How to Choose the Best GPU for LLM: A Practical Guide

The right LLM GPU requirements for your large language model (LLM) workloads are critical for achieving high performance and efficiency. This practical guide will walk you through evaluating and selecting the best GPUs for LLM. Whether you're looking for a GPU for LLM fine-tuning or deploying an LLM for inference tasks, we’ve got you covered.  

Cloud GPU providers offer powerful, on-demand access to high-performance GPUs like the NVIDIA A100 and H100, ideal for training, fine-tuning and inference at scale.

This practical LLM GPU buying guide will walk you through evaluating and selecting the best GPUs for LLM.

GPU for LLM Inference

For a detailed overview of suggested GPU configurations for inference LLMs with various model sizes and precision levels, refer to the table below. This shows the suggested best GPU for LLM inference for the latest Llama-3-70B model and the older Llama-2-7B model.

GPU for LLM Training

For a detailed overview of suggested GPU configurations for fine-tuning LLMs with various model sizes, precisions and fine-tuning techniques, refer to the table below. This shows the suggested GPU for the latest Llama-3-70B model and the older Llama-2-7B model.

Fine-tuning an LLM

If you're fine-tuning an existing open-source LLM, several design decisions will impact the NVIDIA GPU for LLM training requirements:

Fine-tuning Technique

Start with parameter-efficient fine-tuning methods like LoRA or QLoRA instead of full model fine-tuning, as they use less GPU memory. These methods significantly reduce the GPU memory requirements compared to fully supervised fine-tuning. HuggingFace provides a simple Python package to get started with Peft.

Model Size

Model size, measured in billions of parameters, directly impacts GPU memory needs. Most open-source models offer both smaller (e.g., Llama-3 8B) and larger (e.g., Llama-3 70B) versions. Larger AI models require more GPU memory. Generally, start with smaller models tailored to your use case and scale up as needed. 

Precision

Precision determines the model's numerical format, such as float32, float16, bfloat16, int8, or int4. Lower precision reduces GPU memory usage but may affect accuracy. For large models, mixed or quantised precision is recommended to optimise memory use.

Batch Size

Batch size is the number of samples processed simultaneously. Larger batches improve throughput but require more GPU memory, so balance size for optimal utilisation.

Inferencing an LLM

When deploying an LLM for text generation or other AI inference tasks, choosing the right GPU for LLM inference is crucial:

Inference Engine

Using an optimised inference engine like vLLM, TGI or Triton can significantly improve inference speeds and efficiency. These engines leverage techniques like kernel fusion, operator optimisation and quantisation to accelerate inference on GPUs.

Model Size

Similar to fine-tuning, the size of the LLM (in billions of parametres) directly impacts the GPU memory requirements for inference tasks. Larger models require more GPU memory for efficient inference.

Model Architecture

Mixture of Experts (MoE) architectures use sparse computations for faster inference. For example, Mixtral 8x22B has 141B parameters but only uses 39B during inference, though all 141B must still fit in memory.

Batching

Batching multiple inference requests together can improve throughput and GPU utilisation. However, larger batch sizes also increase memory requirements, so finding the optimal balance between batch size and memory usage is essential for efficient inference.

Input and Output Lengths

The expected lengths of input texts and generated outputs can affect GPU memory requirements and inference latency. Longer input texts generally require more GPU memory, while longer output texts increase the time spent on text generation, potentially impacting inference performance.

The recommendations here are just rough estimates to get you started. You'll want to do your thorough testing and benchmarking to test an LLM's performance and resource needs for different GPU setups. Want to know more about GPU selection and LLMs? Check our presentation at NVIDIA GTC 2024, one of the world’s largest AI conferences.

Not sure about which configuration is best for your model and requirements?

Get started with our GPU Selector Tool to find the ideal LLM GPU for fine-tuning and inference, tailored to your project's needs. 

FAQs

How to choose the best LLM GPU?

There is no one-size-fits-all solution when It comes to the best GPU for LLM workloads. It's essential to evaluate your unique needs, constraints and long-term goals to ensure that your GPU infrastructure can support the demanding workloads of these large models. 

Which is the best GPU for LLM training?

NVIDIA A100, NVIDIA H100 SXM and NVIDIA H100 PCIe are some of the best NVIDIA GPU for LLM training.

Which is the best GPU for fine-tuning LLM?

For a detailed overview of suggested LLM GPU for fine-tuning LLMs with various model sizes, precisions and fine-tuning techniques, refer to the bullets below. 

• For full fine-tuning with float32 precision on Meta-Llama-3-70B model, the suggested GPU is 8x NVIDIA A100 (x2).
• For full fine-tuning with float16/float16 precision on Meta-Llama-3-70B model, the suggested GPU is 4x NVIDIA A100.
• For full fine-tuning with float32 precision on the smaller Meta-Llama-2-7B model, the suggested GPU is 2x NVIDIA A100.
• For full fine-tuning with float16/float16 precision on Meta-Llama-2-7B, the recommended GPU is 1x NVIDIA RTX-A6000.

Which is the best GPU for inferencing LLM?

For the largest most recent Meta-Llama-3-70B model, the recommended GPU for LLM inference includes:

• For float32 precision, the recommended GPU is 4xA100
• For float16/float16 precision, the recommended GPU is 2xA100
• For int8 precision, the recommended GPU is 2xRTX-A6000
• For int4 precision, the recommended GPU is 1xRTX-A6000

For the smaller and older Meta-Llama-2-7B model:

• For float32 precision, the recommended GPU is 1xRTX-A6000
• For float16/float16 precision, the recommended GPU is 1xRTX-A6000

What are LLM GPU requirements?

Consider VRAM, tensor performance, bandwidth, NVLink support and efficiency, matched to training or inference workload requirements.

How much VRAM do large LLMs require?

It depends on model size and usage. Check out our blog here for detailed VRAM guidance across different LLM workloads.

Which GPUs are best for inference?

NVIDIA A100 is a top choice for inference, offering great performance at a lower price.