← Can It Run? — GPU compatibility matrix · Local LLMs guide
Gemma 2 27B needs 16 GB VRAM at Q4. The RTX 4060 8GB has 8 GB.
💎 Gemma 2 27B (Google) is a 27B-parameter model for high-quality chat, reasoning, analysis. Best quality under 24GB at Q4 — punches above its weight. The question is whether the RTX 4060 8GB’s 8 GB of VRAM can hold it — here’s the exact math.
VRAM requirements
| Quantization | VRAM needed | RTX 4060 8GB |
|---|---|---|
| Q4_K_M (recommended) | 16 GB | ❌ |
| Q8_0 (near-lossless) | 28 GB | ❌ |
Why it won’t fit (and what to do)
Gemma 2 27B needs 16 GB at Q4 but the RTX 4060 8GB has only 8 GB — you’re 8 GB short. You can offload layers to system RAM, but expect single-digit tokens/sec (painfully slow for chat).
About the RTX 4060 8GB
Architecture: Ada · VRAM: 8 GB · Price: ~$300.
Good: Cheap, efficient Ada card, DLSS 3, low power draw
Watch out: Only 8GB VRAM — caps you at 7-8B models
Check current RTX 4060 8GB price on Amazon →
Related checks
Other models on the RTX 4060 8GB
🎯 LLM Hardware Checker
Enter your exact GPU + RAM, see every model you can run.
💾 VRAM Requirements
Exact VRAM per model at Q4/Q5/Q8/FP16 with context scaling.
Frequently asked questions
Can the RTX 4060 8GB run Gemma 2 27B?
No — not enough VRAM without CPU offloading. Gemma 2 27B needs 16 GB VRAM at Q4_K_M; the RTX 4060 8GB has 8 GB.
How much VRAM does Gemma 2 27B need?
Gemma 2 27B needs about 16 GB at Q4_K_M (recommended) and 28 GB at Q8_0.
How many tokens per second will the RTX 4060 8GB get on Gemma 2 27B?
It won't fit in VRAM; with CPU offloading expect very slow single-digit tokens/sec.
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.