Quantization promises significant improvements in model size and computational efficiency, but the actual performance gains measured in latency and throughput are highly dependent on the underlying hardware executing the inference. A model quantized to INT4 might show impressive theoretical reductions in operations, yet yield minimal speedup on hardware lacking optimized INT4 compute kernels. Therefore, analyzing performance specifically on your target hardware is not just recommended. it's a fundamental part of the evaluation process.

Different hardware platforms, primarily CPUs and GPUs, possess distinct architectures and instruction sets that interact differently with quantized data types and operations. Understanding these interactions is essential for predicting and interpreting performance results.

CPU Inference Performance

While high-performance LLM inference typically relies on GPUs, CPUs remain relevant for certain deployment scenarios, such as edge devices, development environments, or cost-sensitive applications. However, CPUs generally exhibit more modest speedups from quantization compared to GPUs.

Instruction Set Support: Modern CPUs often include Single Instruction, Multiple Data (SIMD) extensions like AVX2 or AVX-512. Some newer generations incorporate specialized instructions for accelerating lower-precision integer arithmetic, such as AVX-512 VNNI (Vector Neural Network Instructions), which significantly boosts INT8 performance. Quantization to INT8 can leverage these instructions, leading to noticeable latency reductions. However, support for sub-8-bit integer operations (like INT4) is less common in hardware instructions, meaning performance gains often come primarily from reduced memory bandwidth usage rather than faster computation.
Memory Bandwidth and Cache: LLM inference can be memory-bandwidth bound, especially on CPUs. Quantization reduces the model size, leading to better utilization of CPU caches (L1, L2, L3) and lower pressure on the main memory bus. This effect contributes to speedups even without specialized compute instructions, particularly for lower bit-widths.
Parallelism: CPUs have far fewer parallel execution cores compared to GPUs. While techniques like multi-threading help, the inherently sequential nature of some model parts and the limited core count cap the achievable throughput, even with quantization.
Software Libraries: Performance heavily depends on the software library used for inference (e.g., ONNX Runtime, Intel oneDNN, PyTorch with specific backends). These libraries contain optimized kernels for various operations and data types. The availability and quality of these kernels for specific CPU architectures and quantization schemes (e.g., INT8 vs. INT4, symmetric vs. asymmetric) directly impact the observed speedup.

When benchmarking on CPUs, pay close attention to the specific CPU model, its supported instruction sets, and the inference library's configuration. Small changes in these factors can lead to significant performance variations.

GPU Inference Performance

GPUs are the workhorses for LLM inference due to their massively parallel architecture, designed for the types of matrix multiplications and element-wise operations prevalent in deep learning.

Specialized Cores (e.g., Tensor Cores): Modern GPUs (especially NVIDIA's) feature specialized hardware units like Tensor Cores, designed to accelerate matrix multiplication at specific precisions. These cores offer substantial performance boosts for FP16, TF32, and INT8 arithmetic. Newer architectures (like Hopper) introduce support for even lower precisions like FP8. Leveraging these cores is often the primary driver of speedups from quantization on GPUs. If a quantization scheme (e.g., INT4) requires emulation or doesn't map efficiently to these cores, the computational speedup might be limited, although memory savings will still apply.
CUDA Kernels and Libraries: GPU performance is dictated by the efficiency of the underlying CUDA kernels. Libraries like cuBLAS, cuDNN, and specialized LLM inference libraries such as NVIDIA TensorRT-LLM or vLLM contain highly optimized kernels for different operations, data types, and GPU architectures. The degree of optimization for a specific quantization format (e.g., GPTQ INT4, AWQ INT4) within these libraries determines the achievable performance. Lack of optimized kernels for a specific low-bit format on a particular GPU architecture can be a major bottleneck.
Memory Bandwidth: Like CPUs, GPUs are also sensitive to memory bandwidth. Large LLMs can easily saturate the GPU's memory bus (HBM or GDDR). Quantization significantly reduces the amount of data transferred between the GPU's main memory and its compute units, alleviating this bottleneck and contributing substantially to faster inference, especially for large models or long sequences.
Architecture Generations: Performance characteristics vary significantly across GPU generations (e.g., NVIDIA's Pascal, Volta, Turing, Ampere, Hopper, Blackwell). Newer generations typically offer better support and higher performance for lower-precision formats. Benchmarking results from one generation may not directly translate to another.

Benchmarking Across Hardware

Because performance is so tightly coupled with hardware specifics, direct comparison is essential. Consider benchmarking your baseline model (e.g., FP16 or BF16) and various quantized versions (e.g., INT8, INT4 using GPTQ/AWQ) across your potential target hardware platforms.

Key factors to control and observe during hardware-specific benchmarking include:

Hardware: Specific CPU model, GPU model (e.g., A100, H100, L40S, RTX 4090).
Software: Inference framework (TensorRT-LLM, vLLM, TGI, ONNX Runtime), CUDA version, driver version, quantization library version.
Workload: Batch size, input sequence length, output sequence length.
Metrics: Latency (per token, end-to-end), throughput (tokens/sec), memory usage (peak GPU/CPU RAM), power consumption (if relevant).

The following chart illustrates hypothetical latency variations for a given LLM task across different hardware and quantization levels.

Hypothetical comparison showing inference latency decreasing with more aggressive quantization (FP16 > INT8 > INT4) and significantly lower latencies on GPUs compared to CPUs. Note the diminishing returns for INT4 relative to INT8, potentially due to kernel optimization levels, especially on the mid-range GPU. High-end GPUs show the most dramatic improvements due to superior hardware support.

Ultimately, theoretical benefits must be validated empirically. Performance analysis on the target hardware provides the ground truth for deciding which quantization strategy offers the best trade-off between efficiency gains and potential accuracy degradation for your specific deployment context. Relying solely on reported benchmarks from different hardware or configurations can lead to inaccurate expectations and suboptimal deployment choices.