A Quick Guide to Troubleshooting Most Common LLM Issues

Many developers await the opportunity to experiment with the latest LLMs, like the recently released Llama 3.1 model, only to be blocked by unexpected challenges. Have you faced memory or other issues when deploying a large language model?  It’s an experience many developers know all too well. The excitement of leveraging such powerful AI models can quickly be overshadowed by the challenges of managing their intensive resource requirements and fine-tuning their performance. So, how do you tackle these challenges and make the most of your model? Continue reading as we’ll explore the most common troubleshooting issues and learn ways to solve them. 

Most Common LLM Issues

Here are the most common issues that you may face when working with an LLM:

1. Memory Constraints: LLMs require a significant amount of memory to load and run efficiently. Many users face out-of-memory errors when trying to deploy large models on systems with insufficient VRAM. The issue is particularly prevalent when working with large versions of LLMs like Llama3.1-70B.
2. CUDA-related Problems: NVIDIA's CUDA toolkit is essential for GPU acceleration of LLMs. However, users often encounter CUDA-related issues, such as version incompatibilities, driver conflicts or improper CUDA installations. These problems can lead to suboptimal performance or complete failure to utilise high-end GPU resources.
3. Model Intricacies: While most large language models are available through HuggingFace, their implementation and architecture can vary slightly. This might cause differences in tokenizers or padding tokens. To address these issues and start your project quickly, you can use packages like vLLM and TensorRT.

How to Troubleshoot LLM Issues?

Here’s how you can troubleshoot the above-mentioned LLM issues:

Memory Constraints

To counter memory constraints, you can:

1. Choose the Right GPU for LLM: An appropriate GPU is imperative for handling memory-intensive LLMs. Our LLM selector calculator can help you determine the optimal GPU based on your model size and requirements. Our tool considers factors such as model parameters, batch size and desired inference speed to recommend suitable hardware configurations. Try Hyperstack GPU Selector For LLMs today!
2. Implement Model Quantisation: Quantisation techniques can significantly reduce memory usage by converting model weights from 32-bit floating-point to lower-precision formats for example 16-bit or 8-bit. You can try libraries like Hugging Face's Optimum or vLLM to apply quantisation while maintaining model performance.
3. Reduce Context Length: When using models with key-value caches, you can reduce the context length to manage memory usage. This involves truncating input sequences or implementing sliding window techniques to process longer texts in chunks.

CUDA-related Problems

To solve issues related to CUDA, you can:

1. Verify CUDA Installation: Ensure that CUDA is properly installed and configured on your system. Use the nvidia-smi command to check the installed CUDA version and compare it with the requirements of your deep learning framework.
2. Check CUDA Compatibility: Verify that your CUDA version is compatible with both your GPU driver and the deep learning framework you're using. You can refer to the compatibility matrix provided by NVIDIA and your framework's documentation.

With Hyperstack, you don't have to worry about setting up CUDA drivers, as our provided images come with them pre-installed so you can get started right away. Learn more here. 

Model Intricacies

To resolve issues related to model intricacies, particularly during inference, we recommend using one of the following packages:

• vLLM: This package allows you to quickly set up an LLM API endpoint. With their pre-built Docker images, you can get started easily. For more details, you can refer to our Llama 3.1 tutorial.
• TensorRT: NVIDIA offers TensorRT, a package that includes various optimisations. Although the installation process can be somewhat complex, it could be worth the investment. You may also consider exploring NVIDIA NIMs which come with pre-packaged API endpoints for easier deployment.

Conclusion

As exciting as it is to experiment with the latest AI models, it’s important to address the challenges to deliver successful outcomes. By understanding and troubleshooting common LLM issues, you can improve model performance and ensure smoother deployments. At Hyperstack, we understand your AI needs. On our easy-to-use platform, you can get started and deploy any workload in the cloud on the latest infrastructure and you only pay for what you consume. This way, you can focus on experimenting with new models without worrying about budget constraints. Check out our cloud GPU pricing here to learn more!

Sign up now to get started with Hyperstack. To learn more, you can watch our platform demo video below:

FAQs

Which is the most common LLM issue? 

Memory constraints are the most common issue which often results in out-of-memory errors during deployment on systems with insufficient VRAM.

Which is the best GPU for LLM? 

Hyperstack offers a range of high-end NVIDIA GPUs ideal for training and deploying large language models. We recommend using powerful cloud GPU for LLM like the NVIDIA A100 and NVIDIA H100 SXM with high computing performance needed to tackle the most demanding LLM workloads. 

How much VRAM is required to run LLMs? 

The amount of VRAM required to run LLMs depends on the size of the model. For inference at fp16 precision:

• A 7B parameter model requires approximately 15GB of VRAM.
• A 70B parameter model demands around 150GB of VRAM.

Smaller models may be manageable with less VRAM but having more memory generally results in better performance when dealing with larger models.