# GPU Inference Memory Calculator MCP for AI Agents MCP > GPU Inference Memory Calculator helps you figure out how much VRAM you need to run an LLM. It handles weight sizes, KV cache, and batch scaling so you don't overspend on hardware or run out of memory during production. It's built for engineers who need to know exactly what their hardware limits are before they hit deploy. It takes the guesswork out of infrastructure planning. ## Overview - **Category:** llm-inference - **Price:** Free - **Endpoint:** https://edge.vinkius.com/vk_preview_Po32HLoFfJyACkPs6caCBt7wRipx1gN5av2RafWO/mcp - **Tags:** gpu, vram, llm, inference, memory-estimation ## Description Imagine you're trying to deploy a new model and you aren't sure if your current hardware can handle the load. Usually, this involves a mess of manual math, checking different precision types like FP16 or INT4, and guessing how many concurrent users you can actually support. It's a common headache for anyone moving from a local prototype to a production environment. You don't want to spend a week on manual calculations only to find out your system crashes the moment it hits real traffic. This MCP takes that guesswork out of the equation. It acts as a specialized engine that calculates the exact memory footprint for your specific setup. You can see how much space the weights take up before you even load them, check the overhead for your KV cache, and get a clear picture of how many requests your GPU can actually process at once. It's about making sure your production environment doesn't crash because you underestimated the batch size. By using this through the Vinkius catalog, you get a reliable way to plan your infrastructure without the headache of manual formulas. It helps you stay within your hardware budget while maximizing the throughput of your inference setup. You won't have to worry about hidden memory spikes or unexpected crashes when your user count climbs. You just get the facts on what your hardware can actually do, allowing you to make confident decisions about your stack. ## Tools ### calculate_max_batch_capacity Determines the largest batch size that fits within a specific GPU's memory limit. Use this to find your peak concurrent capacity. ### estimate_total_vram Calculates the total VRAM required to run a specific batch size. This helps you predict crashes before they happen. ### get_kv_cache_per_request Calculates the memory footprint of the Key-Value cache for a single request. This accounts for context overhead. ### get_model_weights_size Calculates the amount of VRAM required just to load the model's parameters. It supports multiple precision types. ## Prompt Examples **Prompt:** ``` I have a 70B model and 24GB of VRAM. Can I run it in FP16? ``` **Response:** ``` No, a 70B model in FP16 needs about 140GB of VRAM just for the weights. You'll need to look at a lower precision like INT4 or use a much larger GPU. ``` **Prompt:** ``` What's the KV cache for a 32-layer model with a 4096 context length in BF16? ``` **Response:** ``` The KV cache for that setup is about 0.53 GB per request. This means your memory usage will grow as your context length increases. ``` **Prompt:** ``` If my model weights take 14GB and I have 24GB of VRAM, what's my max batch size if the KV cache is 0.5GB per request? ``` **Response:** ``` Based on those numbers, you can run a maximum of 20 concurrent requests before hitting your 24GB limit. ``` ## Capabilities ### Check weight memory See how much VRAM a model needs just to load its parameters based on your chosen precision. ### Estimate KV cache Find out the memory footprint for a single request's context overhead. ### Calculate total VRAM Get the full memory requirements for a specific batch size for a given model. ### Find max batch size Determine the highest number of concurrent requests your GPU can handle without crashing. ## Use Cases ### Checking if a 70B model fits on a 24GB card A developer asks their agent if a specific model will run on a consumer GPU. The agent uses the MCP to check weight sizes and precision limits. ### Planning a production rollout An engineer needs to know how many concurrent users they can support on a specific A100 cluster. They ask the agent to calculate max batch capacity. ### Comparing quantization trade-offs A researcher wants to see the VRAM difference between BF16 and INT8 for a specific model. They use the MCP to get a side-by-side comparison. ### Budgeting for new hardware A manager needs a list of required VRAM for three different models to decide which GPUs to buy. The agent pulls the numbers for all three setups. ## Benefits - Stop guessing your hardware needs. Use `get_model_weights_size` to know exactly what your model requires before you try to deploy it. - Optimize your GPU spend. Use `calculate_max_batch_capacity` to ensure you're getting the most out of every card you own. - Plan for higher concurrency. `get_kv_cache_per_request` helps you understand how context length affects your scaling limits. - Avoid out-of-memory errors. Use `estimate_total_vram` to predict crashes before they happen in your production environment. - Make faster hardware decisions. You can quickly compare different precision types to see which one fits your budget best. ## How It Works The bottom line is you get accurate hardware requirements for LLM deployment without doing the math yourself. 1. Connect the MCP to your AI client like Claude or Cursor. 2. Provide the model parameters, precision, and hardware limits to your agent. 3. Get a breakdown of VRAM requirements and batch capacity. ## Frequently Asked Questions **Can the GPU Inference Memory Calculator help me choose the right GPU?** Yes, it shows you the exact VRAM needs for different models and precisions so you can pick the right hardware for your needs. **Does this tool actually run the LLM for me?** No, it only calculates the memory requirements. You'll still need a separate inference engine to run the model. **How does the GPU Inference Memory Calculator handle different precisions?** You can specify types like FP32, FP16, BF16, INT8, or INT4 to see how much memory each one saves for your specific model. **Can I use this to see how many users I can support?** You can use it to find the max batch capacity, which tells you how many concurrent requests your hardware can handle at once. **Is this useful for production planning?** It's perfect for production because it helps you avoid out-of-memory crashes by predicting the total VRAM needed for your specific batch size. **What's the difference between weight size and KV cache?** The weight size is just for loading the model, while the KV cache is the extra memory needed for the actual conversation context. **What precision modes are supported?** The calculator supports FP32, FP16, BF16, INT8, and INT4 precision modes. **How do I calculate the total VRAM for a batch?** You can use the `estimate_total_vram` tool by providing the pre-calculated weight memory, KV cache size per request, and your desired batch size. **Can I determine the maximum number of concurrent requests for my GPU?** Yes, use `calculate_max_batch_capacity` by providing your total VRAM budget, model weight memory, and KV cache size per request.