Gemma 2 27B: VRAM Requirements & Local Setup Guide 2026
Gemma 2 27B is Google's 27B-parameter open-weight model with a 8K tokens context window.
Quick Facts
- Provider
- Architecture
- Dense Transformer
- Total Parameters
- 27B
- Active Params/Token
- Unknown
- Context Window
- 8K tokens
- License
- open
- Modalities
- text
- HuggingFace
- google/gemma-2-27b-it
VRAM Requirements by Quantization & Context Length
All values in gigabytes (GB). Calculated using the KV-cache formula with model-specific head fractions. “–” means the context length exceeds this model’s maximum context window. Lower quantization = less VRAM but slightly reduced output quality.
| Quantization | 4K ctx | 8K ctx | 32K ctx | 128K ctx |
|---|---|---|---|---|
| Q4_K_M | 17.8 GB | 18.5 GB | – | – |
| Q5_K_M | 20.4 GB | 21.1 GB | – | – |
| Q6_K | 23.1 GB | 23.8 GB | – | – |
| Q8_0 | 29.2 GB | 29.9 GB | – | – |
| FP16 | 52.7 GB | 53.4 GB | – | – |
Weights = 27B params × GGUF bits per weight (Q4_K_M ≈ 4.9); plus runtime overhead; plus fp16 KV cache from config.json: 16 KV heads × 128 dims (23 of 46 layers capped at a 4,096-token window) — ~368 KB per token at short context. Same engine as the Can I Run LLM calculator.
Best GPU for Gemma 2 27B by Budget
Recommendations assume Q4_K_M quantization at 4K context unless stated otherwise. Higher-end quantizations or longer context windows require more VRAM — consult the table above.
- Under $500 (Entry GPU) Not feasible at this budget
- $500–$1,000 (Mid-Range GPU) RTX 3090 24GB (Q4_K_M)
- $1,000–$3,000 (High-End GPU) RTX 5090 32GB (Q4_K_M)
- $3,000+ (Workstation / Cloud) A100 80GB (Q4_K_M)
Benchmark Scores
Scores reported by Google or verified third-party evaluations. Higher is better for all benchmarks except where noted.
| Benchmark | Score |
|---|---|
| HumanEval | 51.8 |
| GSM8K | 74 |
| ARC-Challenge | 71.4 |
| MMLU-Pro | 59.6 |
| Arena Elo (Text) | 1231 |
| AIME 2024–25 (Epoch AI) | 1.4 |
| GPQA Diamond | 36.5 |
How to Run Gemma 2 27B Locally
Before downloading, verify your GPU has sufficient VRAM using the table above. Insufficient VRAM will cause the model to fall back to CPU offloading, which is significantly slower.
Option A — Ollama (Recommended for Beginners)
Ollama is the easiest way to run Gemma 2 27B locally. It handles model downloading, GGUF quantization selection, and serving automatically.
- Install Ollama from ollama.com
- Pull the model:
ollama pull gemma2:27b
- Run interactively:
ollama run gemma2:27b
Option B — HuggingFace Hub
For more control over quantization format, download directly from HuggingFace:
pip install huggingface_hub huggingface-cli download google/gemma-2-27b-it
Strengths and Use Cases
A capable open-weight model you can run locally. See the VRAM table above for exact requirements by quantization and context length.
When choosing a local LLM, Gemma 2 27B is worth considering if your workload aligns with its design goals. As a Dense Transformer architecture from Google, it offers a distinct trade-off between compute efficiency and capability. Its dense architecture provides consistent, predictable performance across a wide range of tasks. Whether you are building a local AI pipeline, experimenting with self-hosted chat, or running automated workflows, understanding this model's hardware envelope helps you plan infrastructure realistically.
For deployment, start with the Q4_K_M quantization unless you have headroom for higher precision. Q5_K_M and Q6_K offer improved output quality at the cost of additional VRAM. FP16 is generally only practical on high-VRAM workstation GPUs or cloud instances. Always verify context length requirements before selecting a quantization — longer context windows multiply KV-cache memory consumption significantly, as shown in the VRAM table above.
Ready to Check Your Hardware?
Use our interactive calculator to see exactly whether your GPU can run Gemma 2 27B — and at what quantization level.
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