Small LLMs aren’t toys anymore. You can run serious models on a normal PC without selling a kidney for a GPU or living on cloud credits. With the right setup, 8GB RAM or VRAM is enough to get real work done. No internet dependency. No subscription traps.
Quantization
This is the whole trick. You shrink the model weights from big floating-point numbers to tighter 4-bit or 8-bit versions. Same brain, smaller footprint. A 7B model that eats 14GB in FP16 suddenly fits into 4 to 5GB after 4-bit quantization. That’s why local AI is possible. GGUF is the format almost everything is using now. Keeps things compatible and easy to load.Memory Basics
VRAM is the fast lane. RAM is the slow lane. If the model fits in VRAM, responses snap back fast. If it spills into RAM, everything crawls. Simple rule: keep the model inside your GPU memory if you want speed.How to Run Local AI
1_ Ollama
CLI based. Clean. Fast. Good for devs who don’t want pretty buttons.
2_ LM Studio
If you want a GUI and don’t feel like typing commands, use this. Lets you browse, download, and test GGUF models straight from a desktop window.
Models That Actually Work Under 8GB
These aren’t “cute hobby” models. They’re legit.
Llama 3.1 8B (Quantized)
General tasks, chat, coding, RAG.Q3_K_M takes around 7.98GB.
Mistral 7B (Quantized)
Fast and efficient. Great when you care about latency.Q4_K_M uses around 6.87GB.
DeepSeek Coder V2 6.7B
Tuned for code. Completion, debugging, explaining logic.Needs roughly 6GB VRAM.
BitNet b1.58 2B4T
Wildly efficient because of the 1.58-bit weight system.Runs in 0.4GB. Yes, really. Perfect for CPU-only setups.
Other honorable mentions: Gemma 7B, Phi-3 Mini, Orca-Mini 7B.
All of these push real AI toward edge devices, not giant datacenters. The trend is obvious. More capability, less hardware. Exactly how it should be.
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