Table of contents
Choosing the right GPU for inference workloadsHosting environment and infrastructure planningModel deployment and serving architectureScaling considerations for AI inferenceSecurity, monitoring, and maintenanceNext stepsAdditional resources
Get the industry’s best GPU server hosting◦ NVIDIA hardware
◦ Optimized configs
◦ Industry-leading support
Explore options

GPU → AI inference vs Training

AI inference vs training: Server requirements and best hosting setups

AI inference servers are the backbone of real-time machine learning applications—from powering LLM chatbots to serving vision models in ecommerce. If you’re renting a dedicated GPU server, your setup decisions directly impact performance, latency, and scalability. 

Let’s walk through what it takes to build a high-performance inference server that can scale with demand.

Get premium GPU server hosting

Unlock unparalleled performance with leading-edge GPU hosting services.

Explore GPU hosting

Choosing the right GPU for inference workloads

Dedicated GPU servers aren’t one-size-fits-all. Inference workloads vary by use case, so the GPU you pick should match your performance and latency needs.

NVIDIA L40S vs H100: Which one fits your use case?

L40S is built for versatile, high-throughput inference across use cases like:

  • Image generation pipelines
  • Customer support chatbots
  • Video processing
  • Text classification or embedding APIs

With 48GB of GDDR6 memory and optimized INT8/FP8 performance, it strikes a balance between cost-efficiency and speed for mid-size models or multitask deployments.

H100, on the other hand, is built for serious scale. It’s ideal for:

  • Serving LLMs like LLaMA 3 or Mixtral
  • Real-time multimodal inference
  • Healthcare or finance models with low-latency demands
  • Large batch inferencing or multi-tenant inference gateways

Its 80GB of HBM3 memory and 4.9 TB/s memory bandwidth give it a clear edge in both memory-bound and compute-heavy use cases.

Which should you choose?

Use L40S when:

  • You’re deploying multiple smaller models
  • Cost-per-inference is your biggest concern
  • Latency tolerance is >50ms

Use H100 when:

  • You’re serving large or complex models
  • Sub-30ms latency is critical
  • You’re expecting high concurrency or running batch jobs at scale

Hosting environment and infrastructure planning

Once you’ve chosen the right GPU, it’s time to build an environment that supports high-performance inference without bottlenecks.

Why dedicated GPU servers outperform virtualized cloud

Virtualized cloud GPU instances often introduce hypervisor overhead and resource sharing, which can limit performance—especially under peak loads. With dedicated GPU servers:

  • You get full, direct access to the GPU
  • There’s no shared memory, vGPU contention, or throttling
  • You control the OS, drivers, and environment entirely

This matters when milliseconds affect customer experience or when throughput is tied directly to revenue.

CPU, RAM, and storage recommendations

For AI training, CPU, RAM, and storage handle massive data preprocessing, feed GPUs efficiently, and store large datasets and checkpoints—while for inference, they support fast I/O, lightweight computations, and responsive model delivery.

  • CPU: Go with a high core count (16+ threads). Inference is often bottlenecked by data preprocessing or concurrent requests.
  • Server RAM: 128GB+ is a good baseline if you’re caching multiple models in memory.
  • Storage: NVMe SSDs ensure fast access to model weights, logs, and any transient data.

Model deployment and serving architecture

Now let’s talk about how to actually deploy and serve your models on the hardware you’ve rented.

Run your inference in containers

Containerized inference ensures portability, reproducibility, and easier scaling. Best practice is to use:

  • Docker + NVIDIA Container Toolkit
  • CUDA + cuDNN images tailored to your model framework

For production scaling, pair with Kubernetes, HashiCorp Nomad, or an inference-first orchestrator.

Popular model servers include:

  • Triton Inference Server: Supports TensorFlow, PyTorch, ONNX, and XGBoost out of the box. Also enables concurrent multi-model serving and batch inference.
  • TorchServe: Lightweight, PyTorch-native option with model versioning and REST APIs.
  • TensorFlow Serving: Best for TensorFlow workloads with built-in support for model versioning and batching.

Strategies for multi-model serving

Strategies for multi-model serving are crucial because they optimize resource usage, reduce latency, and enable scalable deployment of multiple AI models across shared infrastructure. Best practices include:

  • Keeping “hot” models always loaded in memory.
  • Using lazy loading for less frequently accessed models.
  • Auto-scaling instances by endpoint usage.
  • Setting memory constraints to prevent overload on the GPU.

Scaling considerations for AI inference

Inference workloads may be lighter than training, but they demand strategic scaling to handle real-time performance, fluctuating demand, and infrastructure efficiency.

Vertical vs horizontal scaling

Scaling up or out affects how well your inference stack handles throughput, latency, and model size.

  • Horizontal scaling distributes requests across multiple servers running the same or different models. It supports greater concurrency and resiliency, especially under fluctuating workloads.
  • Vertical scaling upgrades a single server with more powerful GPUs, more RAM, or faster NVMe storage. Whether or not this is important depends on how a GPU server is built, so ask potential hosting providers how they prevent resource bottlenecks.
  • Hybrid scaling combines both, running multiple mid-tier servers, each tuned for specific model sizes or use cases.

Autoscaling and load management

Dynamic scaling helps maintain performance and cost-efficiency when traffic spikes or dips.

  • Container orchestration (e.g., Kubernetes) enables on-demand deployment of model-serving pods across your GPU fleet.
  • Load balancers distribute traffic to available inference nodes to prevent overloading and ensure consistent response times.
  • Dynamic batching groups similar inference requests to improve GPU utilization and lower per-request latency.

Cost control tips

Inference at scale can be expensive if resources aren’t tuned to workload patterns.

  • Right-size GPU resources by matching model memory needs to available VRAM—don’t run small models on overkill hardware.
  • Use quantized or optimized models to reduce inference time and memory usage without significantly sacrificing accuracy.
  • Co-locate multiple models on a single GPU using multiplexing or memory-aware schedulers to get better utilization from idle capacity.

Security, monitoring, and maintenance

Inference workloads may run continuously and serve external requests, making uptime, visibility, and protection critical.

Secure deployment and access control

Inference servers often expose APIs or endpoints, which makes access control and model protection essential.

  • Firewall rules and private networking restrict inbound traffic and help limit exposure to only trusted IPs or internal systems.
  • API authentication (via tokens or OAuth) ensures that only authorized apps or users can send inference requests.
  • Model integrity verification (hashing or signing) prevents tampering or accidental use of outdated models in production.

Monitoring and performance visibility

Keeping inference fast and reliable means tracking resource usage, latency, and error rates in real time.

  • GPU utilization tracking helps identify underused or overloaded hardware and informs scaling decisions.
  • Latency and throughput monitoring exposes bottlenecks, especially under high concurrency or varying input sizes.
  • Log aggregation and alerting (via tools like Prometheus + Grafana or ELK stack) helps detect anomalies or system failures early.

Maintenance and model lifecycle management

As models evolve, infrastructure needs regular updates to stay performant and secure.

  • Scheduled patching and driver updates ensure CUDA compatibility and prevent known vulnerabilities from lingering.
  • Rolling model updates reduce downtime during version deployments and help compare model performance in production.
  • Automated backups of model artifacts safeguard against loss and support rollback when something goes wrong.

Additional resources

What is a GPU? →

A complete beginner’s guide to GPUs and GPU hosting

Best GPU server hosting [2025] →

Top 4 GPU hosting providers side-by-side so you can decide which is best for you

A100 vs H100 vs L40S →

A simple side-by-side comparison of different NVIDIA GPUs and how to decide

Brooke Oates is a Product Manager at Liquid Web, specializing in Cloud VPS and Cloud Metal, with a successful history of IT/hosting and leadership experience. When she’s not perfecting servers, Brooke enjoys gaming and spending time with her kids.