← Can It Run? — GPU compatibility matrix · Local LLMs guide
Gemma 2 27B needs 16 GB VRAM at Q4. The RTX 4080 Super 16GB has 16 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 4080 Super 16GB’s 16 GB of VRAM can hold it — here’s the exact math.
VRAM requirements
| Quantization | VRAM needed | RTX 4080 Super 16GB |
|---|---|---|
| Q4_K_M (recommended) | 16 GB | ✅ |
| Q8_0 (near-lossless) | 28 GB | ❌ |
Expected performance
Running Gemma 2 27B at Q4 on the RTX 4080 Super 16GB, expect roughly ~22 tokens/sec in Ollama or llama.cpp — comfortably faster than reading speed, so chat feels responsive.
Headroom: 16 GB used of 16 GB leaves about 0 GB free for KV cache (context). That’s fine for 4-8K context; for 32K+ you may need a smaller batch or a lower quant.
About the RTX 4080 Super 16GB
Architecture: Ada · VRAM: 16 GB · Price: ~$1,000.
Good: Very fast inference, excellent for real-time 14-27B chat
Watch out: Still 16GB — 70B needs offloading or a 24GB+ card
Check current RTX 4080 Super 16GB price on Amazon →
Related checks
Other models on the RTX 4080 Super 16GB
🎯 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 4080 Super 16GB run Gemma 2 27B?
Yes — runs great at Q4 (recommended). Gemma 2 27B needs 16 GB VRAM at Q4_K_M; the RTX 4080 Super 16GB has 16 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 4080 Super 16GB get on Gemma 2 27B?
Roughly ~22 tokens/sec at Q4 in Ollama or llama.cpp.