Improving Inference Latency: Guide and best practices

Inference latency is the single most visible performance metric in modern AI systems. It is the delay between when a user or system sends a request to an AI model and when a useful response (or the first token, in streaming) is returned, and it directly shapes user experience, cost, and scalability. When inference latency is high or unpredictable, applications feel sluggish, resource bills climb, and scaling becomes harder. Getting inference latency right matters for any team running AI inference in production.

This guide explains what inference latency is, how to measure it, and how to improve it across model, hardware, and infrastructure. It is written for platform engineers, ML engineers, and DevOps teams who run or plan to run AI inference in production. We focus on practices that work at scale on Kubernetes, where orchestration and platform choices often matter as much as model-level tuning. You will leave with a clear set of metrics to track, seven actionable best practices, and a view of how the right AI infrastructure can minimize latency and cost together.

Key highlights

• Inference latency is the time from request to response (or first token). It is distinct from throughput, which measures work completed per unit time; both matter, but latency drives perceived responsiveness.

• Optimization spans model design, hardware selection, and infrastructure. Coordination across layers (e.g., KV-cache aware routing, cold-start reduction, GPU utilization) often yields larger gains than model tweaks alone.

• Metrics should include TTFT, end-to-end latency, and percentiles (p90, p95). Reporting only averages hides tail latency that users actually experience.

• Mirantis k0rdent AI addresses inference latency at the platform layer with GPU-aware orchestration, AI workload management, and observability so teams can run low-latency, real-time applications on Kubernetes.

What Is Inference Latency?

Inference latency is the time from submitting a request to an AI model until the model produces a usable output (or the first token in a stream). In streaming setups, time to first token (TTFT) is the slice of latency users feel first; end-to-end latency matters when the full response must be ready before any output is shown. Put simply: TTFT matters most when the user sees a stream (e.g., chat); end-to-end latency matters when the system waits for the complete response before showing anything.

Throughput, by contrast, is how much work the system does per unit time (e.g., tokens per second or requests per minute). A system can have high throughput but poor latency if requests sit in queues or batches; for interactive and real-time applications, latency usually matters more.

Authoritative benchmarks stress reporting the full distribution, not just the mean. As Anyscale’s guide to reproducible LLM metrics notes, teams should report time to first token (TTFT), inter-token latency, and end-to-end latency with percentiles (P50, P90, P95, P99). The same analysis suggests that, for typical workloads, input tokens have roughly 1% of the impact of output tokens on end-to-end latency; reducing output length often improves latency more than trimming input. Input length affects TTFT (prefill) more; output length dominates end-to-end latency.

With that definition and those metrics in mind, the next step is to improve inference latency where it hurts most. The levers fall into three areas: how you measure, how you design the model and hardware, and how you run and orchestrate workloads. The following seven practices span all three so that optimization strategies line up instead of working at cross-purposes.

7 Best Practices for Ensuring Latency-Optimized Inference

Improving inference latency usually requires coordinated optimization strategies across model, hardware, and infrastructure. AI model complexity, hardware capability, and how workloads are scheduled and routed all affect the numbers you see. The following practices apply across those layers.

1. Measure and Baseline Inference Latency Before Optimization

Before changing models, hardware, or infrastructure, establish a baseline. Measure latency consistently under a workload that resembles production (e.g., representative prompt lengths and concurrency). Use metrics that reflect user experience: TTFT for streaming, end-to-end latency for non-streaming, and percentile-based reporting (e.g., p90, p95) so tail latency is visible. A mean-only view hides the long tail that a significant share of users hit. Open-source tools and vendor benchmarks (such as those described in Anyscale’s LLM performance post) provide a starting point for defining and comparing these metrics; use them to measure latency in a reproducible way so that future changes can be evaluated against the same baseline.

2. Adjust Model Architecture for Low-Latency Inference

AI model complexity is a direct lever on latency. Smaller or quantized models (e.g., INT4 or INT8) reduce memory and compute per token, which can lower both time to first token and end-to-end latency. Quantization trades some precision for speed and memory; many production deployments use 8-bit or 4-bit variants when the quality drop is acceptable. Architectures that favor faster first-token response (e.g., efficient attention or smaller context windows) help when the use case allows it. The tradeoff is quality and capability: the right choice depends on acceptable accuracy and whether users tolerate shorter or less nuanced answers. There is no single best model; match the model to the latency budget and the task, and revisit as you improve infrastructure (often the same model on a better-orchestrated platform will hit lower p95 without a smaller model).

3. Lower Time to First Token (TTFT) in LLM Workloads

TTFT is dominated by the prefill phase, when the model processes the full prompt before emitting the first token. Shorter prompts, efficient attention implementations, and hardware that can sustain high compute during prefill all help. Infrastructure choices matter as much as model choices here. Routing requests to nodes that already hold the relevant context in their KV cache avoids re-running prefill; that is why production guides recommend cache-aware routing instead of round-robin for multi-replica inference. When the same context is reused (e.g., a shared system prompt or RAG context), hitting a warm cache can cut effective TTFT sharply.

4. Address Decode and Memory Bottlenecks in Inference Pipelines

After prefill, the decode phase generates tokens one at a time; each step reads from the KV cache and writes new activations. That pattern is often memory-bandwidth bound: the GPU spends more time moving data than doing arithmetic. Batching multiple requests together increases throughput and GPU utilization, but large batches can lengthen queue wait and hurt latency. Modern inference engines use continuous batching so that requests can join and leave the batch as they complete rather than waiting for the whole batch; that keeps utilization high without forcing every request to wait for the slowest one. Batching and scheduling that keep the GPU busy without over-queuing requests improve both latency and throughput when tuned to the workload. Balance batch size and queue depth so that no single dimension becomes the bottleneck; server-level metrics (batch size, queue size) often guide that balance better than raw GPU utilization alone.

5. Select Hardware and GPUs for Real-Time Inference

Specialized hardware and the right compute resources set the ceiling for latency. GPUs with sufficient memory bandwidth and capacity for your model and batch sizes determine how fast decode can run; under-provisioned or poorly matched hardware will cap gains from model or software tuning. Memory bandwidth in particular drives decode performance because each token generation pulls from the KV cache; GPUs with higher memory bandwidth (e.g., H100 vs older generations) can sustain higher token throughput at the same utilization. Placement and scheduling matter as well. GPU utilization is a useful signal for capacity planning, but it can be misleading when decode is memory-bound (utilization may look low even when the GPU is the bottleneck). Pair it with request-level and application metrics such as tokens per second and p95 latency so you can tell the difference between underused capacity and a memory-bound workload.

6. Optimize Infrastructure and Networking for Distributed Inference

Infrastructure and networking choices often have as much impact on inference latency as model or hardware alone. On Kubernetes, the assumptions that work well for CPU microservices (round-robin load balancing, small images, fast pod startup) work against GPU inference. A single conversation or session benefits from sticky routing to the same replica so that the KV cache is reused; round-robin spreads requests across replicas and forces repeated prefill, which can turn sub-second responses into multi-second delays in some workloads. Production guides such as AI Inference on Kubernetes: A Production Guide recommend KV-cache aware routing so that requests hit nodes that already hold the relevant context; that avoids expensive re-computation.

Cold starts are costly because GPU container images are large (CUDA base images alone are on the order of 12 GB, and model weights can push images to tens of gigabytes). With model caching, image optimization, and lazy loading, cold starts can be reduced from 10+ minutes to under 30 seconds in many setups. One analysis found that containers spend about 76% of their start time downloading the image while using only a small fraction of the downloaded files, which is why on-demand or lazy loading can cut container start from over 12 minutes to roughly 2 seconds to ENTRYPOINT in some setups (see Tensorfuse – GPU cold start).

Routing and orchestration at the platform layer compound these gains. AWS reports that Least Outstanding Requests (LOR) routing improved end-to-end P99 latency by 4–33% and throughput per instance by 15–16% in tests (see SageMaker routing strategies). Achieving 90%+ GPU utilization with batching and memory-aware scheduling is a common goal. Orchestration layers (e.g., NVIDIA NIM Operator with model pre-caching, or NVIDIA Grove for prefill/decode scaling) show how platform-level coordination reduces latency at scale.

7. Implement Continuous Monitoring for Inference Time and p90 Latency

Track inference time and tail latency (e.g., p90, p95) in production so you can spot regressions and tune the practices above. Server-level metrics such as batch size and queue size (as in GKE’s best practices for LLM inference autoscaling) often correlate better with latency and throughput than GPU utilization alone. Use them to drive autoscaling and capacity decisions: for example, scale up when queue depth or batch size exceeds a threshold that correlates with your target p95 latency, and use a stabilization window on scale-down to avoid thrashing.

Why Low-Latency Inference Is Critical for Real-Time AI Applications

Latency directly affects business outcomes and user trust. When responses are slow or inconsistent, users abandon flows and lose confidence in the system. Tail latency (e.g., p95) often matters more than the median, because the worst cases define the experience for a meaningful share of users; a system with a good average but a long tail will still frustrate a sizable fraction of requests. The following three areas show where that impact shows up most: experience, cost, and competitive position.

User Experience and AI Assistant Responsiveness

For chat, copilots, and voice agents, inference latency is the main driver of perceived speed. Humans read on the order of a few tokens per second (e.g., ~4–6 depending on language and tokenization); if the model cannot sustain that pace or if TTFT is high, the experience feels laggy. Users notice delays of a few hundred milliseconds; for voice agents, a sub-second first response is commonly targeted so that conversation feels natural. Long TTFT or variable p95 makes the product feel broken even if average latency looks acceptable. Real-world evidence from platform-level changes illustrates the impact. After placing GKE Inference Gateway in front of model servers, Vertex AI reported roughly 35% faster time to first token for one model and about 2x improvement in TTFT p95 for another (bursty chat workloads). Load-aware and content-aware routing, plus admission control at the ingress, reduced queue congestion so that more users got fast, consistent responses instead of long tail delays.

Cost and Resource Efficiency at Scale

Latency and cost are linked. When routing or scheduling is poor, the same workload triggers more re-computation (e.g., cache misses) or leaves GPUs underused, so you pay for capacity that does not translate into throughput. Teams often overprovision (e.g., keep warm pools or avoid scale-to-zero) to mask latency variability; that inflates cost. The same GKE Inference Gateway rollout doubled prefix cache hit rate from 35% to 70%, meaning fewer redundant prefills and lower cost per request. Optimizing inference latency via better orchestration and cache reuse often reduces cost at the same time as it improves responsiveness, so investments in the platform layer can pay off in both dimensions.

Competitive Advantage in Real-Time AI Systems

In markets where users expect instant answers, predictable low latency becomes a differentiator. Applications that feel fast and reliable retain users and support premium use cases (e.g., real-time coding assistance or live translation). Inference latency is therefore not only a technical metric; it shapes whether a product can credibly promise real-time behavior and whether teams can scale that promise as traffic grows. Once latency is visible and stable, product and engineering can iterate on features that depend on it instead of working around variability.

Key Metrics for Measuring Real-Time Inference Latency

Optimizing inference latency depends on tracking precise, actionable metrics across model, hardware, and infrastructure. The table below summarizes core metrics, what they measure, why they matter, and how teams typically optimize them.

Sources: Anyscale – Reproducible Performance Metrics; GKE – Best practices for autoscaling LLM inference with GPUs; AI Inference on Kubernetes; Tensorfuse – GPU cold start.

Use these metrics to baseline current behavior (practice 1), to tune batching and routing (practices 4 and 6), and to drive autoscaling and capacity planning (practice 7). No single metric tells the full story. When choosing autoscaling targets, GKE’s guidance is illustrative: queue size is well suited when the goal is to maximize throughput and minimize cost within a latency budget, whereas batch size is better when you need to hit stricter latency targets (e.g., scale up when batch size approaches a level that historically correlates with p95 breaches).

Maintain Latency-Optimized Inference with Mirantis

The practices and metrics above assume you have control over how workloads are scheduled, routed, and scaled. At enterprise scale, inference latency is as much a systems and orchestration problem as a model-engineering one. Workload placement, GPU utilization, cold-start behavior, and network topology across distributed Kubernetes clusters all determine what users actually see. When replicas span multiple nodes or regions, when multiple models or versions coexist, and when traffic is bursty, the control plane (scheduling, routing, scaling, observability) becomes the difference between predictable low latency and constant firefighting. k0rdent AI from Mirantis targets that platform layer so you can minimize latency and run real-time applications predictably.

• GPU-aware workload orchestration. Schedule and place inference workloads so that GPU capacity and memory bandwidth are used effectively and requests reach the right nodes (e.g., with warm caches). The goal is to avoid unnecessary cold starts and to route traffic to replicas that can serve it with minimal re-computation.

• Intelligent AI workload management. Integrate with AI infrastructure and AI workload management practices so scaling and routing decisions support low latency and high utilization. Scaling policies and queue- or batch-based triggers keep capacity aligned with demand without overprovisioning.

• Multi-cluster routing and policy-driven allocation. Route traffic and allocate resources using policy and observability rather than ad-hoc tuning. Multi-cluster and multi-region setups benefit from a single place to define where workloads run and how traffic is directed.

• Integrated observability. Track inference time, p90/p95 latency, and capacity so you can correct regressions and plan capacity. When latency degrades, observability across the stack (request, batch, queue, GPU, cold start) narrows down the cause quickly.

Mirantis does not sell model-level optimization; it provides the Kubernetes-native control plane that helps teams reduce scheduling delays and cold-start inefficiencies while scaling real-time inference in a consistent way. Ecosystem approaches (e.g., NVIDIA NIM on GKE) show how platform-layer orchestration and one-click patterns are becoming standard; k0rdent AI is built for teams that want that same class of control on their own stack. When your bottleneck shifts from model tuning to orchestration and placement, a dedicated platform for inference orchestration is the next step.
Book a demo today and learn how Mirantis k0rdent AI helps teams reduce inference latency, optimize GPU utilization, and deliver real-time AI performance.