CanIRun.ai
CanIRun.ai answers whether you can run AI locally on your own hardware. It detects your GPU or Mac and shows which open LLMs, coding, image, and video models you can run locally, with VRAM requirements, speed estimates, and grades for each
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What to know before choosing
Official-site material has been collected and reviewed in the product profile below. Pricing and platform details remain unverified unless a specific sourced statement is shown.
Evidence
Can I run AI locally? Best models for your GPU ↗, Can I run Hy3 locally? VRAM & hardware ↗
Official-site collection and source-backed editorial review. Last source review is not a date of product testing.
Product details from official sources
CanIRun.ai answers whether you can run AI locally on your own hardware. It detects your GPU or Mac and shows which open LLMs, coding, image, and video models you can run locally, with VRAM requirements, speed estimates, and grades for each model. The catalog covers models from providers such as Alibaba, Black Forest Labs, DeepSeek, Google, Meta, Microsoft, Mistral, NVIDIA, OpenAI, and Tencent, with filters for task, license, size, and fit grades. Model pages list quantization options with VRAM per quant; for example, Tencent's Hy3 needs about 164.8 GB of memory minimum and 274.7 GB recommended. A docs section explains practical concepts for local LLMs, including parameters, quantization, VRAM, mixture-of-experts, context length, and memory bandwidth.
Capabilities and conditions
- Detects your GPU or Mac and shows which open LLMs, coding, image, and video models you can run locally. Source ↗
- Shows VRAM requirements, speed estimates, and grades for each model. Source ↗
- The Hy3 model page shows VRAM per quantization: at Q4_K_M, Hy3 uses about 151.6 GB of VRAM. Source ↗
Price and free allowance
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Published-source profile · collected 2026-10-17. Automated collection followed by editorial review. These are vendor-published claims, not hands-on product test results.
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