SelfHostLLM is a GPU memory calculator designed for self-hosted LLM (Large Language Model) inference. It helps users calculate the GPU memory requirements and maximum concurrent requests for various LLMs including Llama, Qwen, DeepSeek, and Mistral. The tool supports different quantization levels and context lengths, enabling efficient planning of AI infrastructure. It provides detailed calculations for model memory, KV cache per request, and available memory for inference, along with performance estimations based on GPU memory bandwidth and model size efficiency.
Free
How to use SelfHostLLM?
To use SelfHostLLM, select your GPU model, specify the number of GPUs, and input the system overhead. Choose the LLM model you plan to use, adjust the quantization level, and set the context length. The calculator will then provide the maximum concurrent requests, total VRAM available, model memory required, and KV cache per request. It also estimates the expected speed and performance rating for your configuration.
SelfHostLLM 's Core Features
Supports a wide range of LLMs including Llama, Qwen, DeepSeek, and Mistral, allowing users to plan their AI infrastructure efficiently.
Calculates GPU memory requirements and maximum concurrent requests, providing a detailed breakdown of model memory, KV cache per request, and available memory for inference.
Offers performance estimations based on GPU memory bandwidth and model size efficiency, helping users understand the expected speed and performance rating of their configuration.
Supports different quantization levels and context lengths, enabling users to optimize their LLM inference for memory usage and performance.
Provides a user-friendly interface with detailed explanations for each calculation step, making it accessible for both beginners and experienced users.
SelfHostLLM 's Use Cases
AI researchers can use SelfHostLLM to estimate the hardware requirements for running different LLMs, helping them plan their experiments and deployments efficiently.
Developers working on AI applications can leverage the tool to understand the performance implications of different GPU configurations and model sizes, optimizing their applications for speed and cost.
IT professionals responsible for deploying AI infrastructure can use SelfHostLLM to plan their hardware purchases and configurations, ensuring they meet the requirements of their intended LLM workloads.
Educators teaching AI and machine learning can use the tool as a practical example to explain the hardware considerations involved in running large language models.
Startups and small businesses exploring AI can use SelfHostLLM to estimate the costs and hardware requirements for integrating LLMs into their products or services.
