This page answers Google Gemma 2 27B It fp16 queries with explicit calculations from our model requirement dataset and compatibility speed table.
Short answer: Google Gemma 2 27B It typically needs around 54GB VRAM at FP16, and 65GB is safer for smoother usage.
Exact FP16 requirement from model requirement data.
Throughput data below uses available compatibility measurements/estimates and is sorted by tokens per second for this model.
Need general guidance? Review full methodology.
| GPU | VRAM | Quantization | Speed | Compatibility | Buy |
|---|---|---|---|---|---|
| AMD Instinct MI300X | 192GB | FP16 | 160 tok/s | View full compatibility | Buy options |
| NVIDIA H200 SXM 141GB | 141GB | FP16 | 144 tok/s | View full compatibility | Buy options |
| NVIDIA H100 SXM5 80GB | 80GB | FP16 | 103 tok/s | View full compatibility | Buy options |
| AMD Instinct MI250X | 128GB | FP16 | 100 tok/s | View full compatibility | Buy options |
| NVIDIA H100 PCIe 80GB | 80GB | FP16 | 66 tok/s | View full compatibility | Buy options |
| RTX 5090 | 32GB | FP16 | 63 tok/s | View full compatibility | Buy options |
| NVIDIA A100 80GB SXM4 | 80GB | FP16 | 61 tok/s | View full compatibility | Buy options |
| AMD Instinct MI210 | 64GB | FP16 | 50 tok/s | View full compatibility | Buy options |
| NVIDIA A100 40GB PCIe | 40GB | FP16 | 47 tok/s | View full compatibility | Buy options |
| RTX 4090 | 24GB | FP16 | 38 tok/s | View full compatibility | Buy options |
| NVIDIA RTX 6000 Ada | 48GB | FP16 | 37 tok/s | View full compatibility | Buy options |
| NVIDIA L40 | 48GB | FP16 | 35 tok/s | View full compatibility | Buy options |
Google Gemma 2 27B It at FP16 is estimated to require about 54GB VRAM minimum, with 65GB recommended for smoother operation.
Start with AMD Instinct MI300X, NVIDIA H200 SXM 141GB, NVIDIA H100 SXM5 80GB and review each compatibility page for full speed and fit details.
FP16 is a balance point between memory usage and quality. If your GPU is below 54GB, consider lower-bit quantization; if you have extra VRAM, compare Q8/FP16 options for quality-sensitive workloads.