How AI Inference Actually Works: Prefill, Decode, and Infrastructure

What makes for effective AI infrastructure? Although organizations have previously designed for the more compute-intensive training phase, 2026 marks the first time more money is spent on AI inference infrastructure. But doing this successfully and at scale means bridging a significant design gap.

While training is a batch job with predictable and finite needs, inference is a live service and the Key Value (KV) cache that accumulates during every request is what makes it so demanding on memory. Inference is divided into two distinct phases. The first, prefill, is compute hungry: it processes the entire prompt and generates the initial KV cache. The second, decode, is memory hungry: it reads from that KV cache with every token it generates. Optimizing for just one will often create bottlenecks for the other.

Designing the best infrastructure for scalable AI inference, despite unpredictable loads and resource needs, is the core challenge we’ll explore today.

The Shift from Training to AI Inference (and Why It Matters Now)

Until recently, the majority of AI spending focused on the training phase, the compute-intensive process of feeding the AI data so that it can learn patterns, build relationships, and create its model. However, global spending on infrastructure supporting inference is expected to surpass spending on training for the first time in 2026 – a trend that shows no signs of stopping. In fact, by 2029, Gartner predicts that spending on inference will be nearly twice that of training ($72 billion vs $37 billion). 

What accounts for this shift? The primary reason is the manner in which these two stages work. Training is a batch job, meaning that it takes place all at once. While it may be computationally intensive at the time, often requiring powerful hardware accelerators and lasting anywhere from several hours to several weeks (depending on the size and complexity of the model), it is nevertheless finite.

In contrast, inference is a live, ongoing service. It runs whenever a user makes a request, which can make it much more unpredictable and, as concurrent requests are made, costly. Because of this, AI inference at scale requires low latency, continuous uptime, and consistent performance under variable loads. Any delays or system failures can quickly break the product experience and negatively impact users.

This is why inference spending is on the rise. In order to keep inference running smoothly and overcome challenges like the memory wall, the infrastructure supporting it must be able to handle both the compute-heavy prefill phase and the memory-hungry decode phase – a task that legacy storage was never built to meet.

Prefill and Decode — Two Phases, Two Problems

Understanding AI inference really means understanding the different needs and challenges of its two phases: prefill and decode.

Prefill Phase

Prefill is all the work that happens behind the scenes as the AI processes the input prompt. After converting the input into tokens, the AI uses the model it built during the training stage to analyze these tokens and begin computing the information necessary to generate the output. This phase typically is very GPU-intensive and requires massive parallel throughput to run successfully.

Decode Phase

Once prefill is complete, the decode phase begins. This is when the model uses the information stored during the prefill phase to generate output tokens. Since each token is based on the previous token, this phase is often slower than the prefill phase – and less compute-intensive. However, it is also visible to the end user, which means it needs ultra-low latency to feel seamless. That makes it the most memory-intensive phase of inference.

The Prefill/Decode Problem

Because these two phases have different needs – prefill requires intensive compute, decode requires expansive memory – it poses an acute infrastructure problem. Optimize for the compute-heavy prefill phase and your AI will struggle with high latencies as it generates outputs. But optimize for the memory-hungry decode phase and your AI may take a long time as it processes the input.

The Role of KV Cache in AI Inference

Key-Value (KV) cache is the short-term memory of the AI model during decode. It both enables inference and can act as one of its most significant bottlenecks.

Its name comes from the keys and values that make up memory. As the model processes the prompt during prefill, it stores these calculations in its KV cache. During the decode phase, this enables the model to easily retrieve previous information so that it can generate output tokens without having to dedicate additional compute to recalculations. As long as there is sufficient memory to store this cache, it provides a consistent context window for seamless inference.

But maintaining this sufficient memory is where the bottleneck can occur. KV cache can use up a significant amount of memory very quickly. When this happens, the previous cache is moved out of its high-speed memory storage to higher capacity but slower memory tiers. As a result, this hampers the AI model’s ability to quickly retrieve previous calculations, requiring more time and compute power as redundant prefill phases begin once again.

This is the “memory wall.” Even when there are sufficient compute resources for the prefill phase, inference will suffer if there is not also sufficient memory to maintain the context window during decode.

What AI Demands from Inference Infrastructure

Consistent, high-speed inference is the ideal state of AI. This is when the model is capable of processing inputs and generating high-quality outputs in real-time and without delays. However, in order for this to happen, the right infrastructure needs to be in place. Key capabilities for your inference engine should include:

• Microsecond latency: Although millisecond latency was once acceptable, real-time decision-making and modern user expectations now demand inference that is measured in microseconds. Anything less will result in poor user experience.
• High IOPS: There are many simultaneous and often unpredictable input/output behaviors during AI inference, such as large sequential reads during prefill and small random reads during decode. The ability to handle these tasks seamlessly is essential.
• Memory hierarchy: A clearly defined, tiered memory structure will help maximize data throughput, increase your context memory, and maintain consistent inference. A typical hierarchy starts with HBM for critical KVs, then moves to DRAM for staging and buffering, then NVMe for KV that does not require longer timescales, and then finally local network storage.
• Multi-tenant isolation: As security and privacy concerns over AI grow, it is important to build safeguards directly into the infrastructure. Multi-tenant isolation is an architectural approach that spans the AI stack and ensures tenant namespaces remain separate and secure. 

Traditionally defined “fast storage,” such as standard NVMe SSDs, is not the same as “inference-optimized storage.” Whereas fast storage emphasizes high sequential throughput and is ideal for moving large volumes of data all at once (such as during the training stage), inference-optimized storage prioritizes ultra-low latency and high IOPS. This makes it ideal for inference because it helps the AI handle many unpredictable and concurrent tasks.

What to Look for in AI Inference Infrastructure

So what should purpose-built inference infrastructure actually include? The specifics depend on your workload, but these capabilities matter across the board.

A parallel file system, not legacy NAS

Legacy Network Attached Storage (NAS) wasn’t designed for inference. It creates hotspots, metadata bottlenecks, and contention points that compound as concurrent requests scale. A parallel file system distributes both data and metadata across the system, eliminating those chokepoints and scaling linearly as demand grows. It’s important to balance I/O across infrastructure with no single point of contention.

GPUDirect Storage to keep data moving at memory speed

Every time data passes through the CPU on its way to the GPU, it adds latency. GPUDirect Storage eliminates that detour by creating a direct data path between storage and GPU memory. The result: microsecond-class latency, higher throughput, and GPUs that spend their cycles on inference instead of waiting for data. For workloads that live and die by time-to-first-token, this is non-negotiable.

Software-defined memory extension to break the memory wall

HBM is finite, expensive, and sold out. When KV cache exceeds what’s available, the model evicts cached data and burns GPU cycles re-prefilling tokens it already processed. Software-defined memory extension solves this architecturally — not by adding more GPUs, but by creating a persistent memory tier beneath HBM. Look for solutions that use NVMe and GPUDirect Storage to extend effective KV cache capacity and sustaining 75–99% cache hit rates and eliminate redundant prefill operations. 

Multi-tenant isolation that eliminates noisy neighbors

Inference at scale means serving hundreds or thousands of concurrent users and workloads on shared infrastructure. Without proper isolation, one heavy workload degrades performance for everyone else — the “noisy neighbor” problem. Your inference infrastructure should deliver per-tenant quality of service guarantees, ensuring predictable latency and throughput regardless of what else is running on the same system.

Deployment flexibility from bare metal to multicloud

AI infrastructure needs to be adaptive. A system locked to a single environment — whether on prem or a specific cloud — becomes a constraint the moment your strategy shifts. Look for infrastructure that runs the same binary everywhere, with 100% feature parity and zero reconfiguration between bare metal, public cloud, and hybrid deployments. That flexibility lets you scale inference where economics make sense today and move when they don’t tomorrow.

Want to learn how AI storage impacts inference workloads? Download the Buyer’s Guide to AI Storage for a full evaluation framework.

FAQ

What is AI inference?

AI inference is the process of running a trained model to generate responses based on new inputs. Unlike training — which happens once to build the model — inference runs continuously every time a user makes a request. It consists of two phases: the compute-intensive prefill phase, which processes the input prompt, and the memory-intensive decode phase, which generates the output token by token.

What is the difference between the prefill and decode phases?

Prefill is the first phase of inference, where the model processes the user’s input prompt and performs the heavy GPU computation needed to understand it. Decode is the second phase, where the model generates the output one token at a time, referencing earlier calculations stored in the KV cache. Prefill is compute-bound; decode is memory-bound. This difference is why inference infrastructure needs to balance both resources rather than optimizing for just one.

What is a KV cache and why does it matter?

KV cache (Key-Value cache) is the short-term memory an AI model uses during inference. During the prefill phase, the model stores key-value pairs from its attention calculations in the cache. During decode, it retrieves these instead of recomputing them, which keeps latency low. When the cache fills up and spills to slower memory tiers, performance degrades — a phenomenon known as the memory wall. Sufficient, fast KV cache storage is essential for consistent, low-latency inference.

Why is inference spending surpassing training spending in 2026?

Training is a finite batch job — computationally intensive, but it runs once. Inference is a live, ongoing service that runs every time any user interacts with an AI model. As AI adoption scales to millions of concurrent users, the continuous cost of serving those requests exceeds the one-time cost of training the model. Gartner projects that by 2029, inference infrastructure spending will reach nearly twice that of training.