← Can It Run? — GPU compatibility matrix · Local LLMs guide
Gemma 2 27B needs 16 GB VRAM at Q4. The RTX 3080 10GB has 10 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 3080 10GB’s 10 GB of VRAM can hold it — here’s the exact math.
VRAM requirements
| Quantization | VRAM needed | RTX 3080 10GB |
|---|---|---|
| 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 3080 10GB has only 10 GB — you’re 6 GB short. You can offload layers to system RAM, but expect single-digit tokens/sec (painfully slow for chat).
About the RTX 3080 10GB
Architecture: Ampere · VRAM: 10 GB · Price: ~$400 used.
Good: Fast memory bandwidth, strong tokens/sec for its tier
Watch out: Only 10GB — awkward middle ground, limits model size
Check current RTX 3080 10GB price on Amazon →
Related checks
Other models on the RTX 3080 10GB
🎯 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 3080 10GB run Gemma 2 27B?
No — not enough VRAM without CPU offloading. Gemma 2 27B needs 16 GB VRAM at Q4_K_M; the RTX 3080 10GB has 10 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 3080 10GB 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.