VRAM (Video RAM): Dedicated memory on your graphics card/GPU - Used specifically for graphics processing and AI model computations - Much faster for GPU operations - Critical for running LLMs locally
RAM (System Memory): Main system memory used by CPU and general operations - Slower access for GPU computations - Can be used as fallback but with performance penalty
So - For basic 7B parameter LLMs locally, you typically need:
Minimum: 8-12 GB VRAM - Can run basic inference/tasks - May require quantization (4-bit/8-bit)
Recommended: 16+ GB VRAM - Smoother performance - Handle larger context windows - Run without heavy quantization
Quantization means reducing the precision of the model's weights and calculations to use less memory. For example, instead of storing numbers with full 32-bit precision, they're compressed to 4-bit or 8-bit representations. This significantly reduces VRAM requirements but can slightly reduce model quality and accuracy.
Options if you have less VRAM: CPU-only inference (very slow) - Model offloading to system RAM - Use smaller models (3B, 4B parameters)
Absolutely. China's models are very advanced - especially Qwen. I don't code professionally - just for personal projects. If I used these tools as an employee in a company, I'd defer to that company's wishes. Regardless of whether it's U. S. or China, these models are using and probably storing my data for further training. As they should! I don't care - they're providing me with powerful tools. I use free tiers and jump between them all. ChatGPT, Perplexity (general search), DeepSeek, Qwen (advanced programming), Google AI Studio and others. I'm grateful, not fearful.
BunsenLabs Boron - Debian 12 with Openbox Window Mgr - no desktop, no icons. The machine is not burdened by having to run a heavy desktop environment. All navigation and execution is done with mouse (right click), keybindings or command line. Linux without the Windows artifacts. On my HP i7, boots to login in 19 seconds.
VRAM vs RAM:
VRAM (Video RAM): Dedicated memory on your graphics card/GPU - Used specifically for graphics processing and AI model computations - Much faster for GPU operations - Critical for running LLMs locally
RAM (System Memory): Main system memory used by CPU and general operations - Slower access for GPU computations - Can be used as fallback but with performance penalty
So - For basic 7B parameter LLMs locally, you typically need:
Minimum: 8-12 GB VRAM - Can run basic inference/tasks - May require quantization (4-bit/8-bit)
Recommended: 16+ GB VRAM - Smoother performance - Handle larger context windows - Run without heavy quantization
Quantization means reducing the precision of the model's weights and calculations to use less memory. For example, instead of storing numbers with full 32-bit precision, they're compressed to 4-bit or 8-bit representations. This significantly reduces VRAM requirements but can slightly reduce model quality and accuracy.
Options if you have less VRAM: CPU-only inference (very slow) - Model offloading to system RAM - Use smaller models (3B, 4B parameters)