To achieve near-human levels of text comprehension and generation, LLMs need to be built with billions of parameters (as noted in Kaplan et al. and Wei et al studies) This significantly increases the memory requirements during inference. Even some real-world scenarios demand LLMs to process extensive contextual information requiring the models to handle long input sequences during inference. The core challenge lies in improving the computational and memory capacity of LLMs to run smoothly. In this guide, we will explore 5 ways to optimise LLMs on Hyperstack.
Below are 5 best ways to optimise LLMs to run your models efficiently:
Choosing the right GPU is one of the most important steps in optimising LLMs. A faster GPU speeds up training and inference times, hence resulting in faster response times. For example, using an impotent GPU such as the NVIDIA GTX series for a large LLM like Llama 3- 70B can result in prolonged processing times and unnecessary resource consumption. This is because the GTX series lacks the necessary Tensor cores and high memory bandwidth needed to handle the billions of parameters and extensive computations required by models LLMs. On the other hand, using high-end GPUs like the NVIDIA A100 and NVIDIA H100 designed for AI workloads with high memory bandwidth and NVLink support can handle LLMs efficiently.
Try our LLM GPU Selector to Find the Right GPU for Your Needs
LLMs vary greatly in size and architecture and choosing a more efficient model can reduce the computational burden. For instance, while larger models offer improved accuracy, they require significantly more resources to train and deploy. In many cases, using a smaller, more efficient model with Mixture of Expert (MoE) architecture can show similar results at a fraction of the cost.
MoE models, for example, route only a subset of their parameters during inference, allowing you to use fewer computational resources without sacrificing performance. These models are ideal when you need to balance accuracy with cost efficiency, making them suitable for a range of tasks from text summarisation to real-time user interactions.
The software stack you use to manage your LLM also plays an imperative role in its performance. Using optimised libraries and packages for both training and inference can improve resource efficiency and execution speed. For inference, tools like vLLM provide key optimisations such as KV-cache for faster and more memory-efficient model execution.
Similarly, when fine-tuning LLMs, frameworks like Hugging Face's PEFT (Parameter-Efficient Fine-Tuning) enable quick and effective fine-tuning of models with fewer parameters, reducing the amount of GPU memory required. These libraries help in minimising training and inference times, making it easier to deploy your models.
Fine-tuning hyperparameters such as batch size, learning rate and the number of training epochs can significantly impact the performance of your LLM. Optimising these parameters helps ensure that the model makes the most efficient use of GPU resources during both training and inference.
Similar Read: Static vs. Continuous Batching for LLM Inference
Leveraging advanced techniques can dramatically improve the performance of LLMs, especially when dealing with large-scale models or tight resource constraints. Techniques such as quantisation and low-rank adaptation (LoRa) allow for more efficient training and inference without compromising model accuracy.
Quantisation is particularly useful for reducing the precision of model parameters (e.g., from 32-bit to 16-bit), which can lower the computational load and reduce memory requirements, speeding up both training and inference. This is particularly effective when deploying models at scale, where even small gains in efficiency can lead to significant cost reductions.
LoRa adapters are used during training to reduce the number of parameters that need to be updated, making fine-tuning more efficient. These techniques maintain high model performance while reducing the time and resources required for training and deployment.
Optimising LLMs involves a diverse approach, from selecting the right hardware to fine-tuning hyperparameters and using advanced techniques. At Hyperstack, we understand the need to optimise LLMs for better performance and hence offer high-performance GPUs like the NVIDIA A100, NVIDIA H100 PCIe and NVIDIA H100 SXM, designed specifically for LLMs. We also offer flexible GPU pricing and easy deployment options so you can LLMs run more efficiently, delivering faster responses and reducing operational costs.
Get Started on Hyperstack to Access the Best GPUs for your LLM needs!
Selecting the right GPU ensures faster processing times, better memory management, and improved performance, especially for large models like GPT-4 or Llama.
vLLM optimises inference by using techniques like KV-cache, which speeds up execution and makes model deployment more memory-efficient.
Smaller LLMs, or those using efficient architectures like Mixture of Experts, require fewer resources and can still deliver strong performance for many tasks, making them cost-effective.
Hyperstack provides access to high-performance GPUs such as the NVIDIA A100 and NVIDIA H100, tailored for LLM workloads, along with flexible pricing and fast deployment options.