LlamaToaster

Will this local AI model run on your PC? See how many layers fit in VRAM and what context you can afford, before you download.

Will this local AI model actually run on your computer? Find out on the model page, before you download a single gigabyte. ▶ THE PROBLEM Running AI locally means picking from an enormous catalog: hundreds of thousands of model repositories for llama.cpp, the most popular engine for local inference, and almost 4 million GGUF files on Hugging Face. There are plenty of benchmarks that tell you how good a model is. Nobody can tell you for sure whether it will work on YOUR system. And one model is rarely one file. It comes in anywhere from a few to about twenty quantizations. If you already know which model you want, choosing the exact quant is a puzzle of its own, and what you really want is the most bits that still run well on your hardware. Everyone knows the rule: put as many layers as possible into video memory. But: • What if the VRAM can't hold all the layers? • How many layers fit in YOUR VRAM, and how many have to go to slow system RAM? What about the experts in mixture-of-experts models? • The longer the context, the more memory it needs. How much context can you afford? The usual way to find out is to download several models, load them in your favorite local-AI app, tweak settings blindly on a chatbot's advice, and wait for either an out-of-memory crash or, worse, a silent slowdown when part of the model quietly spills from VRAM into RAM, which nobody warns you about. ▶ WHAT LLAMATOASTER DOES You don't download the model and you don't run it. LlamaToaster reads just a few megabytes of each quant, instead of tens or even hundreds of gigabytes. Open a model page on Hugging Face and click the toaster. Every quantization gets a mark that tells you, before downloading: • Whether it fits at all • How many layers go on your GPU and how much goes to RAM, including the experts of MoE models • Whether it still fits at the context length you choose: move the slider and every mark updates at once The marks, from best to worst: • All layers on the GPU, at the model's maximum context • All layers on the GPU, at your context • All layers on the GPU, with the MoE experts in RAM • Partial offload: half or more of the layers on the GPU • Partial offload: under half of the layers on the GPU • Not enough memory Hover a mark for the figures: VRAM and RAM needed, layers on the GPU, KV cache, file size. ▶ HOW IT WORKS LlamaToaster reads only the model's own header (a few megabytes per quant, with small HTTP range requests; never the model itself) and places it on your GPU and RAM the way llama.cpp's own fit logic does, including the KV cache for your chosen context and MoE layouts. It covers the architectures llama.cpp supports and works with Windows, macOS and Linux machines. ▶ HOW ACCURATE IS IT Memory estimates are typically within about 10% of what llama.cpp actually uses; a few models are further off. For a precise answer and real speed measurements on your own hardware, use LlamaToaster itself (llamatoaster.com). ▶ THIS MACHINE The popup shows your CPU, RAM (with type and speed when known) and GPU. Choose how much memory to keep free, or use what your apps hold right now. Override the VRAM size or the memory type (dedicated, unified, CPU only) if the detection is wrong. ▶ MEMORY SPEED Push the lever to measure RAM and GPU memory bandwidth. Memory bandwidth, more than compute, decides how fast a local model generates text. ▶ MEASURED VRAM (OPTIONAL) Browsers cannot report free VRAM. If you install a LlamaToaster worker on the same computer, the extension reads the real figure through a local helper (AMD, Intel, NVIDIA and Apple silicon). Without it, VRAM comes from a table of known cards and your own "used by other apps" setting, and the popup says so. ▶ MANUAL OR AUTO Manual: nothing is read until you click the toaster. Auto: every model page is checked as it opens. You can dock the popup as a side panel. ▶ PRIVATE BY DESIGN No account, no analytics, no tracking, no servers of ours. Everything runs in your browser and your settings stay on your device. The extension only runs on huggingface.co. Open source (MIT): github.com/LlamaToaster/extension