The most cost-effective way to scale inference usually comes down to maximizing GPU utilization and minimizing idle time. In practice, the best approach is often:

1) Use a purpose-built model serving system

Pick infrastructure that supports:

• Dynamic batching / micro-batching
• Concurrent requests / async execution
• Streaming responses
• Autoscaling
• KV cache reuse for LLMs
• Model quantization support

Examples: vLLM, TGI, Triton Inference Server, TensorRT-LLM.

For LLMs specifically, vLLM is often one of the most cost-effective options because it improves throughput via paged attention and efficient memory management.

2) Batch aggressively, but within latency targets

The cheapest inference is the one where your GPU stays busy.

• Combine requests into batches
• Use dynamic batching to avoid waiting too long
• Tune batch size and max delay to balance throughput vs latency

This is especially important for smaller models and high-QPS workloads.

3) Right-size models and use quantization

Reduce compute cost by:

• Using a smaller model if it meets quality needs
• Applying 8-bit or 4-bit quantization
• Distilling large models into smaller ones
• Using speculative decoding for LLMs

Quantization often provides a large cost reduction with modest quality tradeoffs.

4) Improve request routing and autoscaling

Avoid overprovisioning:

• Scale on queue depth, tokens/sec, or GPU utilization
• Use warm pools or keep a small baseline of always-on capacity
• Route traffic to the most appropriate model version or GPU type

For spiky traffic, autoscaling and bin-packing can save a lot.

5) Optimize memory and serving topology

For LLMs, memory is often the bottleneck:

• Use tensor parallelism only when needed
• Prefer models that fit on fewer GPUs
• Use KV-cache-efficient servers
• Separate prefill and decode if your stack supports it

6) Use the cheapest hardware that meets performance targets

Often the best ROI comes from:

• Newer GPUs with better perf/$
• CPU inference for smaller models
• Spot/preemptible instances for fault-tolerant workloads
• Mixed deployment tiers:
  • CPU for low-QPS or lightweight models
  • GPU for heavy LLM workloads

7) Measure the right metric: cost per 1K tokens or per request

For LLM serving, optimize for:

• $/1K input tokens
• $/1K output tokens
• tokens/sec/GPU
• p95 latency
• GPU utilization

Practical recommendation

If you're serving an LLM and want a strong cost/performance starting point:

• vLLM or TGI
• Quantized model
• Dynamic batching
• Autoscaling by queue depth / tokens
• Spot instances for non-critical traffic
• Small always-on baseline + burst capacity

If you want, I can also give you:

1. a cloud cost optimization checklist,
2. a comparison of vLLM vs TGI vs Triton, or
3. a reference architecture for cost-effective LLM serving.