← Interactive LLM VRAM Calculator — try your own context length & quantization
How much VRAM to run Gemma 2 27B? (FP16/Q8/Q6/Q4 requirements)
Architecture: 46 layers, GQA with 16 KV heads; alternating sliding-window attention. Figures are approximations for weights + an 8k-token FP16 KV cache + ~1 GB runtime overhead, based on typical published GGUF sizes. Actual usage varies by runtime and settings.
| Quantization | Weights | Total VRAM (8k context) | Fits on (example) |
|---|---|---|---|
| FP16 full precision (16-bit) | ~54 GB | ~58 GB | 2×RTX 5090 (64 GB) |
| Q8_0 8-bit | ~29 GB | ~33 GB | 2×RTX 3090 or RTX A6000 (48 GB) |
| Q6_K ~6.6-bit | ~22 GB | ~26 GB | RTX 5090 (32 GB) |
| Q5_K_M ~5.7-bit | ~19 GB | ~23 GB | RTX 3090 / RTX 4090 (24 GB) |
| Q4_K_M ~4.8-bit | ~17 GB | ~21 GB | RTX 3090 / RTX 4090 (24 GB) |
How much VRAM do I need to run Gemma 2 27B?
You need roughly ~21 GB of VRAM to run Gemma 2 27B at Q4_K_M with an 8k-token context — that fits on RTX 3090 / RTX 4090 (24 GB). At Q8_0 plan for about ~33 GB, and full FP16 needs around ~58 GB. Gemma 2 27B has a relatively large KV cache (16 KV heads). The figures here assume a full 8k FP16 cache on every layer; llama.cpp builds with sliding-window-aware allocation use somewhat less. Q4_K_M fits comfortably on a 24 GB card.
What affects these numbers?
Three things: the quantization (bytes per parameter), the KV cache (grows linearly with context length — the table assumes 8k tokens), and runtime overhead. Longer contexts need more memory; quantizing the KV cache (e.g. q8_0 cache in llama.cpp) roughly halves that part. Use the interactive calculator to plug in your exact context length and quantization.