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Arazon
Jan 12, 2026

GPU Optimization for ML Inference: Maximizing Efficiency

GPU costs often dominate production ML budgets. According to Andreessen Horowitz analysis, infrastructure can consume 80% or more of revenue for AI-intensive applications. Optimizing GPU utilization for inference directly impacts both cost and capability—enabling lower latency, higher throughput, and reduced infrastructure spend. This guide covers practical techniques for maximizing GPU efficiency in production deployments.

Understanding GPU Architecture

Core Components

  • CUDA cores / Tensor cores: Parallel processing units
  • High Bandwidth Memory (HBM): Fast but limited GPU memory
  • Memory bandwidth: Rate of data transfer between compute and memory
  • Interconnects: NVLink, PCIe for multi-GPU communication

Bottleneck Analysis

Inference workloads are typically bound by one of:

  • Compute bound: Not enough processing power for operations
  • Memory bandwidth bound: Can't move data fast enough
  • Memory capacity bound: Model doesn't fit in GPU memory

LLM inference is usually memory bandwidth bound—reading model weights from memory dominates runtime.

Batching Strategies

Static Batching

Group requests into fixed-size batches:

  • Simple implementation
  • Higher throughput than single requests
  • Latency penalty waiting for batch to fill
  • Waste from padding variable-length sequences

Dynamic Batching

Assemble batches based on arrival patterns:

  • Timeout-based batch formation
  • Adaptive batch sizes based on load
  • Balance latency and throughput

Continuous Batching

For autoregressive generation, process sequences at different completion stages:

  • Insert new requests as slots become available
  • No waiting for all sequences to complete
  • Dramatically improved throughput for variable-length generation
  • Implemented in vLLM, TGI, and similar frameworks

Memory Optimization

KV Cache Management

For LLM inference, key-value caches consume significant memory:

  • PagedAttention: Non-contiguous memory allocation (vLLM approach)
  • Cache sharing: Reuse common prefixes across requests
  • Cache offloading: Move to CPU when GPU memory constrained

Memory Efficient Attention

  • Flash Attention: Fused kernels reducing memory access
  • Flash Attention 2: Improved parallelism and efficiency
  • xFormers: Optimized attention implementations

Activation Checkpointing

Trade compute for memory by recomputing activations:

  • Reduce memory for large batch sizes
  • Configurable checkpoint granularity
  • Some compute overhead

Model Optimization

Quantization

Reduce numerical precision:

  • FP16 / BF16: Half precision with minimal accuracy impact
  • INT8: 8-bit integers for weights or activations
  • INT4 / GPTQ / AWQ: 4-bit quantization for maximum compression

GPTQ research demonstrates 4-bit quantization maintaining most model quality while dramatically reducing memory requirements.

Pruning

Remove less important weights:

  • Unstructured pruning: Remove individual weights
  • Structured pruning: Remove entire neurons or layers
  • Requires retraining or careful calibration

Distillation

Train smaller models to mimic larger ones:

  • Significant size reduction possible
  • Task-specific distillation often more effective
  • Requires training infrastructure

Inference Frameworks

vLLM

High-throughput LLM serving:

  • PagedAttention for efficient memory use
  • Continuous batching
  • OpenAI-compatible API

TensorRT-LLM

NVIDIA's optimized inference:

  • Maximum performance on NVIDIA hardware
  • Quantization support
  • Multi-GPU parallelism

Text Generation Inference (TGI)

Hugging Face's production server:

  • Flash Attention integration
  • Quantization support
  • Tensor parallelism

ONNX Runtime

Cross-platform inference optimization:

  • Graph optimizations
  • Execution provider flexibility
  • Broad model support

Multi-GPU Strategies

Tensor Parallelism

Split layers across GPUs:

  • Enables larger models than single GPU memory
  • Requires high-bandwidth interconnect (NVLink)
  • Communication overhead for each layer

Pipeline Parallelism

Assign different layers to different GPUs:

  • Lower communication requirements
  • Micro-batching for pipeline efficiency
  • Bubble overhead at batch boundaries

Data Parallelism

Replicate model across GPUs:

  • Scale throughput with GPU count
  • Each GPU processes different requests
  • Simple load balancing

Serving Architecture

Request Routing

  • Load balancing across model replicas
  • Queue management for traffic spikes
  • Priority handling for different request types

Autoscaling

  • Scale GPU instances based on demand
  • Predictive scaling for known patterns
  • Spot instance strategies for cost optimization

Caching

  • Prompt cache for repeated prefixes
  • Semantic cache for similar queries
  • Result cache for deterministic outputs

Monitoring and Profiling

Key Metrics

  • GPU utilization percentage
  • Memory utilization
  • Throughput (tokens/second, requests/second)
  • Latency percentiles (p50, p95, p99)
  • Time to first token (for generation)

Profiling Tools

  • NVIDIA Nsight: Detailed kernel profiling
  • PyTorch Profiler: Python-level profiling
  • DCGM: Data center GPU management

Bottleneck Identification

  • Roofline model analysis
  • Memory bandwidth saturation
  • Compute utilization gaps
  • Data transfer overhead

Cost Optimization

Right-Sizing

  • Match GPU type to workload requirements
  • A10G for smaller models, H100 for largest
  • Consider memory vs. compute trade-offs

Spot Instances

  • 70-90% savings for fault-tolerant workloads
  • Implement graceful handling of interruption
  • Mix spot and on-demand for reliability

Reserved Capacity

  • Committed use discounts for predictable workloads
  • Long-term contracts with cloud providers
  • Balance flexibility vs. cost savings

Practical Optimization Process

  1. Profile current performance: Understand baseline and bottlenecks
  2. Apply low-effort optimizations: Batching, basic quantization
  3. Evaluate inference frameworks: Test vLLM, TGI, TensorRT-LLM
  4. Implement memory optimizations: Flash Attention, PagedAttention
  5. Consider model compression: Quantization, distillation if needed
  6. Optimize infrastructure: Right-sizing, autoscaling, caching

At Arazon, we optimize ML inference infrastructure to maximize performance while minimizing costs. Contact us to discuss how GPU optimization can improve your AI deployment efficiency.