📍 Part of the Local LLMs in 2026 guide
Gemma 2 27B needs 16 GB VRAM at Q4. The RTX 3060 12GB has 12 GB.
● Gemma 2 27B (Google) is a 27B parameter model used for High-quality chat, reasoning, analysis. Best quality under 24GB at Q4 — punches above its weight.
VRAM Requirements
| Quantization | VRAM Needed | RTX 3060 12GB |
|---|---|---|
| Q4_K_M (recommended) | 16 GB | ❌ |
| Q8_0 (high quality) | 28 GB | ❌ |
Why It Won’t Fit
Gemma 2 27B needs 16 GB VRAM at Q4 quantization, but the RTX 3060 12GB only has 12 GB. You’re 4 GB short.
Options: You can run it with CPU offloading (expect ~12 tok/s — very slow), or upgrade to a GPU with 16+ GB VRAM.
About the RTX 3060 12GB
Pros: 12GB VRAM at budget price, runs most 7-8B models comfortably
Cons: Older architecture, slower than Ada-gen cards
Price: ~$300 — Check current price on Amazon →
Try It Yourself
🎯 LLM Hardware Checker
Select your exact GPU + RAM and see ALL models you can run.
💾 VRAM Calculator
Pick any model, see exact VRAM at Q4/Q5/Q8/FP16 with context scaling.
About the speed figure. The tokens/sec number above is an estimate, not a measured benchmark. Real throughput depends on your runtime and backend (Ollama, llama.cpp, vLLM), the exact quantization you download, context length, memory bandwidth, and whether any layers are offloaded to CPU. Treat it as a rough guide to the tier of performance, not a promised result.