How do I choose between different GPU inference platforms for production model serving?

Choosing a GPU inference platform for production model serving is mostly about matching the platform to your constraints: latency, throughput, model size, traffic pattern, operational burden, and cost.

1) Start with your serving requirements

Ask these first:

• Latency target: p50, p95, p99?
• Throughput: requests/sec, tokens/sec, images/sec?
• Traffic shape: steady, bursty, or spiky?
• Model type: LLM, vision, embeddings, ranking, multimodal?
• Model size / precision: FP16, INT8, quantized, tensor parallel?
• Batching tolerance: can requests wait 10–50 ms to batch?
• SLO / availability: do you need multi-region or active-active?
• Compliance: VPC, on-prem, data residency, audit logs?
• Ops model: managed service vs self-hosted?

These answers narrow the choice more than any vendor feature list.

2) Compare platforms on the right dimensions

A. Performance

Key questions:

• How well does it use the GPU?
• Does it support dynamic batching, continuous batching, KV cache reuse, speculative decoding, tensor/pipeline parallelism?
• Is it optimized for your model family?

For LLMs, look for:

• Token latency and decode throughput
• Support for paged KV cache / memory-efficient attention
• Streaming response support
• Multi-GPU sharding

For vision or classical inference:

• Batch throughput, CUDA kernel optimization, and model compilation support matter more.

B. Cost

Look beyond raw GPU hourly cost:

• GPU utilization efficiency
• Autoscaling granularity
• Idle time / warm pool costs
• Network egress
• Managed platform markup
• Engineer time for ops and tuning

A platform with slightly higher GPU price can still be cheaper if it keeps GPUs busier.

C. Operational complexity

• Can you deploy with Docker/Kubernetes?
• Does it integrate with your CI/CD?
• How hard is canarying, rollback, and versioning?
• Does it expose metrics, tracing, and logs?
• Can it autoscale on queue depth, tokens/sec, or concurrency?

D. Reliability and isolation

• Multi-AZ support?
• Health checks and restart behavior?
• Support for model warmup?
• Request draining on deploys?
• GPU failure handling?

E. Flexibility

• Can you bring custom code and custom preprocessing/postprocessing?
• Does it support custom CUDA kernels or Python handlers?
• Can you run multiple models on one GPU?
• Does it support open-source engines and frameworks?

3) Common platform categories

Managed inference APIs

Examples: hosted model APIs or managed endpoints.

Best for: fastest time-to-market, low ops burden
Pros:

• Easy deployment
• Built-in scaling and monitoring
• Good for prototypes and many production use cases

Cons:

• Less control over optimization
• Can be expensive at scale
• Vendor lock-in and limited customization

Self-hosted inference on Kubernetes or VMs

Examples: vLLM, TGI, Triton, TensorRT-LLM, Ray Serve, BentoML.

Best for: cost-sensitive, custom, or regulated workloads
Pros:

• Full control
• Better tuning for your workload
• Portability across clouds/on-prem

Cons:

• More operational burden
• You own scaling, upgrades, and debugging

GPU cloud / inference platforms

Examples: specialized GPU hosting and serverless inference providers.

Best for: teams that want some control without full infra ops
Pros:

• Faster setup than DIY
• Often good autoscaling and deployment UX
• Can be cost-effective for moderate scale

Cons:

• Less control than self-hosted
• Platform-specific quirks and limits

4) Match platform to workload

If you serve LLMs

Prioritize:

• Continuous batching
• Streaming support
• KV cache management
• Tensor parallelism
• Low cold-start times
• Support for quantized models

Often strong choices:

• vLLM for high-throughput open-source serving
• TGI for simpler LLM deployment
• TensorRT-LLM for maximum NVIDIA-optimized performance
• Managed LLM endpoints if ops simplicity matters most

If you serve vision / CV / embeddings

Prioritize:

• Batch throughput
• Model compilation
• Triton support
• Multi-model routing
• Fast preprocessing pipelines

Often strong choices:

• Triton Inference Server
• TensorRT-optimized pipelines
• BentoML / Ray Serve for flexible app integration

5) Evaluate with a benchmark that matches production

Don’t trust vendor benchmarks alone.

Benchmark using:

• Your actual model
• Real input lengths / image sizes / embedding dimensions
• Real concurrency patterns
• Your latency SLO
• Warm and cold starts
• Mixed traffic if applicable

Measure:

• p50/p95/p99 latency
• throughput
• GPU memory usage
• utilization
• error rate
• cost per 1k requests or per million tokens

6) Important “gotchas”

• Batching improves throughput but can hurt tail latency
• Cold starts matter for serverless-style platforms
• GPU memory is often the real bottleneck for LLMs
• Quantization can change quality
• Autoscaling delays can break burst handling
• Networking and CPU preprocessing can bottleneck the GPU
• Framework overhead can dominate for small models

7) A simple decision guide

Choose managed inference if:

• You want minimal ops
• You’re early stage
• You can accept less customization

Choose self-hosted if:

• You need maximum control
• You care about cost at scale
• You have special compliance or latency needs

Choose specialized GPU inference platforms if:

• You want a middle ground
• You need quicker deployment than DIY
• Your workload fits the platform’s strengths

8) Practical shortlist approach

For each candidate platform, score it 1–5 on:

• Latency
• Throughput
• Cost
• Ease of deployment
• Autoscaling
• Observability
• Compliance
• Flexibility
• Vendor lock-in risk

Then run a pilot on 1–3 platforms and compare using your real traffic.

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If you want, I can also give you:

1. a comparison matrix of popular platforms, or
2. a step-by-step evaluation checklist you can use with your team.