AI Inference at Scale: Infrastructure Patterns for Production LLM Serving

Serving a language model in a demo environment is straightforward. Serving that same model to thousands of concurrent users with latency SLAs, cost constraints, and five-nines availability requirements is an infrastructure engineering problem that has no trivial solutions. The gap between proof-of-concept inference and production-grade AI serving is where most enterprise AI programs stall. The teams that cross that gap successfully are those that treat LLM serving infrastructure with the same rigor they apply to any other high-scale distributed system — because that is exactly what it is.

Disaggregated prefill and decode: why it matters at scale

LLM inference has two distinct computational phases. Prefill processes the input prompt — it is compute-intensive and parallelizes well across GPUs. Decode generates output tokens one at a time — it is memory-bandwidth-bound and does not parallelize as naturally. Running both phases on the same GPU fleet means that prefill requests and decode requests compete for resources with very different utilization profiles. Disaggregated inference — routing prefill and decode to separate, purpose-optimized GPU pools — can improve overall throughput by 30–60% at scale by allowing each pool to be sized and scheduled for its specific computational profile. This is a non-trivial infrastructure change but one that major serving frameworks (vLLM, SGLang) increasingly support.

KV cache management: the hidden performance lever

The KV cache stores the attention key-value tensors computed during prefill so they do not need to be recomputed on each decode step. Efficient KV cache management is one of the most impactful optimizations available in production serving. PagedAttention (the technique underlying vLLM) manages KV cache in fixed-size pages rather than contiguous memory, reducing fragmentation and allowing much higher batch utilization. Prefix caching — saving KV cache for common prompt prefixes — can reduce computation costs by 50–80% for workloads where many requests share a system prompt, such as customer service or code completion applications.

• Continuous batching allows new requests to join in-progress decode batches, increasing GPU utilization from 30–50% to 70–90%
• Tensor parallelism splits a single model across multiple GPUs — use when the model does not fit in single-GPU VRAM
• Pipeline parallelism splits model layers across GPUs — lower interconnect bandwidth requirement than tensor parallelism but higher latency
• Speculative decoding uses a small draft model to propose tokens verified by the full model, improving throughput for high-quality output requirements
• Quantization (INT8, FP8, INT4) reduces memory footprint and increases throughput at the cost of some output quality — quantization-aware serving needs validation
• Router/load-balancer design must be aware of session affinity for KV cache reuse to be effective

GPU fleet sizing for inference: the utilization trap

Inference fleet sizing requires modeling the worst case, not the average. A fleet sized for average concurrent users will fail under traffic spikes exactly when reliability matters most. The practical approach is to size for 70–80% average GPU utilization under peak load, keeping headroom for burst and for rolling hardware maintenance. Queuing theory analysis of your request distribution — particularly the tail of long-context requests that hold GPU memory for extended periods — is necessary to avoid underestimating the fleet size needed to meet p99 latency targets.

The teams that succeed with production LLM serving treat it like building a database cluster — not like hosting a web app. The same principles of capacity planning, failure mode analysis, and operational discipline apply. It is just a different kind of state.

How Nexus Compute helps

Nexus Compute supplies GPU inference servers optimized for production LLM serving — high-memory-bandwidth accelerators, NVMe-tiered KV cache storage, and low-latency networking fabrics suitable for disaggregated serving architectures. We work with infrastructure and ML engineering teams to validate fleet sizing assumptions and design serving configurations that meet real latency and throughput requirements under production load patterns.