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Accelerating AI Inference: The Unseen Power of Smarter Storage

The screen flickered, a cascade of numbers and code, as Dr. Anya Sharma watched her latest large language model (LLM) attempt to respond to a complex query.

The NVIDIA H100 GPUs hummed, hot and powerful, churning through calculations.

Yet, the time-to-first-token (TTFT) felt sluggish.

Eighteen, nineteen seconds, sometimes more, before the first word appeared.

It was frustrating, like having a Formula 1 car stuck in traffic.

Her team had invested heavily in cutting-edge GPUs, believing they were the sole arbiters of AI performance.

But this lag hinted at a deeper, unseen bottleneck.

Anya knew the computational grunt was there, but the flow of data, the way the LLM accessed and reused its own vast memory, was clearly hindering its speed.

This was not just an inconvenience; it was a fundamental challenge to deploying truly interactive, cost-effective AI.

The promise of instant AI, the kind that could transform customer service, generate code on demand, or distill complex research in seconds, felt tantalizingly out of reach.

There had to be a better way to unleash the full potential of her GPUs, a way that went beyond simply adding more horsepower.

In short: IBM Storage Scale maximizes GPU utilization and accelerates AI inference by offloading large KV caches from GPUs to high-performance storage, delivering significant speedups and reducing re-computation costs for large language models at scale.

Why LLM Inference Demands More Than Just GPUs

When we envision the powerhouse behind AI tasks, GPUs naturally seize the spotlight.

They are, undeniably, the main infrastructure components for processing complex computations.

However, they represent just one piece of a sophisticated puzzle.

Without the right network and storage infrastructure, todays AI applications, particularly for large language model (LLM) inference, would be agonizingly slow and prohibitively expensive.

This principle has long been established for AI training, and it holds equally true for AI inference (IBM).

LLM-based inference is not merely another workload; it is a dominant and incredibly resource-intensive demand on all three infrastructure pillars: compute, network, and storage.

The sheer volume and velocity of data involved have spurred the development of entirely new software capabilities, such as llm-d, vLLM, and Dynamo, specifically designed to optimize resource management for inference (IBM).

Without efficient management and reuse of pre-computed inference artifacts, GPUs are unnecessarily overloaded with redundant calculations, resulting in prolonged wait times for LLM responses to each query (IBM).

The KV Cache Problem: A Hidden Bottleneck to AI Speed

To understand why LLM inference is so resource-intensive, we need to look under the hood.

Modern LLMs are primarily built upon the transformer architecture, which utilizes a self-attention mechanism.

During inference, this architecture generates vast quantities of intermediate runtime data, specifically in the form of large key (K) and value (V) tensors.

These K and V tensors are kept in GPU memory as a cache for each ordered set of input tokens, as long as space permits.

Their purpose is to be reused by the model to generate subsequent output tokens, saving significant time by avoiding re-computation and accelerating token generation (IBM).

However, GPU memory is finite.

As new inputs or requests flood in, this memory quickly fills, forcing older, potentially valuable K and V tensors to be discarded to make room for new ones.

This limitation severely restricts the ability to reuse previously computed KV data, impacting overall inference efficiency (IBM).

To illustrate the scale of this problem: experiments show that for a Llama3-70B model processing 128K input tokens, a common context window size, the KV cache alone can consume about 40GB.

Without efficient management beyond GPU memory, the time-to-first-token (TTFT) can be as high as 19 seconds when using four NVIDIA H100 GPUs (IBM).

Storing all this data solely within GPU memory is simply not sustainable.

While solutions like CPU RAM caching offer some relief, even CPU RAM can quickly become saturated.

For global optimization across a fleet of inference servers, the ability to persist data and share KV values across multiple inference instances becomes invaluable (IBM).

IBM Storage Scale: Unlocking AI Inference Acceleration

The clear solution to this pressing challenge lies in a high-performance storage tier—one that combines high bandwidth and low latency with cache and locality awareness.

This is precisely where IBM Storage Scale excels, offering a persistent space ranging from a few terabytes to hundreds of petabytes for KV data, capable of supporting a virtually unlimited number of active users and agent sessions (IBM).

Adding an IBM Storage Scale tier to an AI inference solution fundamentally transforms the architecture.

It enables widespread KV cache sharing across hundreds or even thousands of GPU servers.

This means that KV caches generated on one GPU can be immediately utilized by any other GPU in the entire cluster, drastically reducing redundant calculations (IBM).

Beyond sheer capacity and sharing, IBM Storage Scale delivers predictable low latency and high throughput—critical requirements for many interactive inference use cases, ensuring a fluid user experience.

Furthermore, it provides robust enterprise functionalities, including tiering, quota management, failure handling, scaling, and access control, all readily available for this new, vital type of AI data.

These capabilities, many honed through the stringent performance demands of traditional High-Performance Computing (HPC) deployments, are now available for state-of-the-art AI inference (IBM).

Seamless software integration with frameworks like vLLM and llm-d allows users to deploy these benefits out of the box, optimizing AI inference cost and performance at scale in production (IBM).

Real-World Impact: Performance Gains and Operational Benefits

To demonstrate the power of IBM Storage Scale as an AI inference accelerator, IBM conducted an experiment using Llama3-70B, served by vLLM, on four NVIDIA H100 GPUs.

The setup involved offloading the KV cache to either CPU DRAM or IBM Storage Scale.

The results clearly illustrated the intuitive finding that caching to CPU DRAM is significantly faster than re-computing KV data when not stored in GPU memory, providing a 23.6x speedup for 128K context input sizes (IBM).

However, the true excitement emerges with the integration of IBM Storage Scale.

When utilized as a KV cache tier, it delivers all the benefits described above, including distributed sharing across an entire fleet of vLLM inference instances.

Critically, it achieves an 8-12x speedup in time-to-first-token relative to recomputing.

This performance approaches that of CPU RAM caching, clocking in at 1.6 seconds TTFT compared to 0.8 seconds for CPU RAM (IBM).

While CPU RAM remains slightly faster for immediate node-local access, IBM Storage Scale offers the crucial advantage of virtually infinite capacity and distributed sharing, which CPU RAM cannot.

Meeting stringent latency expectations—a TTFT of 2 seconds or less—becomes achievable, rather than facing the prohibitive 19 seconds of an unoptimized setup (IBM).

This allows GPUs to be freed up for higher-value operations, like token generation, instead of wasting cycles on KV cache prefill.

Moreover, this solution integrates natively with familiar inference services, such as llm-d, delivering these substantial gains while minimizing software complexity for the service operator (IBM).

This directly addresses the critical need for AI infrastructure that maximizes GPU utilization and delivers efficient LLM inference.

The Future of Distributed Inference and High-Performance Storage

The demands of AI inference with KVCache offload introduce a new set of requirements for storage systems.

They critically need both high bandwidth and low-latency access—a challenging combination for most conventional storage solutions.

IBM Storage Scale rises to this challenge with a unique blend of performance, scalability, versatility, and enterprise-readiness (IBM).

Its impressive capabilities include a bandwidth of 300 GB/s, 13 Million IOPS with sub-microsecond latency per building block, and scalability to over 100,000 nodes (IBM).

This robust architecture is versatile enough to accelerate not only AI training and inference but also HPC, analytics, and databases, offering a value proposition that is hard to match.

The journey of high-performance storage in accelerating distributed inference is only just beginning.

Opportunities exist to deliver even greater value by combining this work with content-aware storage solutions, with the potential to shorten time to insight for enterprise data from hours and days down to mere seconds (IBM).

This evolution underscores the pivotal role of robust data management for AI in shaping the future of interactive, real-time artificial intelligence.

Conclusion: Smarter AI, Faster Insights

Dr. Anya Sharma’s initial frustration with her LLMs sluggish responses, despite powerful GPUs, is a common experience in the rapidly evolving world of AI.

The core lesson is clear: in the pursuit of accelerated AI inference, storage is no longer an afterthought; it is a central pillar of performance.

By effectively managing the massive intermediate data, the KV cache, that LLMs generate, solutions like IBM Storage Scale unlock the full potential of GPUs, transforming slow, expensive interactions into near-instantaneous dialogues.

This shift to high-performance storage for LLMs not only delivers tangible speedups but also makes AI truly scalable and cost-efficient for production environments.

The future of AI is not just about smarter models, but about smarter infrastructure that allows those models to perform at their peak.

For anyone building or deploying AI, understanding this interplay between compute and storage is not just an advantage; it is a necessity for bringing truly intelligent applications to life.

Glossary of Key Terms:

• AI Inference: The process of taking a trained AI model and using it to make predictions or generate outputs based on new, unseen data.
• GPU Utilization: The percentage of time a Graphics Processing Unit (GPU) is actively working on computational tasks, ideally maximized for efficiency.
• KV Cache Offload: The technique of moving Key (K) and Value (V) tensors (intermediate data generated by LLMs) from limited GPU memory to a more capacious storage tier to free up GPU resources and enable reuse.
• Large Language Model (LLM): An AI model trained on vast amounts of text data, capable of understanding, generating, and responding to human language.
• Time-to-First-Token (TTFT): A critical metric in LLM inference measuring the latency from when a user query is sent to when the first token of the response is generated.
• IBM Storage Scale: A high-performance, scalable file system designed to manage large volumes of data with high bandwidth and low latency, ideal for AI workloads.
• HPC Storage: High-Performance Computing storage, characterized by extremely high data transfer rates and low latency, typically used for scientific and complex computational tasks.
• Distributed Inference: Running AI inference across multiple compute nodes or servers to handle large workloads or ensure high availability.

References

• IBM. Accelerating AI inference with IBM Storage Scale.

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