How It Works: Methodology & Data Sources

Updated: April 26, 2026

At RunLocalModel.com, our goal is to demystify the hardware requirements for running Large Language Models (LLMs) locally. To do this, we aggregate data from the community and apply mathematical heuristics to estimate how well a model will run on your specific GPU.

Our Data Sources

We do not host the models ourselves. Instead, we pull metadata from the most trusted repositories in the open-source AI community:

• Hugging Face: The primary source for our model database. We fetch model metadata (like parameter counts, tags, licenses, and release dates) directly from Hugging Face.
• Hardware Specs: Our GPU database is compiled from official vendor datasheets (NVIDIA, AMD, Apple, Intel) to ensure accurate VRAM and Memory Bandwidth figures.
• Inference Engines: Our estimation logic is heavily inspired by the memory allocation patterns of popular inference engines like llama.cpp, Ollama, and LM Studio.

How We Estimate VRAM Requirements

Knowing if a model fits in your GPU's memory (VRAM) is the most critical step. If a model exceeds your VRAM, it will either fail to load or "spill over" into your system RAM, reducing generation speed to a crawl.

Our llama.cpp-like estimation method calculates the total required VRAM by summing three components:

1. Model Weights

The size of the model weights depends on the parameter count (e.g., 7 Billion) and the quantization level (how heavily the model is compressed).
Formula: Parameter Count × Bytes per Parameter (based on Quantization, e.g., 0.5 bytes for Q4_K_M)

2. KV Cache (Context Window)

The KV (Key-Value) cache stores the context of your conversation. The longer your context (e.g., 8,000 tokens vs 4,000 tokens), the more memory is required. We also factor in the precision of the KV cache (F16, Q8, or Q4).

3. System Overhead

Inference engines and your operating system require a baseline amount of memory just to operate. We add a flat overhead (typically around 600MB) to ensure a safe buffer.

How We Estimate Speed (Tokens per Second)

If a model fits entirely in your VRAM, the primary bottleneck for text generation speed is your GPU's Memory Bandwidth (measured in GB/s), not necessarily its raw compute power (TFLOPs).

Our speed estimation uses the following logic:

• Base Speed: (Effective Memory Bandwidth) / (Model Size in GB). We apply an efficiency penalty based on the GPU vendor (e.g., NVIDIA GPUs typically achieve ~72% of their theoretical max bandwidth during inference).
• Memory Penalty: If the estimated VRAM requirement exceeds your available VRAM, we apply a severe penalty to the speed, simulating the massive slowdown of offloading to system RAM.
• Context Penalty: Generating text at the end of a very long context window is slower than at the beginning. We apply a logarithmic penalty based on your selected context size.

Compatibility Grades Explained

Based on the ratio of Estimated VRAM Required to Your Available VRAM, we assign a simple grade:

• Runs great (S): Uses less than 40% of your VRAM. Plenty of room for massive context windows or running other apps.
• Runs well (A): Uses up to 65% of your VRAM. Very comfortable fit.
• Decent (B): Uses up to 85% of your VRAM. Good, but you might need to close other VRAM-heavy applications (like games or video editors).
• Tight fit (C): Uses up to 100% of your VRAM. It will run, but you may need to reduce the context window.
• Barely runs (D): Exceeds your VRAM slightly (up to 130%). It will offload to system RAM and run very slowly.
• Too heavy (F): Massively exceeds your VRAM. Likely will crash or be completely unusable.