What’s the cheapest way to run inference on GPUs at scale?

The cheapest way depends on your workload, but in practice the lowest-cost GPU inference setups usually come from combining these four levers:

1. Use the smallest/cheapest GPU that meets latency
2. Increase utilization with batching and continuous batching
3. Run quantized models
4. Use the right deployment option: spot/preemptible or reserved bare-metal/cloud GPUs

Lowest-cost options by situation

1) Best raw $/throughput: Spot/preemptible cloud GPUs

If your serving can tolerate interruptions or has failover:

• AWS Spot, GCP Spot, Azure Spot
• Often 50–90% cheaper than on-demand
• Good for:
  • stateless inference
  • async jobs
  • batch generation
  • replicated services with autoscaling and retry

Tradeoff: interruptions, capacity scarcity, more ops work.

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2) Best steady-state cost: Reserved/committed GPUs

If you have predictable load:

• 1-year or 3-year committed use discounts
• Often cheaper than on-demand by a lot, though not as cheap as spot
• Good for:
  • always-on APIs
  • stable traffic

Tradeoff: less flexibility.

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3) Cheapest at very high volume: Own GPUs / colocation / dedicated bare metal

If you’re at large scale and utilization is high:

• Buying or leasing GPUs can beat cloud rates
• Especially if you can keep them busy 24/7
• Good for:
  • large inference fleets
  • latency-sensitive services
  • large model serving

Tradeoff: capex, ops, hardware refresh, staffing.

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What actually reduces inference cost the most

A. Maximize GPU utilization

This is usually the biggest lever.

Use:

• dynamic batching
• continuous batching
• microbatching
• request coalescing
• KV-cache reuse where applicable

Popular serving stacks:

• vLLM
• TensorRT-LLM
• TGI (Text Generation Inference)
• Triton Inference Server

If your GPU sits idle half the time, your cost per token/request is much worse than it needs to be.

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B. Quantize models

Quantization often cuts cost dramatically:

• FP16/BF16: baseline
• INT8: common sweet spot
• INT4 / 4-bit: best savings when quality allows

Benefits:

• lower memory use
• more model replicas per GPU
• higher throughput
• cheaper GPUs may become viable

For LLM serving, 4-bit or 8-bit weights can be a major cost reducer, especially for smaller latency-tolerant use cases.

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C. Choose the smallest GPU that fits the model

A lot of cost comes from overprovisioning.

For inference, often cheaper to use:

• consumer/“prosumer” GPUs where acceptable
• smaller datacenter GPUs instead of top-tier ones
• newer GPUs only if they improve throughput enough to offset price

Rule of thumb:

• If your model fits and latency is okay, an L4/A10-class GPU may be far cheaper than an A100/H100
• H100s can be economical only when you truly need their throughput or memory bandwidth

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D. Reduce token/output length

For LLMs, cost is heavily driven by:

• prompt length
• generated tokens
• concurrency

Ways to lower cost:

• shorten system prompts
• trim context
• summarize history
• cap max output tokens
• use retrieval instead of stuffing context

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Common cheapest architecture patterns

For LLM APIs

A cost-efficient setup often looks like:

• vLLM
• INT4/INT8 quantized model
• continuous batching
• spot GPUs with multiple replicas
• autoscaling based on queue depth / token rate

For batch inference

Cheapest is often:

• spot GPUs
• large batches
• offline processing
• checkpointing/retry
• possibly multi-GPU jobs to maximize throughput

For low-latency online serving

• reserved GPUs
• continuous batching
• quantized model
• keep replicas hot
• maybe use a slightly larger GPU to avoid tail latency issues if it improves overall throughput

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Practical ranking for cheapest GPU inference

In many real-world cases:

1. Spot GPU + batching + quantization
2. Reserved GPU + batching + quantization
3. Bare metal / owned GPUs at high utilization
4. On-demand cloud GPUs with poor utilization

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Quick recommendation

If you want the cheapest path that’s usually sane:

• Use vLLM or TensorRT-LLM
• Quantize to 8-bit or 4-bit if quality allows
• Run on spot/preemptible L4/A10-class GPUs
• Use continuous batching
• Keep enough replicas for interruption tolerance
• Measure cost per 1K tokens or per request, not just GPU-hour price

Important caveat

The “cheapest” GPU depends on:

• model size
• context length
• batchability
• latency SLA
• whether you’re doing LLMs, vision, or embedding models

A GPU that’s cheap per hour can be expensive per token if it can’t batch well or doesn’t fit the model efficiently.

If you want, I can help you figure out the cheapest concrete setup for your model by comparing:

• model size
• throughput target
• latency target
• cloud vs self-hosted
• budget constraints