Optimizing GenAI Inference: Lessons from Production GPU Clusters

Deploying large language models in production is straightforward until it isn’t. The gap between a working demo and a cost-effective, scalable production system is where most teams struggle.

After architecting GenAI inference infrastructure serving multiple diffusion and LLM models on GCP/GKE and AWS/EKS, here are the techniques that delivered measurable results.

The Real Cost of Naive Deployments

Most teams start with a simple containerized model serving setup. It works—until you’re paying 3x what you should for GPU compute, and latency spikes during peak traffic.

The core issues:

• Memory fragmentation from inefficient KV-cache management
• Underutilized GPUs due to poor batching strategies
• Cold start latency from loading model weights on every request

KV-Cache Optimization

The KV-cache stores key-value pairs from previous tokens during autoregressive generation. Naive implementations allocate fixed memory per sequence, wasting GPU RAM.

What works:

• PagedAttention (used in vLLM) manages KV-cache like virtual memory
• Dynamic allocation reduces memory waste by 60-70%
• Enables higher batch sizes without OOM errors

Distributed Inference Architecture

For models that don’t fit on a single GPU:

1. Tensor Parallelism — Split model layers across GPUs
2. Pipeline Parallelism — Stage different layers on different devices
3. Data Parallelism — Replicate the model for throughput

The choice depends on your latency vs. throughput requirements. We typically use tensor parallelism for latency-sensitive applications and combine it with data parallelism for high-throughput scenarios.

Results

Implementing these techniques across our production infrastructure:

• 40% reduction in inference latency
• 25% reduction in operational costs
• 3x improvement in requests per GPU

Key Takeaways

1. Don’t scale hardware before optimizing software
2. Monitor GPU utilization, not just request latency
3. Batch aggressively, but understand your latency SLAs
4. Document your cluster management playbooks—you’ll need them at 2 AM

The infrastructure work isn’t glamorous, but it’s what separates demos from production systems.

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