Gemma 2 9B
By Google · United States
Updated 2026-07-13
Overview
Google's Gemma 2 9B, a distilled instruct model that outperforms Llama 3 8B on several benchmarks at a slightly larger size.
When to pick this model
- General-purpose chat with stronger output quality than Llama 3 8B
- Workloads that don't need a long context window
- Instruction-following tasks and structured output
- Single consumer GPU deployments
- Fine-tuning baselines under Google's Gemma license
VRAM requirements by quantization
| Quantization | VRAM required |
|---|---|
| Q4_K_M (recommended) | 6 GB |
| Q5_K_M | 7.5 GB |
| Q8_0 | 11 GB |
| FP16 (no quantization) | 20 GB |
VRAM figures include model weights plus a typical 8k KV cache and ~600 MB runtime overhead (Ollama / llama.cpp baseline). Add headroom for higher context lengths.
In practice, Gemma 2 9B fits an 8 GB consumer card at Q4_K_M (6 GB). Stepping up to Q8_0 nearly doubles the footprint to 11 GB, and unquantized FP16 weights take 20 GB — plan your GPU around the Q4 or Q5 figure unless you specifically need the higher fidelity.
Without a GPU, Gemma 2 9B needs roughly 12 GB of system RAM to run on CPU via llama.cpp or Ollama — workable for background jobs, but far slower than GPU inference. Throughput estimates from our compatibility engine: around 9 tokens/sec on entry-level GPUs, on the order of 28 tokens/sec on a mid-range card, and up to 75 tokens/sec on high-end hardware — assuming the chosen quantization fully fits in VRAM.
What hardware do you need
The table below matches Gemma 2 9B to common GPU memory tiers, using the highest-fidelity quantization that fully fits each card class. Spilling layers to system RAM works but costs most of the speed, so size your card to the quantization you actually want to run.
| GPU memory | Example cards | Best fit for Gemma 2 9B |
|---|---|---|
| 8 GB | RTX 5070 Laptop, RTX 5060, RTX 5060 Ti 8GB | Q5_K_M (7.5 GB used) |
| 12 GB | RTX 5070, RTX 5070 Ti Laptop, RTX 4080 Laptop | Q8_0 (11 GB used) |
| 16 GB | RTX 5080, RTX 4080 Super, Radeon RX 9070 XT | Q8_0 (11 GB used) |
| 24 GB | RTX 4090, Radeon RX 7900 XTX, RTX 5090 Laptop | FP16 (20 GB used) |
| 32 GB | RTX 5090 | FP16 (20 GB used) |
Which GPU should you buy to run Gemma 2 9B?
To run Gemma 2 9B locally at Q4, you need ~6 GB of VRAM. The best value for this is a RTX 5060 (8 GB VRAM).
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Published benchmark scores
| Benchmark | Score |
|---|---|
| MMLU | 71.3 |
| HellaSwag | 87.2 |
| HumanEval | 40.2 |
Scores published by the model author or aggregated from public leaderboards. Re-measured monthly by our editorial team.
To put Gemma 2 9B in context: its MMLU score of 71.3 ranks #20 of the 34 catalog models with a published MMLU result (catalog median 73.4); its HumanEval score of 40.2 ranks #25 of the 26 catalog models with a published HumanEval result (catalog median 81.1). Scores are author-published and measured under different harnesses, so treat gaps of a few points as noise rather than a verdict.
Strengths
- Beats Llama 3 8B on multiple benchmarks
- Solid quality-per-parameter
- Reliable instruction following
- Distilled from Gemma 2 27B for better quality density
Limitations
- 8k context is the standout limitation
- No vision capabilities
- Gemma license is more restrictive than Apache 2.0
Typical workloads
In our catalog grid, Gemma 2 9B is filed under Chat, Translation — the use cases where its size/quality trade-off makes the most sense. Its tags translate to concrete workloads: everyday chat, drafting and summarization.
Note the 8k-token context window — fine for short interactions, limiting for long documents or big retrieval contexts. It ships under the Gemma license — commercial use is generally possible but read the specific terms before embedding it in a product.
Architecture & training
Architecture: Dense Transformer · Gemma 2 9B · sliding window attention
Training: 8T tokens. Architecture distilled from Gemma 2 27B.
A strong 9B if you can live with 8k context — otherwise pick Qwen 2.5 7B or Llama 3.1 8B for the 128k window.
Quick start
ollama run gemma2:9bOr use the open-source MCP server to query this model from Claude Desktop, Cursor, or any MCP-compatible client.
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Frequently asked questions
How much VRAM does Gemma 2 9B need?
At the recommended Q4_K_M quantization, Gemma 2 9B needs about 6 GB of VRAM. Q8_0 takes 11 GB, and unquantized FP16 weights take 20 GB.
Can Gemma 2 9B run without a GPU?
Yes — with roughly 12 GB of system RAM it runs CPU-only through llama.cpp or Ollama. Expect a fraction of GPU speed, which is fine for background or batch jobs but slow for interactive chat.
What context window does Gemma 2 9B support?
Gemma 2 9B supports a 8k-token context window (8,192 tokens).
Can I use Gemma 2 9B commercially?
Gemma 2 9B ships under the Gemma license. Commercial use is generally permitted subject to its terms — review the license text before shipping a product.
How fast is Gemma 2 9B on consumer hardware?
Our compatibility engine estimates on the order of 28 tokens/sec on a mid-range GPU and up to 75 tokens/sec on high-end cards, assuming the quantization fully fits in VRAM.
Which quantization of Gemma 2 9B should I download first?
Start with Q4_K_M (6 GB) — the standard size/quality sweet spot. Step up to Q5_K_M or Q8_0 only if you have VRAM headroom. It already fits an 8 GB card at Q5_K_M.