Find local models that
fit your hardware
We do the math of checking if a model will run on your hardware, so that you don't have to. Zero guesswork, zero crashed systems, zero wasted 20GB downloads.
Running local AI is full of guesswork
Between context windows, weights, and quantization levels, knowing what actually runs on your PC shouldn't require trial and error.
Local models are exploding
There are hundreds of open-source model variants releasing every week. But users don't know which ones will actually run smoothly on their machine without throwing fatal Out-Of-Memory (OOM) errors.
Calculating the math is hard & tedious
Accurately checking fit across different model architectures (GQA, MLA, Sliding Window), quantizations (Q4 vs Q8 vs FP16), and context lengths requires tedious memory equations that most developers don't have time to compute.
Downloading the wrong model wastes hours
Downloading a 20GB to 40GB model only to find it freezes your system or crawls at 1 token/sec is frustrating. Worse, you might miss out on a great model that your GPU could have run effortlessly.
Precision hardware intelligence for local AI
Stop guessing. Our engine analyzes the internal architecture of every model to give you deterministic, guaranteed compatibility answers.
Multi-model fit check in seconds
Instantly scan our complete catalog of open-source models against your hardware. Know in milliseconds which models run at pure VRAM speeds, CPU offloading, or will OOM.
Architecture-aware KV cache engine
Most calculators ignore KV cache. We calculate exact KV cache memory based on your target context window (4k to 128k+) and the model's actual attention mechanism.
True 3-part VRAM memory modeling
We compute true total consumption: Model Weights + Context KV Cache + CUDA/Runtime Overhead Buffers. No unexpected crashes when your prompt grows.
Why trust our calculations?
We don't use simplified napkin math. We model the full physical memory footprint of real model runs.
No generic rules-of-thumb
We don't rely on blanket napkin formulas like “params × bytes”. Modern models frequently use mixed precision and don't always ship in standard FP16, making it impossible to accurately predict their size using generic math.
Per-architecture KV cache algorithms
Different model families use distinct attention mechanisms (GQA, MLA, Sliding Window, SSM/Mamba) which dictate how KV cache expands. We implement dedicated algorithms for each architecture to calculate exact KV cache growth.
Calibrated on real engine runs
Every formula is stress-tested against real hardware runs to guarantee precision.
Test your hardware compatibility live
Hardware & Context Setup
Instant Architecture Evaluation
Llama 3.1 8B
Total: 6.3 GB (Weights: 4.8 GB + KV: 1.0 GB)Qwen 2.5 14B
Total: 11.1 GB (Weights: 8.8 GB + KV: 1.5 GB)DeepSeek R1 Distill 32B
Total: 23.4 GB (Weights: 19.7 GB + KV: 2.0 GB)Llama 3.3 70B
Total: 48.4 GB (Weights: 42.4 GB + KV: 2.5 GB)Stop guessing. Run the right model.
Whether you want to find what fits your GPU or verify a specific model, we make it instant and deterministic.
Find models for your machine
Input your graphics card VRAM and system RAM to see every quantized model that fits at full speed.
Check a specific model
Look up latest open models to see exact memory math and hardware compatibility.