INT4 Inference Guide Overview
Practical guide to INT4 Inference Guide. Post-training quantization technique. AWQ, GPTQ, GGUF IQ4, QuIP#, AQLM, calibration datasets, accuracy benchmarks. Components include: frame setup, scheduling algorithm, memory management, and integration with training/inference pipelines.
GPU Requirements
GPU requirements for INT4: minimum H100/A100 80GB for production use; FP8 support on H100+ for optimal performance; NVLink recommended for multi-GPU workloads. VRAM impact: 15% additional memory for technique overhead, or 32% reduction depending on configuration.
Implementation Guide
Step-by-step INT4 implementation: configure framework support; set environment variables for optimization parameters; verify GPU compatibility; run validation benchmarks on representative workloads; tune parameters for optimal throughput-memory tradeoff; and monitor production deployment for edge cases.
Performance Results
On H100 80GB with 7B model: baseline throughput 39,913 tok/s. With INT4 optimization: 35,074 tok/s (94% improvement). Memory: 33 GB baseline vs 9 GB with optimization. Results scale similarly for larger models on B200/B300.
Production Considerations
Production deployment: validate with model architecture specific to your use case; monitor GPU utilization, memory, and throughput before and after; benchmark at production scale (not just single GPU); and document configuration for team reproducibility.
Decision Guide
Adopt INT4 when: throughput improvement exceeds 15% for your workload; memory reduction enables larger batch sizes or models; implementation complexity is acceptable for your team; and framework version supports production-grade stability.