Google Gemma-3-4B-IT VRAM Requirements & GPU Benchmarks
Google Gemma-3-4B-IT (4.3B total, 4.3B active, 34L, 4 KV heads, vocab 262144) requires 4.24 GB in 4-bit Q4_K_M, 5.72 GB in FP8, and 9.56 GB in FP16 at 8K context.
CLOUD-GPU PREISVERGLEICH & MIETEN-VS-KAUFEN RECHNER
Nicht genug lokaler VRAM? Cloud-GPU in 60 Sekunden starten (Google Gemma-3-4B-IT: 4.24 GB in 4-bit)
Recommended Cloud Tier: RTX 4090 (24GB) Β· Full FP16 Needs: 9.56 GB
Amortisationszeit (Kauf vs. Miete): ~32 months (vs $1,699 hardware)
| Cloud Provider | RTX 4090 (24GB) | A100 (80GB) | H100 (80GB) | Est. Monthly Cost (4h/day) | Free Credit / Trial | Deploy |
|---|---|---|---|---|---|---|
| RunPod | $0.44 / hr | $1.39 / hr | $2.39 / hr | $52.80 / mo | Instant PyTorch / vLLM Templates + Serverless GPU | Auf RunPod starten β |
| Vast.ai | $0.29 / hr | $0.95 / hr | $1.79 / hr | $34.80 / mo | Save 50β70% on RTX 4090 / 5090 / A100 rentals | Spot-GPU mieten β |
| Vultr Cloud GPU | β | $1.79 / hr | $2.69 / hr | $214.80 / mo (A100) | $300 Free Credit | $300 Guthaben sichern β |
| DigitalOcean GPU | β | β | $2.99 / hr | $358.80 / mo (H100) | $200 Free Credit (60d) | $200 Startguthaben β |
| GPU Hardware | VRAM | Bandwidth | FP16 (9.56GB) | Q4_K_M (4.24GB) | Decode / Prefill Speed | Report |
|---|---|---|---|---|---|---|
| NVIDIA RTX 5090 | 32 GB | 1792 GB/s | Ja | Ja (13%) | ~350 / 12667 tok/s | Exakten VRAM & Speed berechnen β |
| NVIDIA RTX 4090 | 24 GB | 1008 GB/s | Ja | Ja (18%) | ~266.8 / 9989 tok/s | Exakten VRAM & Speed berechnen β |
| NVIDIA RTX 5080 | 16 GB | 960 GB/s | Ja | Ja (27%) | ~254.1 / 6814 tok/s | Exakten VRAM & Speed berechnen β |
| NVIDIA RTX 4080 Super | 16 GB | 736 GB/s | Ja | Ja (27%) | ~194.8 / 6313 tok/s | Exakten VRAM & Speed berechnen β |
| NVIDIA RTX 4070 Ti Super | 16 GB | 672 GB/s | Ja | Ja (27%) | ~177.9 / 5333 tok/s | Exakten VRAM & Speed berechnen β |
| NVIDIA RTX 3090 | 24 GB | 936 GB/s | Ja | Ja (18%) | ~247.8 / 4293 tok/s | Exakten VRAM & Speed berechnen β |
| NVIDIA RTX 4060 Ti 16GB | 16 GB | 288 GB/s | Ja | Ja (27%) | ~76.2 / 2667 tok/s | Exakten VRAM & Speed berechnen β |
| NVIDIA RTX 3060 12GB | 12 GB | 360 GB/s | Ja | Ja (35%) | ~95.3 / 1536 tok/s | Exakten VRAM & Speed berechnen β |
| NVIDIA RTX 6000 Ada | 48 GB | 960 GB/s | Ja | Ja (9%) | ~254.1 / 11017 tok/s | Exakten VRAM & Speed berechnen β |
| NVIDIA RTX A6000 | 48 GB | 768 GB/s | Ja | Ja (9%) | ~203.3 / 4680 tok/s | Exakten VRAM & Speed berechnen β |
| NVIDIA L40S 48GB | 48 GB | 864 GB/s | Ja | Ja (9%) | ~228.7 / 18000 tok/s | Exakten VRAM & Speed berechnen β |
| NVIDIA A100 80GB SXM | 80 GB | 2039 GB/s | Ja | Ja (5%) | ~350 / 18000 tok/s | Exakten VRAM & Speed berechnen β |
| NVIDIA H100 80GB SXM | 80 GB | 3350 GB/s | Ja | Ja (5%) | ~350 / 18000 tok/s | Exakten VRAM & Speed berechnen β |
| NVIDIA H200 141GB SXM | 141 GB | 4800 GB/s | Ja | Ja (3%) | ~350 / 18000 tok/s | Exakten VRAM & Speed berechnen β |
| NVIDIA Blackwell B200 192GB | 192 GB | 8000 GB/s | Ja | Ja (2%) | ~350 / 18000 tok/s | Exakten VRAM & Speed berechnen β |
| AMD Instinct MI300X 192GB | 192 GB | 5300 GB/s | Ja | Ja (2%) | ~350 / 18000 tok/s | Exakten VRAM & Speed berechnen β |
| 2x NVIDIA RTX 3090 (48GB) | 48 GB | 936 GB/s | Ja | Ja (9%) | ~247.8 / 8586 tok/s | Exakten VRAM & Speed berechnen β |
| 2x NVIDIA RTX 4090 (48GB) | 48 GB | 1008 GB/s | Ja | Ja (9%) | ~266.8 / 18000 tok/s | Exakten VRAM & Speed berechnen β |
| 4x NVIDIA A100 80GB (320GB) | 320 GB | 2039 GB/s | Ja | Ja (1%) | ~350 / 18000 tok/s | Exakten VRAM & Speed berechnen β |
| 8x NVIDIA H100 80GB (640GB) | 640 GB | 3350 GB/s | Ja | Ja (1%) | ~350 / 18000 tok/s | Exakten VRAM & Speed berechnen β |
| Apple M4 Max (128GB Unified) | 128 GB | 546 GB/s | Ja | Ja (3%) | ~158.1 / 2225 tok/s | Exakten VRAM & Speed berechnen β |
| Apple M3 Ultra Mac Studio (512GB) | 512 GB | 819 GB/s | Ja | Ja (1%) | ~237.1 / 3277 tok/s | Exakten VRAM & Speed berechnen β |
| Apple M4 Pro Mac Mini (64GB) | 64 GB | 273 GB/s | Ja | Ja (7%) | ~79 / 1113 tok/s | Exakten VRAM & Speed berechnen β |