AI Inference: GPU and HPC Infrastructure Guide for LLM Serving

As large language models (LLMs) spread across organizations, attention has shifted from training toward inference. Training a model with hundreds of billions of parameters takes months; what a company actually needs in production is the ability to serve the trained model fast, cheap, and without interruption. Building AI inference infrastructure is now one of the most common tasks for HPC architects.

In this guide we cover the technical decisions for planning GPU and HPC infrastructure for production LLM inference: which GPUs, which serving layer, and what scaling strategy.

What Changes When Moving from Training to Inference?

Training and inference do not use hardware the same way. Training runs long, high-utilization jobs, while inference targets low latency and high throughput. Let’s summarize the differences:

You can read our GPU Cluster Technical Guide post alongside this guide. For inference, choosing the right memory capacity and serving software often matters more than raw hardware.

Hardware Components for LLM Inference

During inference, both the model weights and the KV cache created between prompt and response consume GPU memory. These two components drive the GPU selection:

• Model weights: A 70B model needs roughly 140 GB (FP16). More parameters × bytes per parameter = larger minimum memory.
• KV cache: Long input and output sequences reserve memory per request. In long-context applications the cache can exceed the weights.
• Memory bandwidth: Because autoregressive inference is largely limited by memory bandwidth, high-bandwidth memory such as HBM3e can be worth more than peak FLOPs.

The larger your model, the more critical the question “does it fit in a single GPU?” Models at 70B and above do not fit on one GPU and must be split across several via tensor parallelism.

GPU Selection: H200, A100, L40S

For inference you look at memory capacity and bandwidth rather than raw FLOPs. Our GPU Selection Guide covers the general comparison; for inference specifically:

The new B200 generation also stands out for large inference workloads thanks to high HBM3e capacity and bandwidth. We compared these models against the H100 in our NVIDIA H200 and B200 Architectures post.

Serving Layer: vLLM, TensorRT-LLM, and TGI

Hardware alone is not enough; you need an inference server that serves the model in real time. This layer groups requests efficiently (batching), manages the KV cache, and keeps GPUs saturated. The leading options today:

The shared goal of these tools is continuous batching, letting the GPU process more concurrent requests. During peak periods, autoscaling the number of serving replicas also comes into play.

Scaling and Topology

Two kinds of scaling apply to an inference cluster:

1. One model, multiple GPUs (tensor parallel): When the model does not fit on one GPU, weights are split across several. GPU-to-GPU messaging is short, so NVLink bandwidth matters most. Tensor parallel groups of 2–4 GPUs are ideal for latency.
2. Multiple replicas, load balancing: Several serving replicas run independently per model; requests are distributed by a load balancer. This is the most practical way to scale capacity horizontally.

The approaches are usually combined: small tensor parallel inside each replica, load balancing across replicas.

Kubernetes and Autoscaling

Inference workloads are not constant; traffic can multiply during end-of-day or campaign periods. Running LLM serving on Kubernetes is now the standard solution. HPA and custom metrics (for example GPU utilization) automatically adjust the number of replicas.

Mevasis brings container and multi-cluster experience to this area. See our Kubernetes GPU Cluster Guide and Job Scheduler Guide posts for production configuration details.

Cost and Rental Options

The most common mistake when building inference infrastructure is provisioning a GPU park larger than your model requires. When demand fluctuates, GPU rental is an effective way to scale without fixed investment. Check GPU Hourly Rental for instant capacity access, and our HPC Cluster Rental Guide for long-term rental models.

Conclusion

Production LLM inference requires a discipline different from training, in both hardware and software. The key questions to answer:

1. What is the model size and parameter count? Does it fit on a single GPU?
2. Is latency or throughput the priority? This determines GPU and serving technology.
3. How variable is demand? Fixed fleet or rental?
4. Where will replicas scale? Kubernetes?

An inference infrastructure that combines the right GPU, the right serving layer, and the right scaling strategy delivers an optimum balance of latency and cost.

Mevasis would be happy to support you in designing AI inference and HPC infrastructure. Fill in the form on our Contact page.