CanIRunAI Logo

CanIRunAI

Hardware Analyzer
Star
Checking Ollama…
✦ NEXT-GEN LOCAL AI COMPATIBILITY ENGINE

Can Your Machine Run Local AI?
Find Out In 1 Second.

Instant client-side hardware probe. Match your CPU, unified memory, and GPU VRAM against 50+ cutting-edge LLMs, coding copilots, and vision models without downloading gigabytes of weights.

⚡ Scan My Hardware Explore Model Library ↓
⚡ Zero-Latency Client Probe 🔒 100% Client-Side & Private 🎛 Precision Quantization Math

⚙ Your Hardware

Detecting hardware…
✦ UNCOMPROMISING PRECISION

Why CanIRunAI?

Running local AI should be effortless. We eliminate the guesswork, wasted bandwidth, and Out-Of-Memory freezes before you pull a single tensor.

🛑

Say Goodbye to 20GB OOM Freezes

Downloading a 30GB model only to watch your runtime crash with CUDA out of memory is frustrating. CanIRunAI verifies VRAM budgets before you waste time and bandwidth.

🧠

Unified Memory vs. Discrete VRAM

Apple Silicon (M1/M2/M3/M4) allocates high-bandwidth Unified Memory differently than Windows & Linux discrete GPUs. Our engine applies exact architecture-specific headroom caps.

🔒

100% Client-Side & Private

No backend telemetry. No device fingerprint harvesting. All hardware discovery runs client-side inside your browser via WebGL shaders and memory APIs.

🎛

Dynamic Quantization Math

Switch between Q4_K_M, Q5, Q8, and FP16 with live recalculation. We factor in base parameter weights plus a 20% runtime buffer for KV-cache and context window tokens.

✦ UNDER THE HOOD

How It Works

A transparent 3-stage hardware evaluation algorithm that simulates runtime tensor memory before you pull a model.

STAGE 01

Client-Side Hardware Probe

We query the browser's unmasked WebGL context (WEBGL_debug_renderer_info) to detect your GPU silicon, alongside logical CPU thread counts and approximate memory heap quotas with zero server requests.

STAGE 02

Architecture Headroom Modeling

The engine allocates dynamic memory ceilings: reserving 2.5 GB for your operating system, allocating up to 75% wired memory on Apple Silicon, or reserving discrete VRAM for dedicated NVIDIA & AMD graphics cards.

STAGE 03

Quantized Tensor Verification

Model parameter weights are scaled by your chosen quantization precision (Q4, Q8, FP16) plus a 20% runtime buffer for token context windows, classifying models into Can Run Smoothly, Tight Fit, or Too Heavy.

✦ RUNTIME COMPATIBILITY

Supported AI Runtimes & Frameworks

CanIRunAI formulas are benchmarked against the world's most popular open-source local AI inference engines.

🦙 Ollama
⚡ llama.cpp
🎨 ComfyUI
🚀 vLLM
💻 LM Studio
🤖 Jan.ai
🤗 Hugging Face GGUF
🌌 Stable Diffusion
📚 Frequently Asked Questions & Hardware Guide
Direct answers to common questions about running open-source LLMs, VRAM requirements, and local AI performance.
Can my PC or laptop run local LLMs like LLaMA 3 or DeepSeek R1?
Most modern PCs and laptops with 16 GB of RAM can comfortably run 7B to 8B parameter models (such as LLaMA 3.1 8B, Qwen 2.5 7B, or Mistral 7B) at 4-bit (Q4) quantization. Running larger 14B to 32B models typically requires 24 to 32 GB of system RAM or a dedicated GPU with 12 to 16 GB of VRAM. Models with 70B parameters require 40 GB+ of VRAM/RAM.
Can I run AI models on CPU without a dedicated GPU?
Yes! Lightweight quantized LLMs like LLaMA 3.2 1B/3B, Phi-3 Mini 3.8B, and Qwen 2.5 0.5B/1.5B run fast on modern multi-core CPUs using Ollama or llama.cpp. For image generation, frameworks like stable-diffusion.cpp and LCM-LoRA enable image synthesis directly on CPU without needing a dedicated CUDA GPU.
What is the difference between Q4, Q5, Q8, and FP16 quantization?
Quantization compresses model weights to consume significantly less memory. FP16 requires ~2 bytes per parameter (a 70B model needs ~140 GB). Q4 (4-bit) reduces this to ~0.55 bytes per parameter, allowing a 70B model to fit in ~40 GB with minimal loss in reasoning quality. Q8 provides near-original precision at ~1 byte per parameter.
How does Apple Silicon Unified Memory work for local AI?
On Apple Silicon Macs (M1, M2, M3, M4), the CPU and GPU share high-bandwidth Unified Memory. Metal and Ollama can allocate 66% to 75% of total system RAM as GPU VRAM. For example, a Mac with 36 GB or 64 GB of unified memory can run large 14B to 32B models at full GPU speed without needing a discrete graphics card.
How does CanIRunAI detect my hardware specs?
CanIRunAI runs 100% client-side in your browser using standard WebGL APIs (WEBGL_debug_renderer_info) and navigator.deviceMemory to inspect your GPU, VRAM, and RAM instantly with zero installs or tracking. You can also customize your exact hardware using the ✏️ Edit Specs button above.