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Compiling a LLM for High Performance Inference

This is the next blog in the blog series on LLMs and Inference. In the previous blog on LLMs and Inference, we discussed about the safetensors format for LLMs. In this blog, we will walk through a critical step for LLM Inference.

Compiling a Large Language Model (LLM) generally refers to optimizing the model’s computational graph and kernel execution to improve inference or training performance on specific hardware (like GPUs or TPUs). Think of this as the next logical step that is performed after loading a model.

LLM Compilation

What Does “Compiling” an LLM Involve?

In the context of LLMs, compile does not mean compiling like C code into a binary. Instead, compiling a LLM involves the following:

1. Graph Optimization

Frameworks like TensorRT and ONNX Runtime analyze the LLM’s operations (matrix multiplies, attention layers, layer norms, etc.). Redundant operations are fused (e.g., combining multiple layers into one kernel), constants can be folded and unused paths are removed.

2. Hardware-Specific Kernel Generation

If you know the type of AI accelerator you plan to deploy on, then kernels (low-level operations) can be optimized for the target hardware (e.g., NVIDIA GPUs, AMD, TPUs). CUDA or Triton-based code is generated for max throughput. Finally, memory layout and precision (e.g. FP16, BF16, INT8) are tuned for speed and compatibility.

3. Static or Dynamic Quantization

During or after compilation, the model is generally reduced to lower precision formats like INT4 or FP8. This can reduce memory requirements and can speed up inference dramatically without hurting accuracy too much.

Why Compile an LLM?

In general, there are three major benefits you can expect by compiling a LLM

Faster Inference

By compiling a LLM, you can reduce the latency and deliver a better throughput (i.e. more tokens/sec)

Lower Memory Usage

By compiling a LLM, you can fit larger models into a smaller GPU's VRAM. A smaller GPU will definitely cost less and may be easier to acquire.

Better GPU Utilization

By compiling a LLM, you can ensure your GPUs are maxed out. There is no value in under utilizing an expensive GPU resource.

Examples of LLM Compilation Frameworks

There are several LLM compilation frameworks. There are two that are extremely popular and can be considered market leaders.

NVIDIA TensorRT-LLM

TensorRT-LLM is NVIDIA’s high-performance inference framework optimized for serving large language models with ultra-low latency and high throughput on GPUs This open source project is an extremely specialized library primarily designed for high end Nvidia H100 and A100 GPUs, with mixed-precision and fused attention kernels.

It supports advanced features like kernel fusion, quantization (FP8/INT8), and tensor parallelism for efficient deployment of 10B+ parameter models.

vLLM

vLLM is an open-source LLM inference engine designed for high-throughput, low-latency serving using a novel PagedAttention mechanism. It enables efficient GPU memory management and dynamic batching, making it ideal for serving large models at scale. vLLM automatically compiles models into paged memory + efficient attention layout.

Conclusion

Compiling an LLM means transforming the model into an optimized version that runs faster, more efficiently, and closer to the metal on your target hardware. It is a key step for scalable inference and production deployment. In the next blog, we will discuss about quantization and how it helps Inference.