SelfHostLLM

Calculate GPU memory needed for self-hosted LLM inference instantly

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Added Jul 2026

selfhostllm.org

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What is Selfhostllm?

SelfHostLLM is a GPU memory calculator designed to help developers and AI engineers determine hardware requirements for self-hosting large language models. It calculates optimal configurations by analyzing GPU specs, model parameters, quantization methods, and context lengths to prevent out-of-memory errors. The tool accounts for system overhead, KV cache requirements, and concurrent request capacity, making it essential for anyone running LLM inference locally on Mac, PC, or NPU-equipped devices.

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Key Features of Selfhostllm

GPU memory configuration calculator
Support for multiple GPU setups
Quantization options (FP16/BF16, INT8, INT4, MXFP4, Extreme Quant)
Context length customization (1K to 2M tokens)
Concurrent request capacity estimation
System overhead accounting
KV cache calculation
Hardware configuration tool for Mac and PC with NPU

Who Should Use Selfhostllm?

Plan GPU infrastructure for self-hosted LLM deployment

Optimize quantization strategy for memory constraints

Estimate concurrent request capacity for inference servers

Determine hardware upgrades needed for larger models

Calculate KV cache overhead for long-context applications

Configure tensor parallelism for multi-GPU setups

Selfhostllm: Pros & Cons

✓Pros

Free to use
No signup required
Comprehensive memory calculation algorithm
Supports wide range of quantization methods
Accounts for system overhead and KV cache
Open source (available on GitHub)
Responsive interface with multiple input methods

✕Cons

Web-based only, no API or programmatic access
No integration with model marketplaces or download tools
Calculations are estimates and may vary based on specific framework implementations
Limited to memory calculations, does not benchmark actual performance
No cost estimation for cloud GPU alternatives

Frequently Asked Questions about Selfhostllm

What GPU models does SelfHostLLM support?

The calculator supports any GPU model. You can input the specific model name, VRAM capacity, and number of GPUs in your hardware configuration to get accurate memory calculations.

How does quantization affect model memory usage?

Quantization reduces model size: INT8 provides ~25% reduction, INT4 ~50% reduction, MXFP4 ~70% reduction, and Extreme Quant ~75% reduction. Lower precision reduces memory but may impact model quality.

Can I use this calculator for multi-GPU setups?

Yes, you can specify the number of GPUs and VRAM per GPU in the hardware configuration section. The calculator accounts for tensor parallelism requirements across multiple GPUs.

Does this calculator work for Mac and PC?

Yes, SelfHostLLM includes dedicated guides for running LLMs on Mac and on PCs with NPU hardware, accessible from the top of the page.

What does KV cache overhead mean?

KV cache stores key-value pairs for attention mechanisms and grows with context length and batch size. The calculator shows how much VRAM this requires to help you estimate concurrent request capacity.