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AI/MLPythonPerformance

Optimizing AI Inference Pipelines

Batching, quantization, and GPU use so you can run multiple models on a single edge node.

Jan 15, 2026 · 6 min read

Running several AI models on one machine—especially at the edge—is mostly about using the hardware you have in a smarter way. Here’s what has worked for me: batching, quantization, and a clear pipeline so CPU and GPU don’t fight each other.

Batch when you can

Single-image inference is simple but wastes GPU time. We batch frames (or requests) and run inference every N milliseconds or when the batch is full. That means a bit more latency but much higher throughput. For video, we often batch 4–8 frames; for document or image APIs we batch by request. The trick is to cap batch size and timeout so latency stays within what the product needs.

Quantization

Moving from FP32 to FP16, or to INT8 when the model supports it, cuts memory and often doubles effective throughput with small accuracy loss. We quantize after training and validate on a held-out set. For edge deployment, INT8 is a big win; for servers with enough memory, FP16 is a good default. We keep one full-precision model for evaluation and run quantized versions in production.

One machine, multiple models

When several models share a GPU, we either load them in sequence and switch at runtime (simpler, one active model at a time) or keep them all in memory if VRAM allows. We also offload some models to CPU (e.g. light classifiers or pre/post steps) so the GPU focuses on the heavy nets. Setting CUDA_VISIBLE_DEVICES or process affinity helps when you have multiple GPUs or want to reserve one for a critical model.

Pipeline design

We use a small queue in front of the inference worker: producers push frames or tasks, the worker batches them, runs the model, and pushes results to the next stage (e.g. event writer, API response). That keeps the rest of the system non-blocking and makes it easy to add more workers or GPUs later.

Takeaways

  • Batch inputs to improve GPU utilization; tune batch size and timeout for latency.
  • Use quantization (FP16 or INT8) to reduce memory and increase throughput.
  • Separate CPU vs GPU work and design a simple queue-based pipeline so scaling is straightforward.