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GPU Inference Memory Calculator

GPU Inference Memory Calculator MCP Connector for Claude

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Estimate GPU VRAM requirements for LLM inference based on model parameters, precision, and batch size.

4 tools Official Updated Oct 1, 2026 Official Vinkius Partner
More Details Connect to Claude

This MCP server provides a specialized engine for calculating the precise GPU memory footprint required for Large Language Model (LLM) inference. It allows you to determine model weight memory, KV cache size per request, total VRAM usage for specific batch sizes, and the maximum concurrent batch capacity that fits within your hardware budget. Use get_model_weights_size to find parameter memory, get_kv_cache_per_request for architectural overhead, estimate_total_vram for workload scaling, and calculate_max_batch_capacity to optimize GPU utilization.

gpuvramllminferencememory-estimation

4 tools expose this connector's capabilities to your AI agent.

estimate_total_vram

Calculates the total VRAM required to run a specific batch size

calculate_max_batch_capacity

Determines the largest possible batch size that can fit within a specific GPU's memory limit

get_kv_cache_per_request

Calculates the memory footprint of the Key-Value cache for a single inference request

get_model_weights_size

g., FP32, FP16, BF16, INT8, INT4). Calculates the amount of VRAM required just to load the model's parameters

See how to talk to your AI agent using GPU Inference Memory Calculator.

How much VRAM is needed for a 70B parameter model using FP16 precision?

A 70B parameter model using FP16 precision requires approximately 140.0000 GB of VRAM just for the model weights.

Calculate the KV cache size for a model with 32 layers, 32 heads, and a head dimension of 128 at a context length of 4096 using BF16.

The estimated KV cache size per request is approximately 0.5333 GB.

If I have 24GB of VRAM, a model weight size of 14GB, and a KV cache per request of 0.5GB, what is the maximum batch size I can run?

The maximum batch size that fits within your 24GB VRAM budget is 20 concurrent requests.

The calculator supports FP32, FP16, BF16, INT8, and INT4 precision modes.

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