Gemma 2 27B Hardware Requirements
Updated: November 2, 2026
Gemma 2 27B needs a 24 GB graphics card. ollama run gemma2:27b downloads a 15.6 GB 4-bit file that uses about 18 GB of memory at a 4K context and about 20 GB at its full 8K, so a 16 GB card is not enough. Google released it in June 2024 with 27 billion parameters and an 8,192-token context. Its successor is Gemma 4.
📊 Technical Specifications
Gemma 2 27B VRAM requirements by quantisation
At the default Q4_0 the model needs about 18 GB at 4K and 20 GB at 8K; Q4_K_M about 19 GB and 21 GB; Q5_K_M about 22 GB and 23.5 GB; Q6_K about 25 GB. Sizes are from the Ollama registry. Memory is file + key-value cache + about 1 GB of buffers, worked out rather than measured. With 46 layers and 16 key-value heads of size 128, the cache costs about 377 KB per token: 1.5 GB at 4K and 3.1 GB at 8K.
| Ollama tag | Quant | Download | Memory at 4K | Memory at 8K | Fits |
|---|---|---|---|---|---|
gemma2:27b-instruct-q2_K | Q2_K | 10.45 GB | ~13.0 GB | ~14.6 GB | 16 GB card |
gemma2:27b-instruct-q3_K_M | Q3_K_M | 13.42 GB | ~16.0 GB | ~17.5 GB | 24 GB card; a 16 GB card only at a 2K context |
gemma2:27b (q4_0, the default) | Q4_0 | 15.63 GB | ~18.2 GB | ~19.7 GB | 24 GB card (RTX 3090, 4090), 32 GB Mac |
gemma2:27b-instruct-q4_K_M | Q4_K_M | 16.65 GB | ~19.2 GB | ~20.7 GB | 24 GB card |
gemma2:27b-instruct-q5_K_M | Q5_K_M | 19.41 GB | ~22.0 GB | ~23.5 GB | 24 GB card, tight at 8K |
gemma2:27b-instruct-q6_K | Q6_K | 22.34 GB | ~24.9 GB | ~26.4 GB | 32 GB card or 48 GB Mac |
gemma2:27b-instruct-q8_0 | Q8_0 | 28.94 GB | ~31.5 GB | ~33.0 GB | 48 GB card or 48 GB Mac |
gemma2:27b-instruct-fp16 | FP16 | 54.46 GB | ~57.0 GB | ~58.6 GB | 80 GB card or 96 GB Mac |
On CPU only, 32 GB of system RAM runs the 4-bit file, slowly: speed is bounded by memory bandwidth divided by the 15.6 GB file, which on dual-channel DDR5-5600 (89.6 GB/s) is a ceiling of about 6 tok/s. The VRAM calculator runs the same arithmetic for other cards. For the newer generation at the same size, see Gemma 4 hardware requirements.
🔬 Technical Specifications
Model Details: Gemma 2 27B is Google's second-generation language model with 27 billion parameters designed for high-performance text generation and reasoning tasks.
Gemma 2 27B Base
Gemma 2 27B IT
🎯 Key Features
🏗️ Model Architecture & Development
Architecture Overview: Gemma 2 27B is a decoder-only transformer from Google DeepMind. The details below are from Table 1 and section 2 of the Gemma 2 technical report.
Layers and width
Attention
Vocabulary and training
🔬 Technical Innovations
📊 Performance Analysis
Benchmark Results: Google's figures for the pretrained 27B, from Table 13 of the Gemma 2 report. MMLU 5-shot 75.2, GSM8K 5-shot 74.0, MATH 42.3, BBH 74.9, HumanEval 51.8, MBPP 62.6. This site has not re-run them.
Performance Metrics
📊 Benchmark Results
Source: Gemma 2 Technical Report (arXiv:2408.00118), Table 13
💡 Key Strengths
⚖️ Model Comparison Analysis
Comparative Analysis: MMLU 5-shot for base models, each as published by its own vendor, so small gaps are within evaluation noise.
📊 MMLU 5-shot, vendor-reported
🏆 Gemma 2 27B Advantages
📊 Technical Strengths
🎯 Best Use Cases
| Model | Params | Ollama download | MMLU (vendor) | Command |
|---|---|---|---|---|
| Gemma 2 27B | 27B | 15.6 GB | 75.2 | ollama run gemma2:27b |
| Llama 3.1 70B | 70B | 42.5 GB | 79.3 | ollama run llama3.1:70b |
| Qwen 2.5 32B | 32B | 19.9 GB | 83.3 | ollama run qwen2.5:32b |
| Gemma 2 9B | 9B | 5.4 GB | 71.3 | ollama run gemma2:9b |
⚙️ Installation Guide
Step-by-step setup: Ollama is the short route (one command, 15.6 GB); the Hugging Face route below downloads the full 16-bit weights for use with Transformers.
Python Environment Setup
Install required Python packages and dependencies
Tokenizer Dependencies
Install tokenizer support packages
Model Download
Download Gemma 2 27B from Hugging Face Hub
Verification Test
Test model loading and basic functionality
💻 Hardware Requirements
System Specifications: the short version of the VRAM table above.
System Requirements
🎯 Use Cases & Applications
Practical Applications: jobs that fit inside an 8K context, where a general model of this size is enough.
🏢 Business Applications
- • Document analysis and summarization
- • Business intelligence and reporting
- • Customer support automation
- • Content creation and marketing
- • Internal knowledge management
🔬 Research & Development
- • Academic research assistance
- • Data analysis and interpretation
- • Literature review automation
- • Technical writing and documentation
- • Prototype development
💻 Development Tools
- • Code generation and completion
- • Technical documentation
- • Debug assistance
- • API development support
- • Software architecture planning
📝 Content Creation
- • Blog and article writing
- • Social media content
- • Email composition
- • Creative writing assistance
- • Translation and localization
📚 Resources & Documentation
Official Resources: the primary sources behind every figure on this page.
Gemma 2: Improving Open Language Models at a Practical Size (arXiv:2408.00118)
Architecture (Table 1), training tokens and every benchmark score on this page
Usage, chat template, licence and intended use
Ollama gemma2 tags
Every gemma2 tag and its download size
Gemma Terms of Use
The licence: commercial use allowed, with a prohibited-use policy
MMLU 5-shot accuracy. Source: Gemma 2 Technical Report (Google DeepMind)
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❓ Frequently Asked Questions
How much VRAM does Gemma 2 27B need?
The default gemma2:27b download is a 15.6 GB 4-bit file. Add the key-value cache (about 1.5 GB at 4K, 3.1 GB at the full 8K context) and roughly 1 GB of buffers and it needs about 18 GB, so plan on a 24 GB card such as an RTX 3090 or 4090, or a Mac with 32 GB or more. It does not fit a 16 GB card; only Q2_K (10.5 GB) does. Q4_K_M is 16.7 GB and needs about 19 GB. FP16 is 54.5 GB.
How does Gemma 2 27B compare to Llama 3.1 70B?
On MMLU, Google reports 75.2 for Gemma 2 27B and Meta reports 79.3 for Llama 3.1 70B. Gemma 2 27B is a 15.6 GB download against 42.5 GB, so it runs on one 24 GB card where the 70B needs two, but Llama 3.1 70B has a 128K context window against Gemma 2's 8K.
What is the license for Gemma 2 27B?
Gemma 2 27B uses the Google Gemma Terms of Use license, which permits commercial use but has some restrictions (e.g., no use for training competing models). Check the full terms at ai.google.dev/gemma/terms before commercial deployment.
How do I run Gemma 2 27B with Ollama?
Install Ollama from ollama.com, then run: ollama run gemma2:27b. That downloads the 15.6 GB Q4_0 instruct build. Other quantisations are separate tags, such as gemma2:27b-instruct-q4_K_M (16.7 GB). For the smaller 9B: ollama run gemma2:9b (5.4 GB).
Is Gemma 2 27B good for coding?
Gemma 2 27B scores 51.8% on HumanEval and 62.6% on MBPP in Google's report: usable, but not a code specialist. For coding, Qwen 2.5 Coder 32B or a newer code model is the better use of a 24 GB card.
What is Gemma 2 27B's context length?
8,192 tokens. Gemma 2 alternates layers with a 4,096-token sliding window and layers that attend over the full 8,192 tokens (Table 1 of the Gemma 2 report). Ollama starts with a 4K context below 24 GB of VRAM; set num_ctx to 8192 to use all of it.
Should I use Gemma 2 27B or Gemma 4?
For new work, Gemma 4. Its 26B and 31B Ollama tags (gemma4:26b, 18.7 GB; gemma4:31b, 20.4 GB) fit the same 24 GB cards, with far longer context than Gemma 2's 8K. Keep Gemma 2 27B if you have prompts or fine-tunes built on it.
Local AI Alternatives to Gemma 2 27B
| Model | Params | MMLU | Ollama download | Ollama Command | Best For |
|---|---|---|---|---|---|
| Gemma 2 27B | 27B | 75.2% | 15.6 GB | ollama run gemma2:27b | One 24 GB card, 8K context |
| Gemma 2 9B | 9B | 71.3% | 5.4 GB | ollama run gemma2:9b | Budget-friendly Google model |
| Qwen 2.5 32B | 32B | 83.3% | 19.9 GB | ollama run qwen2.5:32b | Multilingual + coding |
| Llama 3.1 70B | 70B | 79.3% | 42.5 GB | ollama run llama3.1:70b | 128K context, needs 48 GB |
| Mixtral 8x7B (MoE explained) | 46.7B (MoE) | 70.6% | 26.4 GB | ollama run mixtral | MoE architecture |
MMLU: base models, each vendor's own report or model card. Downloads: default Ollama tag. The newer Gemma 4 26B and 31B tags are 18.7 GB and 20.4 GB.
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