What is the best GPU setup for batch inference?

The “best” GPU setup for batch inference depends mostly on model size, precision, latency target, and batch size, but in general:

Best overall pattern

Use as few GPUs as needed, and make each GPU as fully utilized as possible.

For batch inference, the sweet spot is usually:

• 1 GPU per model replica for simpler deployment
• Large enough batches to saturate the GPU
• High-memory GPUs if the model is large or you want bigger batches
• Fast interconnect only if the model must be split across GPUs

Good setup choices by scenario

1) Small/medium models, high-throughput batch jobs

Best choice:

• Single modern GPU
• Examples: NVIDIA L4, L40S, A10, A100, H100
• Prefer:
  • More VRAM over multiple weaker GPUs
  • TensorRT / vLLM / Triton / FasterTransformer depending on model type

Why:

• Batch inference often benefits more from one strong GPU with large memory than from spreading work across many GPUs.

2) Large models that fit on one GPU

Best choice:

• One high-VRAM GPU
• Examples: A100 80GB, H100 80GB, L40S 48GB
• Use:
  • FP16/BF16, or INT8/INT4 if acceptable
  • Large batches and async request queuing

Why:

• Avoids communication overhead from multi-GPU model parallelism.

3) Models too large for one GPU

Best choice:

• Multi-GPU with NVLink or high-speed interconnect
• Examples:
  • 2–8 GPUs in one node
  • NVLink-enabled systems for model parallel inference
• Use:
  • Tensor parallelism
  • Pipeline parallelism only when necessary

Why:

• If the model doesn’t fit, splitting across GPUs is necessary, but it adds overhead.

4) Cost-efficient batch inference at scale

Best choice:

• Several midrange GPUs, each running a replica
• Examples:
  • L4, A10, L40S
• Best when:
  • You have many independent inference tasks
  • The model fits comfortably on each GPU
  • You want horizontal scaling

Why:

• For batch workloads, throughput scales well with multiple replicas.

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Practical recommendation

If you want a broadly good default:

For most batch inference workloads:

• 1× NVIDIA L40S (48GB) if budget-conscious and model fits
• 1× A100 80GB or H100 80GB if the model is large or you want maximum throughput
• Multiple replicas of that GPU if the batch volume is high

If you’re serving LLMs:

• Prefer:
  • vLLM for efficient batching and KV cache management
  • H100/A100/L40S depending on budget
• Avoid model parallelism unless needed.

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Key selection rules

Choose based on:

1. VRAM first — can the model + batch fit?
2. Throughput second — can the GPU stay busy?
3. Interconnect third — only critical for multi-GPU model splitting
4. Cost per token/sample — optimize for your actual workload

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Rule of thumb

• If the model fits on one GPU, use one GPU.
• If throughput is the issue, add more identical GPUs and replicate the model.
• If memory is the issue, use bigger VRAM or multi-GPU sharding.

If you tell me:

• model type/size,
• precision,
• batch size,
• latency requirement,
• and budget,

I can recommend a specific GPU configuration.