Google Gemma-3-4B-IT on NVIDIA RTX 5080 (16GB VRAM)
In 4-bit Q4_K_M, Google Gemma-3-4B-IT uses only 4.4 GB of your 16 GB VRAM (28% utilization) at ~228.3 tokens/sec. In full 16-bit precision, it requires 10.58 GB.
🎛️ Interactive VRAM & KV-Cache Tuner
📊 Real-Time Memory Footprint
4.4 GB / 16 GB🚀 Ready-to-Run Launch Command
ollama run gemma3:4b # Or with llama.cpp server (8K context, Flash Attention): llama-server -hf google/gemma-3-4b-it-GGUF -c 8192 -ngl 99 -fa
Need 4.4 GB+ VRAM to run Google Gemma-3-4B-IT?
Recommended cloud setup for 4-bit: RTX 4090 (24GB) (or 10.58 GB for full FP16). Spin up a pre-configured vLLM / Ollama instance in 60 seconds:
| Provider | Best For | RTX 4090 (24GB) | A100 (80GB) | H100 (80GB) | Bonus / Trial | Action |
|---|---|---|---|---|---|---|
| RunPod | 1-Click vLLM / Ollama Pods | $0.44 / hr | $1.39 / hr | $2.39 / hr | Instant PyTorch / vLLM Pods + Serverless GPU | Deploy on RunPod → |
| Vast.ai | Cheapest Spot GPU Market | $0.29 / hr | $0.95 / hr | $1.79 / hr | Save 50–70% on RTX 4090 / 5090 / A100 rentals | Check Spot GPUs → |
| Vultr Cloud GPU | Enterprise SLA & Bare Metal | — | $1.79 / hr | $2.69 / hr | $300 Free Credit for New Users | Claim $300 Credit → |
| DigitalOcean GPU Droplets | Managed H100 Droplets | — | — | $2.99 / hr | $200 Free Trial (60 Days) | Get $200 Free → |
All Quantization Formats: Google Gemma-3-4B-IT on NVIDIA RTX 5080
| Quantization Format | Bits/Weight | Weights VRAM | KV Cache (8K) | Total VRAM | Fits on 1x NVIDIA RTX 5080 (16GB)? | Est. Speed | GPUs Needed |
|---|---|---|---|---|---|---|---|
| FP16 / BF16 (Unquantized) | 16 bpw | 8.86 GB | 1.06 GB | 10.58 GB | Yes (66%) | ~69.2 tok/s | 1x NVIDIA RTX 5080 |
| FP8 (W8A8 / E4M3) | 8.5 bpw | 4.71 GB | 1.06 GB | 6.42 GB | Yes (40%) | ~130.3 tok/s | 1x NVIDIA RTX 5080 |
| GGUF Q8_0 (8-bit) | 8.5 bpw | 4.71 GB | 1.06 GB | 6.42 GB | Yes (40%) | ~130.3 tok/s | 1x NVIDIA RTX 5080 |
| GGUF Q6_K (6-bit) | 6.56 bpw | 3.63 GB | 1.06 GB | 5.35 GB | Yes (33%) | ~168.8 tok/s | 1x NVIDIA RTX 5080 |
| GGUF Q5_K_M (5-bit) | 5.69 bpw | 3.15 GB | 1.06 GB | 4.87 GB | Yes (30%) | ~194.6 tok/s | 1x NVIDIA RTX 5080 |
| GGUF Q4_K_M / AWQ (4-bit) | 4.85 bpw | 2.69 GB | 1.06 GB | 4.4 GB | Yes (28%) | ~228.3 tok/s | 1x NVIDIA RTX 5080 |
| GGUF IQ4_XS (4.25 bpw) | 4.25 bpw | 2.35 GB | 1.06 GB | 4.07 GB | Yes (25%) | ~260.6 tok/s | 1x NVIDIA RTX 5080 |
| GGUF Q3_K_M (3-bit) | 3.91 bpw | 2.16 GB | 1.06 GB | 3.88 GB | Yes (24%) | ~280 tok/s | 1x NVIDIA RTX 5080 |
Frequently Asked Questions (Google Gemma-3-4B-IT + NVIDIA RTX 5080)
Can NVIDIA RTX 5080 (16GB) run Google Gemma-3-4B-IT locally?
Yes — NVIDIA RTX 5080 (16GB) easily runs Google Gemma-3-4B-IT even in full FP16. In 4-bit Q4_K_M, Google Gemma-3-4B-IT uses only 4.4 GB of your 16 GB VRAM (28% utilization) at ~228.3 tokens/sec. In full 16-bit precision, it requires 10.58 GB.
How is KV Cache calculated for Google Gemma-3-4B-IT?
Google Gemma-3-4B-IT uses Dense-GQA with 34 transformer layers, 8 attention heads, and 4 KV heads (head dimension 256). At 8,192 tokens in FP16, KV cache consumes 1.06 GB. Enabling 8-bit KV cache (-ctk q8_0 -ctv q8_0 in llama.cpp or --kv-cache-dtype fp8 in vLLM) cuts this in half to 0.53 GB.