Once you have quantized your Large Language Model, the challenge shifts to selecting the most suitable deployment framework to serve it efficiently. The framework acts as the engine that loads the quantized model, manages incoming requests, performs inference, and returns results. Your choice significantly impacts performance characteristics like throughput and latency, hardware utilization, ease of integration, and scalability. Several powerful frameworks cater specifically to LLM serving, each with distinct strengths, particularly when handling quantized models. Evaluating these options based on your specific requirements, target hardware, and the chosen quantization method is an important step.

Key Factors in Framework Selection

When evaluating deployment frameworks for quantized LLMs, consider these dimensions:

Quantization Support: How well does the framework integrate with different quantization formats and libraries? Does it offer native support for low-bit data types (e.g., INT4, FP8) and popular quantization algorithms (GPTQ, AWQ)? Or does it rely on external libraries or specific model conversion steps?
Performance: What are the typical throughput (requests per second) and latency (time per request) metrics for quantized models on your target hardware? Does the framework incorporate optimizations like continuous batching, paged attention, or custom kernel fusion that benefit quantized inference?
Hardware Compatibility: Is the framework optimized for specific hardware (e.g., NVIDIA GPUs), or does it offer broader compatibility across CPUs, different GPU vendors, or even edge devices?
Ease of Use and Integration: How complex is the setup and configuration? How straightforward is it to deploy a quantized model using the framework's API? What is the level of documentation and community support?
Feature Set: Does the framework offer specific features important for your application, such as support for multiple adapters, multi-modal inputs, or integration with specific MLOps ecosystems?

Let's examine some prominent frameworks through the lens of deploying quantized LLMs.

Text Generation Inference (TGI)

Developed by Hugging Face, Text Generation Inference (TGI) is a purpose-built solution for deploying LLMs at scale. It's designed for high throughput and low latency, incorporating features like tensor parallelism, continuous batching, and optimized transformers kernels.

Quantization Support: TGI readily integrates with quantization formats supported by the Hugging Face transformers library. This includes loading models quantized using bitsandbytes (NF4, INT8), GPTQ, and AWQ directly from the Hugging Face Hub or local paths, often with minimal configuration changes.
Performance: Offers good performance out-of-the-box, particularly leveraging continuous batching. Its optimizations are generally targeted at FP16/BF16, but quantized models benefit significantly from the efficient request handling and batching mechanisms.
Hardware Compatibility: Primarily focused on NVIDIA GPUs, but community efforts and underlying library support may extend compatibility.
Ease of Use: Relatively straightforward to set up, especially for models hosted on the Hugging Face Hub. Deployment often involves running a Docker container with specific model ID and quantization parameters.
Use Case: A strong choice for users heavily invested in the Hugging Face ecosystem who need a production-ready server with good support for common quantization methods (GPTQ, AWQ, bitsandbytes) without extensive manual optimization.

vLLM

vLLM is an open-source library designed specifically for fast LLM inference and serving. Its primary innovation is PagedAttention, an attention algorithm inspired by operating systems' virtual memory and paging concepts, which dramatically improves throughput by reducing memory waste.

Quantization Support: vLLM has native support for popular quantization schemes like AWQ. Support for other methods like GPTQ or bitsandbytes might require specific versions or integrations, and the landscape is evolving. It excels where memory efficiency from quantization combines with PagedAttention's memory management.
Performance: Often achieves state-of-the-art throughput, especially in scenarios with high concurrency, due to PagedAttention and continuous batching. It's particularly effective at maximizing GPU utilization.
Hardware Compatibility: Primarily targets NVIDIA GPUs (CUDA).
Ease of Use: Provides a Pythonic API for offline batch inference and an OpenAI-compatible server for online serving. Integration might be slightly more involved than TGI if not using natively supported quantization formats.
Use Case: Ideal when maximizing throughput on NVIDIA GPUs is the top priority, especially for applications involving dynamic batch sizes or long contexts. Excellent choice if using AWQ quantization.

NVIDIA TensorRT-LLM

TensorRT-LLM is an open-source library from NVIDIA built on top of NVIDIA TensorRT. It focuses on compiling LLMs into highly optimized runtime engines specifically for NVIDIA GPUs, maximizing inference performance through kernel fusion, layer optimizations, and optimized implementations for various precisions.

Quantization Support: Provides robust, first-class support for various quantization formats, including INT8 (SmoothQuant, per-tensor/per-channel), INT4 (AWQ, GPTQ implementations), and even FP8, directly within the TensorRT framework. It allows fine-grained control over precision settings.
Performance: Generally delivers the highest performance (lowest latency, highest throughput) on NVIDIA GPUs due to deep graph optimizations and specialized kernels tailored for the hardware architecture. Quantized models benefit significantly from these hardware-specific optimizations.
Hardware Compatibility: Exclusive to NVIDIA GPUs. Requires a specific build process to create the optimized engine for the target GPU architecture.
Ease of Use: Steeper learning curve compared to TGI or vLLM. Requires a model compilation step ("engine building") which can be time-consuming and requires careful configuration. Deployment involves using the built engine, often integrated with NVIDIA's Triton Inference Server.
Use Case: The best option when seeking peak inference performance on supported NVIDIA hardware and willing to invest the effort in the model compilation and deployment workflow. Essential for leveraging cutting-edge low-precision formats like FP8 or highly optimized INT4/INT8 implementations.

ONNX Runtime

ONNX Runtime is a cross-platform inference and training accelerator compatible with various execution providers (CPUs, NVIDIA GPUs, AMD GPUs, ARM, etc.). It uses the Open Neural Network Exchange (ONNX) format as its model standard.

Quantization Support: Supports quantized models in the ONNX format. Post-training quantization (static and dynamic) tools are available to convert FP32 ONNX models to INT8. Integration of more advanced LLM-specific quantization techniques (like GPTQ/AWQ) often requires converting the quantized model into an equivalent ONNX representation, potentially using formats like QOperator or QDQ (QuantizeLinear/DequantizeLinear). Support can vary depending on the target execution provider (e.g., TensorRT or CUDA Execution Providers for GPU acceleration).
Performance: Performance heavily depends on the chosen execution provider (EP). Using hardware-specific EPs (like the TensorRT EP or CUDA EP on NVIDIA GPUs) is necessary for competitive performance. Generic CPU performance is good, but achieving peak GPU performance comparable to TensorRT-LLM or vLLM might require more effort in model conversion and optimization tuning.
Hardware Compatibility: Its main strength. ONNX Runtime allows deploying models across a wide range of hardware targets, making it suitable for heterogeneous environments or edge deployments.
Ease of Use: Model conversion to ONNX, especially with complex quantization schemes, can be challenging. Once converted, the runtime API is relatively consistent across platforms.
Use Case: Suitable for scenarios requiring deployment across diverse hardware platforms (including CPUs and non-NVIDIA GPUs) or when integrating LLMs into existing applications that already use the ONNX standard.

Framework Comparison Summary

Feature	Text Generation Inference (TGI)	vLLM	TensorRT-LLM	ONNX Runtime
Primary Goal	Production Serving (HF focus)	High Throughput (GPU)	Peak Performance (NVIDIA)	Cross-Platform Inference
Quantization	Integrates HF libs (BnB, GPTQ, AWQ)	Native AWQ, others evolving	Native INT4/INT8/FP8 (GPTQ, AWQ)	ONNX INT8, QDQ conversion
Key Optimizations	Continuous Batching	PagedAttention, Cont. Batch	Kernel Fusion, Graph Opts	Execution Providers (EPs)
Performance	Good	Excellent Throughput	State-of-the-Art (NVIDIA)	EP Dependent
Hardware Focus	NVIDIA GPU (Primary)	NVIDIA GPU	NVIDIA GPU Only	CPU, GPU (Multi-vendor)
Ease of Use	High (esp. HF models)	Medium	Lower (Requires build step)	Medium (Conversion step)
Flexibility	Moderate	Moderate	Moderate (NVIDIA locked)	High (Hardware/Platform)

Making the Choice

Selecting the right framework involves balancing your priorities:

For rapid deployment and integration with the Hugging Face ecosystem using common quantization methods (GPTQ, AWQ, bitsandbytes): TGI is often the most direct path.
For maximizing request throughput on NVIDIA GPUs, particularly with AWQ: vLLM's PagedAttention offers significant advantages.
For achieving the absolute lowest latency and highest throughput on NVIDIA GPUs, leveraging advanced quantization (including FP8) and custom optimizations: TensorRT-LLM is the performance leader, though it requires more setup effort.
For deploying across diverse hardware (CPUs, multiple GPU vendors, edge) or standardizing on the ONNX format: ONNX Runtime provides the necessary flexibility, but may require careful model conversion and EP selection for optimal performance.

The landscape of LLM deployment frameworks is dynamic. New features, improved quantization support, and performance enhancements are continuously being developed. It's worthwhile to monitor the progress of these projects and re-evaluate your choice as your requirements or the frameworks themselves evolve. Having explored these options, the subsequent sections will provide practical guidance on implementing deployment using some of these key frameworks.