Inference Optimization: Practical Techniques for Faster, Cost-Effective AI

Optimizing your AI infrastructure for inference isn’t just a smart business decision, it’s an essential one. That’s why so many organizations are now searching for the magic button to make it happen. The only problem? There isn’t one.

Instead, inference optimization involves understanding how multiple components across an entire system function, support each other, and interact. Rather than one single strategy, organizations must embrace a range of inference optimization techniques for everything from GPU memory and compute to storage, networking, and architecture. And they must do so continuously as their infrastructure adapts and scales with the demands of AI.

So while there may be no magic button, the following is a practical framework for optimizing inference – from quick wins to long-term architectural changes – that you can start implementing today.

The Economics of AI Inference (Why Optimization Matters)

How did AI inference optimization become such a central piece of the modern AI puzzle? Answering this begins with understanding a 160-year-old concept called Jevons paradox.

Originally used to describe how the efficiency of coal-powered technology affected coal consumption, Jevons paradox is now a broader idea that describes how the consumption of a resource increases when technological efficiencies lower its cost of use. In terms of AI, this cost is measured per token. Therefore, according to Jevons paradox, the cheaper each token becomes, the greater the demand for AI will be – which happens to be exactly what is happening.

In 2021, the cost for a million tokens was roughly $60 on GPT-3. Three years later, that cost had fallen to just 6 cents. Since then, driven by hardware improvements and model efficiency gains, the cost per token has continued to decline by 10x annually, a rate that exceeds even the rapid price drops of other technologies like PCs. At the same time, demand for AI has exploded. Once a niche tool, AI is now used regularly by 88% organizations, while the industry itself is projected to expand to nearly $5 trillion by 2033.

In order to keep up with this demand, the real costs can now be found in infrastructure. With more AI users than ever, as well as more complex models and agentic workloads, enterprise AI inference spending has increased by 320% over the past two years – and it shows no sign of slowing down. According to Gartner, inference spending will represent 55% of AI-optimized IaaS in 2026 and more than 65% by 2029.

The implications for organizations are clear: Align technical and business goals now and invest in getting the cost of inference down so that you can continue to support AI demands tomorrow.

Quick Wins — Inference Optimizations You can Deploy Now

Delivering sustainable, real-time inference over the long-term may involve making systemic changes to your entire architecture, but that doesn’t mean there aren’t more immediate solutions that can help along the way. After all, the demands that make fast inference so essential aren’t waiting, so why should you?

The following LLM inference optimization techniques can help you start delivering a better user experience:

KV Cache Quantization

This technique reduces the memory load that key-value (KV) cache takes up by transforming it into a less precise format. By doing so, it frees up capacity for the AI model to process a greater amount of requests, handle additional complexity, or both.

The precision of the KV cache can be measured in bits. Typically, AI models store KV cache with either 32 or 16 bits of precision (also referred to as 32 or 16 floating points, or FP). However, most models can still perform well with minimal quality loss when that precision is reduced to 8 bits, or FP8. Quantization achieves this by either rounding or shortening the numerical quantities that make up the keys and values of the KV cache. This effectively cuts its memory footprint in half. Each cached value occupies 8 bits instead of 16, so the math is straightforward — half the bits, half the memory per entry. A smaller footprint means less data transfers from HBM into the GPU’s compute cores on every decode step, easing the memory-bandwidth bottleneck that limits token generation speed.

Prefix Caching

This is another technique for optimizing the KV cache. In this case, it takes advantage of the fact that many prompts are able to use identical prefixes when they are processed. By doing so, it’s possible to achieve cache hit rates of 87% or more. High cache hit rates mean the system reuses pre-computed KV states instead of regenerating them from scratch, eliminating up to 70% of redundant prefill compute and increasing concurrency. 

What is a KV cache prefix? This refers to any computations shared between different prompts. Examples might include:

• Prompt reuse: If a user or multiple users set a rule, such as “You are a critical reviewer,” then the model would process this request the first time, then reuse it as a cached prefix for subsequent prompts while only processing the unique question or request that follows.
• Long document analysis: When referencing a large document multiple times with different questions, the entire file is processed once and cached as a prefix. Afterwards, only specific questions need to be processed for each request.
• Multi-round conversations: In conversational scenarios with LLMs, the prefix refers to the chat history. When a user adds a new message, the model reuses the cache of the entire previous history instead of recomputing it from the beginning.

Continuous Batching

Batching is a strategy employed by AI models to help them process multiple requests simultaneously. Rather than take up the GPU’s entire memory bandwidth loading model parameters for each request, batching groups these requests together so that the model parameters can be used across many different requests.

While batching itself can help improve throughput, a significant limitation is that a new batch cannot begin until every request in the current batch is complete. When one of these requests is significantly slower than the others, it can result in underutilized GPU resources.

Continuous batching works around this inefficiency by instead letting requests finish independently and immediately beginning a new request inside that batch. In this way, it functions very similarly to an assembly line: by constantly ensuring new requests are replacing finished ones, GPU utilization stays maximized and idle time is eliminated. This optimization strategy has become common across many different inference engines, with some demonstrating improvements of as much as 23x in throughput.

Right-Size Your Serving Framework

Model serving frameworks are how AI models receive requests, process them, and send back the results. If the AI model itself is like an engine, the serving framework functions like the rest of the car. And just as there are different models of cars, so are there different types of serving frameworks. Choosing the right framework can help optimize for real-time inference by matching its features to your model’s most common types of workloads. Consider the following:

• vLLM: This framework offers dynamic KV cache management, making it efficient when juggling multiple models or variable requests lengths. It also comes with broad hardware support and a huge community, which means there’s a lot of operational tooling around it. 
• TensorRT-LLM: This is NVIDIA’s own open-source inference engine. Features like quantization and kernel fusion (collapsing multiple GPU operations into one) make it capable of delivering very low time-to-first-token (TTFT). However, it may not perform as well for multi-model setups.
• SGLang: The key innovation of this framework is RadixAttention, a memory management feature that allows it to automatically cache and reuse KV states across different requests. This helps this framework perform significantly better at multi-turn conversations and shared-context workloads.

Watch: A Blueprint for Supercharging LLM Inference with PagedAttention over RDMA

Architectural Optimization — Infrastructure That Compounds

Traditional data centers aren’t built for the competing demands of prefill and decode. Because of this, when it comes to creating sustainable, real-time inference over the long-term, it’s become economically necessary to update your infrastructure. The best infrastructure for scalable AI inference goes beyond quick fixes.The best infrastructure for scalable AI inference goes beyond quick fixes. 

The following are some inference optimization techniques you can employ at the infrastructure level.

KV Cache Offloading

This refers to the process of moving (or offloading) specific KV cache data from high-speed GPU and DRAM memory tiers to lower-speed storage tiers like NVMe and local SSDs. By freeing up valuable memory for more active data without making the model have to recompute previous cache states, you can address the KV cache bottleneck while promoting inference.

This strategy takes advantage of the fact that not all cache data needs to stay on GPU memory. For example, when a user doesn’t interact with the model continuously, the cache data produced during their session becomes inactive. However, in traditional configurations, this cache data will remain in place even as more active sessions produce their own cache data. With limited GPU memory available, the result is reduced throughput and wasted resources.

KV cache offloading solves this problem by rearchitecting how cache data is stored. Consider how NVIDIA Dynamo utilizes this technique. When KV cache exceeds available memory, a memory management layer offloads cache data from the GPU memory to slower tiers according to predetermined eviction strategies, such as relevance or idle time. If this cache data is needed again, it is reloaded into GPU memory as needed in order to avoid costly recomputation.

In tests, NVIDIA used this strategy to accelerate TTFT by 14x to 28x, proving this as a reliable technique for maintaining inference without the need to add GPUs.

Disaggregated Serving

Disaggregation separates the two phases of inference, prefill and decode, into independent GPU pools. In doing so, it prevents the competing demands of these phases from interfering with each other and slowing the other down.

In traditional infrastructure, the compute-heavy tasks of prefill and the memory-bound work of decode will run together, which means they’re often competing for the same resources. Because only one phase can run at a time, the GPU must complete a prefill task before it can begin a decode task. When there are multiple requests arriving at once from concurrent users, the result is a prefill bottleneck: much lower throughput, poor resource utilization, and slower inference.

Disaggregated serving directly addresses this by allocating dedicated GPU resources to both the prefill and decode phases. By optimizing each of these pools according to their specific goals (for example, lower TTFT or more efficient concurrency), you can increase the performance across both phases without affecting the availability of resources. Disaggregated serving systems like DistServe have successfully used this technique to deliver 7.4x more requests compared with similar architectures.

Memory Extension

Related to KV cache offloading but even broader in scope, memory extension (also called software-defined GPU memory) expands available GPU memory beyond the limited range of HBM and into much larger capacity NVME – but without affecting performance.

Memory extension works by using a software layer to transform storage-class tiers into a high-bandwidth repository for KV cache data, model weights, activations, optimizer states, and anything else needed for inference. Through this layer, data bypasses the north-south traffic paths traditionally used for storage access (which are limited to around 400GB of bandwidth) and instead utilizes the high-speed east-west traffic, which is the same fabric used for GPU-to-GPU communication. With up to 3,200GB of bandwidth available along this route, as well as the ability to bypass the CPU and OS stack entirely, data can move in and out of HBM and NVMe with microsecond latency.

This ability to move data between the GPU and storage tiers at memory-class speeds effectively expands KV cache capacity by as much as 1000x more than HBM alone, dramatically increasing context lengths and the system’s capacity for memory-bound workloads.

Watch: Breaking Down the Memory Wall in AI Infrastructure

Elastic Infrastructure

This strategy dynamically matches computing resources to fluctuating demand. By scaling GPUs in and out this way, elastic infrastructure helps maximize both performance and resource utilization, while also reducing latency and overall costs.

In an ideal world, available compute and memory would match up perfectly with the requests coming in from users. In reality, however, user behavior varies widely. Idle periods can suddenly be overrun with user demands and multi-step conversations, stressing resources and slowing throughput. Likewise, periods of intense activity can drop off just as quickly into stagnation, leaving GPUs underutilized.

Elastic infrastructure addresses this challenge by using the resources typically allocated for training. Instead of running separate GPU pools for training and inference, an elastic infrastructure runs both workloads on the same cluster, then shifts resources between the two based on demand. As a result, it’s possible to allocate twice the resources to inference during peak periods, such as daytime hours, then shift the majority of those resources entirely to training during slower periods, such as the night. This level of flexibility helps maximize resource utilization and run inference more efficiently.

Storage Optimization — The Overlooked Lever

Storage may be the most underinvested layer in inference infrastructure. While many organizations continue to prioritize GPUs, storage is treated like a passive layer. One reason for this is that storage is not typically seen as a critical dependency to overall performance. But when it comes to AI inference at scale, storage may be just as important as GPUs.

Although inference is often thought to be GPU-bound, it’s actually I/O-bound. As fast and efficient as a GPU is, if data cannot be delivered to it in time, that GPU will sit idle. In fact, research shows that most organizations achieve less than 30% GPU utilization, a failure that can cost hundreds of thousands of dollars an hour when measured at scale. 

Because of this, optimizing storage is a vital step for achieving real-time inference. The following are a few strategies to help you do this.

GPUDirect Storage

Developed by NVIDIA and WEKA, GPUDirect Storage (GDS) is a technology that establishes a direct path between NVMe storage and GPU memory. This puts into practice the technique of memory extension by bypassing the CPU altogether and turning NVMe into a high-speed memory tier. 

What does this achieve? With GDS in place, 40+ GB/s transfer speeds from NVMe to GPU memory are possible, with some configurations reporting even higher performance. GDS can also relieve bandwidth bottlenecks stemming from CPU bounce buffers and reduce dependence on CPUs to process storage data transfer.

Context Memory Storage

As inference workloads scale — longer contexts, multi-turn conversations, agentic workflows — KV cache demands quickly outgrow what GPU HBM can hold on its own. Context memory storage addresses this by creating a shared, pod-level memory tier purpose-built for ephemeral KV cache.

NVIDIA’s CMX™ platform, powered by the BlueField®-4 storage processor, is one implementation of this approach. It extends GPU memory with a high-bandwidth context tier that reduces the latency, cost, and power overhead of shuttling KV cache data between GPUs and storage. Leveraging the more performant and efficient NVIDIA Rubin platform, CMX reports 5x higher throughput and 5x better power efficiency compared to traditional storage paths. For organizations running large-scale inference, context memory storage becomes an actively optimized performance layer.

Parallel File System vs. NAS

Network Attached Storage (NAS) and parallel file systems (PFS) are two different architectural approaches to storage infrastructure. NAS is the older of the two and utilizes a centralized server to store and share files. While this has plenty of advantages for certain applications, this design is not well-suited for AI inference. This is because NAS runs both prefill and decode phases through the same storage infrastructure, meaning all metadata is forced to share the same I/O path. With many requests coming through, bottlenecks occur that kill inference at scale.

PFS takes a different approach. Rather than use a single centralized server, it divides data and metadata into different nodes. Doing this allows metadata to be cached in memory, where it can be retrieved using very fast parallel lookups. It also means nodes can scale independently from each other, delivering better performance. And with multiple nodes working separately but in parallel, prefill and decode requests won’t get stuck behind each other in a queue.

Intelligent Tiering

This is an automated storage approach that divides storage types into tiers based on latency and moves data according to its bandwidth needs. In this tiered storage hierarchy, HBM and DRAM are reserved for active and critical inference data, while slower types like NVMe and network storage are used for cold and archived data. This prevents inactive data from taking up limited low-latency memory, although the ability to move data up or down as needed still keeps it accessible.

Measuring Optimization Impact

When do you know your optimization efforts have been successful? Without a clear set of metrics, you are flying blind. But here’s the catch: most metrics lie when measured in isolation. A TTFT number at idle tells you nothing about what happens at 200 concurrent users. A TPS figure at short context masks degradation at production depths. The metrics that matter are the ones measured under real load, at real scale.

As you test different techniques, apply various strategies to scale token usage, and rearchitect your infrastructure, use the following key metrics to score your success:

• Time to first token (TTFT): How long it takes for the model to generate its first token. Target 200ms or lower — but measure it across a sweep of concurrent session counts. When KV cache is available, TTFT stays under a second. When cache eviction forces a full recompute, it can spike to 11 seconds. The shape of that degradation curve matters more than any single-point number.
• Tokens per second (TPS): How quickly a model generates output tokens. 20+ TPS feels like real-time typing. But qualify it against context depth — as multi-turn sessions grow into hundreds of thousands of tokens, decode-phase memory pressure intensifies and throughput can drop sharply. A TPS number without context qualification is a best-case snapshot, not a production metric.
• KV cache hit rate: The percentage of tokens served from cache rather than recomputed. This is the causal driver of TTFT, throughput, and cost. But not all hits are equal — a hit from GPU HBM takes sub-milliseconds, while a hit from NVMe takes tens of milliseconds. Both register as “hits” in a flat metric. Track hit rate by memory tier to see what your system is actually doing under load.
• P99 latency: The worst-case decode performance your model delivers. At scale, the bottom 1% translates to thousands of users — and in agentic workflows, a single tail-latency spike can stall an entire agent loop.
• Cost per million tokens: The total cost to deliver tokens at scale. As workloads shift toward multi-turn agentic sessions, the emerging standard is session TCO — the fully loaded cost to complete an entire task end-to-end, including recompute penalties from cache misses. Memory architecture alone can drive session cost differences of 36% or more.

These metrics interact as a system. Cache hit rate drives TTFT. TTFT under concurrency determines how many sessions you can serve. Concurrency determines how many GPU nodes you need. And that determines cost. Optimizing one at the expense of the others gives you a false picture.

Build your AI infrastructure without bottlenecks. Download the Buyer’s Guide to AI Storage for a full evaluation framework.

FAQs

What is inference optimization? Inference optimization is the process of improving the speed, cost, and efficiency of AI model inference — the phase where a trained model generates predictions or responses. It spans multiple layers including GPU memory, KV cache management, storage architecture, and serving frameworks, and aims to reduce latency while maximizing throughput and resource utilization.

What are the most effective LLM inference optimization techniques? The most effective LLM inference optimization techniques include KV cache quantization (halving memory footprint with FP8 precision), prefix caching (reusing shared prompt computations for up to 87% cache hit rates), continuous batching (up to 23x throughput improvement), and right-sizing your serving framework to match your workload profile.

How do you reduce the cost of AI inference? Reducing AI inference cost requires a systems approach: optimize KV cache to free GPU memory, use disaggregated serving to improve resource utilization, implement memory extension to avoid buying more GPUs, adopt elastic infrastructure to share resources between training and inference, and upgrade to parallel file systems that keep GPUs fed.

What are AI inference infrastructure best practices? AI inference infrastructure best practices include deploying parallel file systems instead of NAS, enabling GPUDirect Storage for zero-copy data movement, implementing intelligent storage tiering, extending GPU memory through software-defined memory layers, and continuously benchmarking with real workloads rather than synthetic tests.

What is the best infrastructure for scalable AI inference? The best infrastructure for scalable AI inference combines a parallel file system architecture with GPUDirect Storage support, software-defined memory extension beyond GPU HBM, multi-tenant isolation, and deployment flexibility across on-prem, cloud, and hybrid environments. It must handle the competing I/O demands of both prefill and decode phases simultaneously.

What is KV cache quantization? KV cache quantization reduces the precision of key-value cache data from 16-bit (FP16) to 8-bit (FP8) floating point, cutting memory footprint roughly in half with minimal quality loss. This frees GPU memory for more concurrent requests, longer context windows, or more complex models — making it one of the fastest inference optimizations to deploy.

What is disaggregated serving in AI inference? Disaggregated serving separates the two phases of inference — prefill (compute-bound) and decode (memory-bound) — into dedicated GPU pools. This prevents the phases from competing for resources, enabling up to 7.4x more requests compared with traditional architectures. It’s now in production at major AI companies via frameworks like NVIDIA Dynamo.

How do you measure AI inference performance? Measure AI inference performance across five key metrics: time-to-first-token (TTFT) for responsiveness, tokens per second (TPS) for decode throughput, P99 tail latency for worst-case user experience, cost per million tokens for economics, and GPU utilization percentage for resource efficiency. Optimizing one at the expense of others creates false savings.