP16 precision (roughly 140 GB of binary checkpoint files), a standard cloud network volume sustaining only 250 MB/s requires over nine minutes simply to load the weights. In contrast, high-speed NVMe storage architectures delivering 14 GB/s complete the same loading cycle in under ten seconds.

Enterprise Relevance: When Storage Bottlenecks Degrade User SLAs

Slow storage turns pod autoscaling events into multi-minute outages, delays multi-tenant LoRA switching, and extends failover recovery time during hardware faults.

Storage throughput constraints directly manifest as availability and reliability risks in production enterprise architectures. Consider the following operational scenarios:

When multi-tenant platforms serve dozens of task-specific LoRA adapters over a single foundational model backbone, adapters must be dynamically fetched from disk when cache misses occur. Insufficient random-read IOPS and read throughput introduce unpredictable latency spikes (P99 jitter) into live user interactions, violating enterprise application SLAs.

Architectural Boundaries: Local NVMe vs Shared Cluster Storage

Local NVMe delivers raw bandwidth (>7 GB/s per drive) for fast weight caching, while distributed shared storage enables stateless autoscaling and centralized model registry access across the GPU fleet.

Architectural Decision Matrix: AI Storage Architectures for LLMs

Architecting an enterprise AI inference cluster requires a disciplined tiering boundary between local host storage and centralized distributed storage fabrics:

• Local Host NVMe Tier: Delivers maximum raw sequential bandwidth (exceeding 7,000 MB/s per PCIe Gen4 drive or 14,000 MB/s on PCIe Gen5). Local NVMe is optimal as an ephemeral read cache for model weights, frequently accessed LoRA adapters, and local serving engine scratch space. However, local storage is node-bound and cannot be shared directly across autoscaling worker pods.
• Shared Distributed Storage Fabric: Centralizes model registries, baseline checkpoints, and shared datasets across all GPU nodes in the cluster. Enterprise distributed storage systems utilizing parallel file systems or high-performance object storage with GPUDirect Storage (GDS) bypass host CPU buffers, transferring data directly from network interface cards to GPU memory over high-speed RoCE/InfiniBand fabrics.
• Tiered Caching Topology: Modern enterprise platforms deploy a hybrid topology: centralized model registries store source checkpoints, while intelligent startup daemons populate local node NVMe caches. Infrastructure architectures such as OneSource AI Storage Architecture combine distributed throughput with dedicated local NVMe fabrics to eliminate storage bottlenecks across inference fleets.

In production enterprise AI deployments, storage architectures must resolve the competing requirements of multi-terabyte model checkpointing and ultra-low-latency weight loading. The OneSource AI Storage Architecture resolves this through a disciplined two-tier topology: each dedicated bare-metal GPU node is provisioned with high-throughput local PCIe Gen5 NVMe scratch arrays delivering over 14 GB/s of direct read bandwidth for instant weight loading, dynamic multi-LoRA adapter switching, and secondary KV cache paging. This local tier is backed by an enterprise parallel distributed storage fabric operating over GPUDirect Storage (GDS) and RoCE, which streams checkpoints directly between storage controllers and GPU VRAM without CPU memory bounce-buffering, eliminating I/O starvation during sustained workloads.

FAQ

Does offloading the KV cache to NVMe storage hurt inference token latency?

Yes; paging inactive KV cache blocks to NVMe storage introduces microsecond-to-millisecond latency penalties compared to native GPU high-bandwidth memory (HBM). Disk offloading is primarily designed as a fail-safe mechanism to prevent out-of-memory (OOM) crashes during unexpected concurrency bursts rather than an active tier for latency-sensitive token generation.

What aggregate read bandwidth is needed for instant autoscaling of 70B LLMs?

To achieve cold-start model weight loading in under 10 seconds for a 70B parameter model in FP16 precision (approximately 140 GB of binary data), the underlying storage subsystem must deliver at least 14 GB/s of sustained aggregate read throughput directly into system and GPU memory.

How does OneSource AI Storage Architecture optimize LLM checkpointing and weight ingestion?

OneSource AI Storage Architecture pairs local PCIe Gen5 NVMe scratch storage on dedicated GPU nodes with a high-bandwidth distributed parallel storage fabric. By leveraging GPUDirect Storage (GDS) over RDMA networks, data transfers bypass host CPU memory bottlenecks entirely, allowing 70B+ parameter model weights to load in under ten seconds and preventing GPU compute cycles from stalling during heavy checkpoint write bursts.