I'm building an inference service—what GPU infrastructure should I use?

It depends mostly on model size, latency target, traffic pattern, and whether you need autoscaling. Here’s a practical way to choose GPU infrastructure for an inference service.

1) Start with the workload shape

Use a single GPU VM or bare metal if:

• You’re serving one model or a small set of models
• Traffic is modest or predictable
• You want the simplest ops
• You can tolerate manual scaling

Good for:

• Early-stage products
• Internal APIs
• Batch inference
• Low/medium QPS

Use managed GPU instances / cloud autoscaling if:

• Traffic is spiky or uncertain
• You need fast iteration
• You want less infra maintenance
• You’re okay with some cloud cost premium

Good for:

• Customer-facing APIs
• Seasonal or bursty demand
• Teams without dedicated infra engineers

Use a multi-GPU host or cluster if:

• The model is too large for one GPU
• You need high throughput
• You want to batch aggressively
• You serve multiple replicas with shared networking/storage

Good for:

• 70B+ LLMs
• High-QPS embedding or reranking services
• Large vision models at scale

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2) Match GPU type to the model

For LLM inference

• Small/medium models (7B–13B):
  • NVIDIA L4, A10, L40S often work well
• Larger models (30B–70B):
  • A100 80GB, H100, or multiple GPUs with tensor parallelism
• Very latency-sensitive, high-throughput:
  • H100 or L40S depending on budget and precision

For embeddings / reranking / CV

• L4 / A10 are often cost-effective
• L40S if you want more headroom
• A100/H100 only if the model is heavy or traffic is high

For cost-sensitive inference

• L4 is often one of the best price/performance options
• A10 can also be strong if available cheaply

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3) Infrastructure options, from simplest to most scalable

Option A: Managed GPU endpoints

Examples: cloud model-serving endpoints, managed inference platforms

Pros

• Fastest to deploy
• Built-in autoscaling and monitoring
• Little DevOps

Cons

• More expensive
• Less control over runtime/optimizations
• Vendor lock-in

Best when: speed matters more than control.

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Option B: Kubernetes with GPU nodes

Examples: EKS/GKE/AKS + NVIDIA device plugin + KServe/Ray Serve/Triton/vLLM

Pros

• Good balance of control and scale
• Autoscaling
• Works well for multi-model deployments

Cons

• More operational complexity
• GPU scheduling and bin packing can be tricky

Best when: you have multiple services or need production-grade flexibility.

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Option C: Dedicated GPU VMs or bare metal

Pros

• Maximum control
• Often best performance per dollar
• Easier to tune networking and storage

Cons

• More manual scaling and orchestration
• Less elastic

Best when: you’re optimizing cost/perf and have stable demand.

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4) Serving stack matters as much as the GPU

For LLMs:

• vLLM: excellent throughput, good for batched decoding
• TensorRT-LLM: very strong performance, more optimization effort
• TGI (Text Generation Inference): solid, production-friendly
• SGLang: good for advanced serving patterns

For general model serving:

• NVIDIA Triton Inference Server
• Ray Serve
• KServe
• BentoML

If you’re building an LLM API, a common strong choice is:

• L4/A10/L40S + vLLM + Kubernetes or autoscaled GPU VMs

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5) Key infrastructure decisions

Memory vs compute

• If the model doesn’t fit in GPU memory, no amount of compute helps.
• Prefer more VRAM if you’re serving larger models or long context windows.
• Prefer more compute if the model fits comfortably and you need throughput.

Latency vs throughput

• Low latency: smaller batch sizes, faster GPUs, careful request scheduling
• High throughput: batching, larger GPUs, continuous batching frameworks like vLLM

Precision

• FP16/BF16 is standard
• INT8/INT4 quantization can dramatically cut cost if quality remains acceptable
• Quantization often changes the optimal GPU choice

Scaling style

• Vertical scaling: one bigger GPU per replica
• Horizontal scaling: many smaller replicas
• For inference, horizontal scaling plus batching is often easier to manage.

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6) Practical recommendations

If you’re just getting started

• Use L4 or A10 cloud GPUs
• Run vLLM or Triton
• Deploy on:
  • a simple GPU VM
  • or Kubernetes only if you already use it

If you expect real production traffic

• Use L4/L40S for cost-effective serving
• Add autoscaling based on:
  • GPU utilization
  • queue length
  • request latency
• Use a serving framework with batching support

If you’re serving large LLMs

• Use A100 80GB or H100
• Plan for:
  • tensor parallelism
  • KV cache memory pressure
  • request batching
• Consider dedicated nodes or a cluster

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7) A simple decision shortcut

• Need cheapest decent inference? → L4
• Need more headroom without going top-end expensive? → L40S
• Need big VRAM for large LLMs? → A100 80GB
• Need maximum performance and low latency at scale? → H100
• Need fastest setup with minimal ops? → Managed GPU endpoint

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8) If you want a default recommendation

For many new inference services, I’d start with:

• NVIDIA L4 GPUs
• vLLM for LLMs
• Kubernetes or autoscaled GPU VMs
• Cloud-managed observability
• Add quantization if quality allows

This is usually the best balance of:

• cost
• performance
• ease of deployment
• scalability

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If you tell me:

1. model type and size,
2. target latency,
3. expected QPS,
4. context length / batch size, and
5. budget preference,

I can recommend a specific GPU class and deployment architecture.