GPU Inference Memory Calculator MCP, Ready to Go
Use the GPU Inference Memory Calculator with Claude or Cursor to plan VRAM for LLM inference, optimize batch sizes, and avoid out-of-memory errors.
No credit card required. Experience the power of this integration risk-free.
Plan your VRAM requirements and batch capacities for LLM deployment
Works with every AI agent you already use
…and any MCP-compatible client
How fast is the GPU Inference Memory Calculator MCP Server?
Average time for the server to become ready for requests over the last 3 days, measured until the initialize / tools/list handshake completes. Metrics are updated daily between 00:00 and 04:00 UTC. Create a free account, use this MCP on Vinkius Cloud, and connect it to your AI agent in seconds.
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What AI agents can do with GPU Inference Memory Calculator: 4 Tools for VRAM Planning
Calculate weight sizes, KV cache overhead, and max batch capacity for your LLM inference hardware to plan your production environment accurately.
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.
One MCP enables access. Vinkius turns MCPs into production-ready infrastructure.
You're looking at one of 5,800+ managed MCPs. The real value isn't the catalog. It's the control plane that secures, governs, audits, and manages every interaction between your agents and the tools they use.
No Shadow AI
Every agent action is visible, approved, and auditable. Nothing runs outside your governance.
Absolute agent control
Fine-grained permissions for every agent, MCP, and tool. Instantly revoke access and audit every execution.
Cost control per token
Spend broken down to the token, tool, and agent. Budgets and hard limits. No surprise invoices.
Managed & monitored infra
We operate the runtime, authentication, scaling, retries, and monitoring. Your team manages AI, not infrastructure.
Data protection, DLP by design
Sensitive data is filtered before reaching the model. Access is governed so agents receive only the information they're allowed to use.
Token optimization, real savings
Lower AI costs by delivering the right context instead of unnecessary tools. Better accuracy, faster responses, and fewer wasted tokens.
GPU Inference Memory Calculator for Accurate LLM Hardware Planning
This is for ML engineers and infrastructure leads who need to plan hardware for LLM deployment. It's for the person who needs to know if a 70B model will actually fit on their existing cluster without crashing.
MLOps Engineer
Planning production inference scaling and choosing the right GPU instances for a fleet.
AI Researcher
Testing different quantization levels to see how they affect memory and speed on local hardware.
Hardware Architect
Mapping out the hardware budget for a new company-wide LLM project and estimating costs.
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.
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