Practical Guide to LLM Inference Optimization: A Comprehensive Benchmarking Solution from Quantization Formats to Production Deployment

This article introduces the open-source project inference-optimization-bench, which provides a complete benchmarking framework for GPU-accelerated LLM inference. It covers comparisons of mainstream quantization formats such as GGUF/AWQ/GPTQ, TensorRT-LLM integration practices, and production-grade deployment solutions using Docker and Kubernetes, helping developers master end-to-end optimization strategies from quantization techniques to deployment.

With the widespread application of LLMs, inference performance and cost have become bottlenecks for implementation. An optimized system can reduce latency by 10x, increase throughput by 5x, and reduce GPU consumption. inference-optimization-bench is an open-source GPU-accelerated LLM inference benchmarking suite with core features including multi-format quantization support, TensorRT-LLM integration, performance visualization, cloud-native deployment, and a modular architecture.

Quantization is a core technology for inference optimization. Below is a comparison of three mainstream quantization formats:

• GGUF: The standard for the llama.cpp ecosystem, supporting multiple quantization levels, optimized for ARM/AVX, suitable for edge devices and consumer GPUs.
• AWQ: Activation-aware weight quantization that protects weights with a large impact on output. At 4-bit, it approaches FP16 precision, making it suitable for high-accuracy scenarios.
• GPTQ: Quantization based on approximate second-order information, supporting flexible configurations from 2-bit to 8-bit. 4-bit can achieve 4x compression with almost no performance loss.

TensorRT-LLM is an SDK optimized by NVIDIA specifically for LLM inference. Key integration points include: converting models to TensorRT engines, enabling efficient kernels like FlashAttention/MQA, configuring in-flight batching, and KV cache management. On A100/H100 GPUs, it can increase throughput by 2-4x and reduce latency by over 50% compared to native PyTorch.

The project provides production-grade deployment solutions:

• Docker Containerization: Multi-stage Dockerfile builds, including CUDA environment, quantization toolchain, TensorRT-LLM dependencies, and monitoring agents.
• Kubernetes Orchestration: Provides Deployment (supports HPA), Service (load balancing), ConfigMap (dynamic parameter adjustment), PersistentVolumeClaim (model caching), and Prometheus monitoring (metrics like GPU utilization).

Key benchmarking metrics include Time to First Token (TTFT), throughput, end-to-end latency, and memory efficiency. Test scenarios are designed to cover different sequence lengths (128-8192), concurrency pressure (10-1000 users), long text generation, and mixed loads.

Quantization Format Selection Decision Tree:

• Extreme Speed: GGUF Q4_0 + llama.cpp
• Balanced Precision and Efficiency: AWQ 4-bit
• NVIDIA GPU Exclusive: TensorRT-LLM + GPTQ
• Multi-GPU Parallelism: TensorRT-LLM's TP/PP

Deployment Strategies:

• Development and Testing: Local Docker deployment to verify configurations
• Small-Scale Production: Single-node K8s + HPA
• Large-Scale Services: Multi-node GPU cluster + Service Mesh

inference-optimization-bench provides a systematic testing framework covering the complete chain from quantization to deployment, helping developers make technical selection decisions. Future directions include supporting more quantization schemes (e.g., GGUF Q6_K/Q8_K), integrating vLLM, adding multimodal support, and introducing a cost analysis module. Mastering these optimization techniques is key to enhancing the competitiveness of LLM applications.