✅ FITS IN (W8A8 QUANTIZATION
Google Gemma-3-27B-IT on 2x NVIDIA RTX 4090 (48GB) (48GB VRAM)
Full FP16 (60.98 GB) exceeds 48 GB VRAM, but 4-bit GGUF Q4_K_M / AWQ requires 21.64 GB (17.11 GB weights + 3.88 GB KV cache at 8K context), delivering ~37.6 tokens/sec.
🎛️ Interactive VRAM & KV-Cache Tuner
📊 Real-Time Memory Footprint
21.64 GB / 48 GB
🟦 Weights (17.11 GB)
🟪 KV Cache (3.88 GB)
🟨 Activation Buffer (0.66 GB)
Total Required VRAM21.64 GB
Available Hardware VRAM48 GB (2x NVIDIA RTX 4090 (48GB))
VRAM Headroom / Overflow26.36 GB
Estimated Generation Speed~37.6 tokens/sec
Architecture & AttentionDense-GQA (62L, 16 KV heads)
🚀 Ready-to-Run Launch Command
ollama run gemma3:27b # Or with llama.cpp server (8K context, Flash Attention): llama-server -hf google/gemma-3-27b-it-GGUF -c 8192 -ngl 99 -fa
CLOUD GPU ALTERNATIVE
Need 21.64 GB+ VRAM to run Google Gemma-3-27B-IT?
Recommended cloud setup for 4-bit: RTX 4090 (24GB) (or 60.98 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-3-27B-IT on 2x NVIDIA RTX 4090 (48GB)
| Quantization Format | Bits/Weight | Weights VRAM | KV Cache (8K) | Total VRAM | Fits on 1x 2x NVIDIA RTX 4090 (48GB) (48GB)? | Est. Speed | GPUs Needed |
|---|---|---|---|---|---|---|---|
| FP16 / BF16 (Unquantized) | 16 bpw | 56.44 GB | 3.88 GB | 60.98 GB | No (Over by 13.0GB) | ~1.5 tok/s | 2x 2x NVIDIA RTX 4090 (48GB) |
| FP8 (W8A8 / E4M3) | 8.5 bpw | 29.99 GB | 3.88 GB | 34.52 GB | Yes (72%) | ~21.5 tok/s | 1x 2x NVIDIA RTX 4090 (48GB) |
| GGUF Q8_0 (8-bit) | 8.5 bpw | 29.99 GB | 3.88 GB | 34.52 GB | Yes (72%) | ~21.5 tok/s | 1x 2x NVIDIA RTX 4090 (48GB) |
| GGUF Q6_K (6-bit) | 6.56 bpw | 23.14 GB | 3.88 GB | 27.68 GB | Yes (58%) | ~27.8 tok/s | 1x 2x NVIDIA RTX 4090 (48GB) |
| GGUF Q5_K_M (5-bit) | 5.69 bpw | 20.07 GB | 3.88 GB | 24.61 GB | Yes (51%) | ~32.1 tok/s | 1x 2x NVIDIA RTX 4090 (48GB) |
| GGUF Q4_K_M / AWQ (4-bit) | 4.85 bpw | 17.11 GB | 3.88 GB | 21.64 GB | Yes (45%) | ~37.6 tok/s | 1x 2x NVIDIA RTX 4090 (48GB) |
| GGUF IQ4_XS (4.25 bpw) | 4.25 bpw | 14.99 GB | 3.88 GB | 19.53 GB | Yes (41%) | ~42.9 tok/s | 1x 2x NVIDIA RTX 4090 (48GB) |
| GGUF Q3_K_M (3-bit) | 3.91 bpw | 13.79 GB | 3.88 GB | 18.33 GB | Yes (38%) | ~46.7 tok/s | 1x 2x NVIDIA RTX 4090 (48GB) |
Frequently Asked Questions (Google Gemma-3-27B-IT + 2x NVIDIA RTX 4090 (48GB))
Can 2x NVIDIA RTX 4090 (48GB) (48GB) run Google Gemma-3-27B-IT locally?
Yes — 2x NVIDIA RTX 4090 (48GB) (48GB) can run Google Gemma-3-27B-IT using FP8 (W8A8 / E4M3). Full FP16 (60.98 GB) exceeds 48 GB VRAM, but 4-bit GGUF Q4_K_M / AWQ requires 21.64 GB (17.11 GB weights + 3.88 GB KV cache at 8K context), delivering ~37.6 tokens/sec.
How is KV Cache calculated for Google Gemma-3-27B-IT?
Google Gemma-3-27B-IT uses Dense-GQA with 62 transformer layers, 32 attention heads, and 16 KV heads (head dimension 128). At 8,192 tokens in FP16, KV cache consumes 3.88 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 1.94 GB.
Compare Google Gemma-3-27B-IT on Other GPUs
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