What is FP16 and FP8 quantization?

FP16 (16-bit floating point) is the standard full-precision format for AI models. FP8 (8-bit floating point) cuts memory usage roughly in half with minimal quality loss, supported on NVIDIA RTX 40-series and newer GPUs. Quantization reduces the precision of model weights to use less VRAM.

GGUF is a quantization format that compresses AI models to various quality levels (Q8, Q5, Q4, Q3). Lower quantization levels use less VRAM but may reduce output quality. GGUF allows large models like Flux (12GB at FP16) to run on GPUs with as little as 4-6GB VRAM.

What is CPU offloading in AI generation?

CPU offloading moves parts of the AI model from GPU VRAM to system RAM. This allows running models that would otherwise exceed your VRAM, but generation speed is significantly slower because system RAM has much less bandwidth than VRAM. It is a last-resort option for very large models.

How accurate are Can My GPU Run ratings?

Ratings are calculated from real VRAM usage data sourced from HuggingFace, GitHub repositories, and community benchmarks. The algorithm considers your GPU's VRAM capacity, compute generation, available model formats, and bandwidth. While actual performance can vary based on resolution, batch size, and system configuration, the ratings provide a reliable baseline for compatibility.

How It Works — Can My GPU Run? | GPU VRAM Compatibility Guide for AI Models

What is Can My GPU Run?

Can My GPU Run is a free tool that instantly tells you which AI models your graphics card can run. Running AI image and video generation locally requires loading large neural network models into your GPU's VRAM. Different models need different amounts of memory, and not every GPU has enough.

Instead of guessing or reading through scattered documentation, select your GPU and immediately see compatibility ratings for over 120 models across image generation, video, upscaling, 3D, and audio.

How to Use

1

Select Your GPU

Choose your graphics card from the dropdown. We support 65+ GPUs from NVIDIA GeForce (RTX 5090 to GTX 1060), AMD Radeon (RX 9070 XT to RX 6600), and Apple Silicon (M1 to M4 Ultra).

2

Browse AI Models

Browse 120+ models organized by category. Use filters to narrow by type (Image, Video, 3D, Audio), search by name, or sort by rating, VRAM usage, or popularity.

3

Check Compatibility

Each model shows an instant S-to-F rating. Click any model for detailed format options, exact VRAM requirements, and setup recommendations for your GPU.

Understanding Ratings

Each model receives a compatibility rating based on how well it fits your GPU's VRAM capacity, compute generation, and available optimized formats.

S

Perfect

Runs at full precision with VRAM to spare. No compromises needed.

A

Great

Runs at full or near-full quality. May use FP8 on some formats.

B

Good

Works well with quantization (FP8 or GGUF). Slight quality trade-off.

C

Usable

Requires heavy quantization or aggressive settings. Still functional.

D

Barely

Only possible with CPU offloading or extreme quantization. Very slow.

F

Can't Run

Not enough VRAM even with all optimizations. Incompatible hardware.

Where Does the Data Come From?

Every rating is based on real-world data, not guesswork. VRAM requirements are sourced from official model repositories on HuggingFace and GitHub, verified against community benchmarks and real hardware tests. GPU specifications come from manufacturer datasheets.

Ratings are updated regularly as new models are released, new quantization formats become available, and community benchmarks provide better data. If you spot an inaccuracy, let us know.

VRAM & Formats Explained

AI models can be loaded in different precision formats that trade off memory usage vs. quality. Here's what each format means:

FP16 Full Precision

16-bit floating point. The standard format with maximum quality. Requires the most VRAM. Use this when your GPU has enough memory.

FP8 Half Memory

8-bit floating point. Cuts VRAM usage roughly in half with minimal visible quality loss. Supported on NVIDIA RTX 40-series and newer GPUs.

GGUF Flexible Compression

Quantized format with multiple levels (Q8, Q5, Q4, Q3). Lower levels use less VRAM. Q5 is the best balance of quality and memory for most users.

CPU Offload Last Resort

Moves part of the model to system RAM. Allows running models that exceed your VRAM but generation is significantly slower due to lower memory bandwidth.

Why This Matters

Running AI generation locally on your own hardware gives you full control over your creative process. No cloud subscriptions, no usage limits, no sending your prompts to external servers. But the hardware requirements can be confusing — model sizes range from 1GB to 24GB+ of VRAM, and the same model can run differently depending on the format you choose.

Can My GPU Run cuts through this complexity. Instead of reading GitHub READMEs and community forums to piece together whether a model works on your GPU, you get an instant answer with format recommendations tailored to your hardware.

GPU Comparison

Can't decide between two GPUs? The comparison tool lets you pick any two graphics cards and see how they stack up across all 120+ AI models. You'll see:

VS

Side-by-Side Overview

Compatibility counts, average scores, and a clear winner declaration so you can see the overall picture at a glance.

📊

Rating Distribution

Stacked bar charts showing how many models each GPU gets at S, A, B, C, D, and F tiers. More green = better GPU for AI workloads.

🔍

Model-by-Model Breakdown

Drill into every category and model to see exactly where one GPU outperforms the other, with visual comparison bars for each model.

To compare GPUs, select any GPU on the main page and click the Compare button, or choose two GPUs directly from the comparison page.

Frequently Asked Questions

What is VRAM and why does it matter for AI?

VRAM (Video Random Access Memory) is the dedicated memory on your graphics card. AI models must be loaded into VRAM to generate images or video. If a model requires more VRAM than your GPU has, it either won't run, will need quantization to reduce memory usage, or must partially offload to system RAM (which is much slower).

What's the difference between FP16 and FP8?

FP16 (16-bit floating point) is the standard full-precision format. FP8 (8-bit floating point) cuts memory usage roughly in half with minimal quality loss. FP8 is supported on NVIDIA RTX 40-series and newer GPUs. If your GPU supports FP8, it's often the best way to run larger models without visible quality degradation.

What is GGUF format?

GGUF is a quantization format developed by the llama.cpp community. It compresses AI models to various quality levels — Q8 (highest quality, ~50% of FP16 size), Q5 (good balance), Q4 (smaller), Q3 (smallest, lowest quality). GGUF allows models like Flux that normally need 12GB at FP16 to run on GPUs with just 4-6GB VRAM.

What is CPU offloading?

CPU offloading splits a model between your GPU's VRAM and your system's RAM. This lets you run models that exceed your VRAM capacity, but generation becomes significantly slower because system RAM bandwidth (typically 50-80 GB/s) is much lower than VRAM bandwidth (typically 200-1000 GB/s). It's a last-resort option when no quantized format fits your GPU.

How accurate are the ratings?

Ratings are based on real VRAM usage data from HuggingFace, GitHub, and community benchmarks. The algorithm considers your GPU's VRAM, compute generation, available formats, and bandwidth. While actual performance varies with resolution, batch size, and system config, the ratings provide a reliable compatibility baseline. We update the database regularly as new models and benchmarks appear.

Do I need a specific GPU brand?

No. Can My GPU Run supports NVIDIA GeForce (RTX 50-series down to GTX 1060), AMD Radeon (RX 9070 XT down to RX 6600), and Apple Silicon (M1 through M4 Ultra). NVIDIA GPUs have the broadest software support (CUDA), but AMD (ROCm) and Apple (MPS) work with many popular models. The tool accounts for platform-specific compatibility in its ratings.

How It Works