Cutting GPU Inference Costs Without Hurting Latency

GPU bills have a way of growing quietly until someone in finance asks a pointed question. Here's how I approach inference cost optimization without trading away the latency that users care about.

Start by measuring, not guessing#

Before changing anything, attribute cost to workloads. You can't optimize what you can't see.

# Per-model GPU utilization via DCGM exporter + Prometheus
sum by (model) (rate(dcgm_gpu_utilization[5m]))

If a model averages 15% GPU utilization, you don't have a cost problem — you have a packing problem.

Quantization: the highest-leverage lever#

Moving from FP16 to INT8 or FP8 roughly halves memory footprint and increases throughput, often with negligible quality loss for many workloads.

# Example: load an AWQ-quantized model with vLLM
from vllm import LLM
 
llm = LLM(
    model="TheBloke/Llama-3.1-8B-Instruct-AWQ",
    quantization="awq",
    gpu_memory_utilization=0.9,
)

Always validate quality on your eval set before shipping. Benchmarks lie; your task is the only ground truth.

Right-size the hardware#

The biggest, newest GPU is rarely the most cost-effective. For an 8B model:

• An A10G may deliver better tokens-per-dollar than an A100.
• Tensor parallelism across cheaper GPUs can beat one expensive card.

Pack more model into each GPU#

• Continuous batching keeps the GPU busy.
• Multi-LoRA serving lets one base model serve many fine-tunes.
• KV cache management (paged attention) reclaims wasted memory.

The results#

On a recent platform, combining FP8 quantization, right-sized A10G nodes, and continuous batching cut the monthly bill by ~60% while keeping p99 latency within noise. None of these are exotic — they're disciplined defaults.

The lesson: cost optimization is mostly removing waste, not clever tricks.