Gemma 2 2B
By Google · United States
Updated 2026-07-13
Overview
Google's Gemma 2 2B, a compact instruct model distilled from larger Gemmas. Small enough to run on a Raspberry Pi 5 or modest CPU.
When to pick this model
- Edge devices, microservers, and SBCs
- Background tasks where latency beats sophistication
- Text classification, simple summarization, and routing
- Educational and demo deployments
- Fallback model when GPU resources are unavailable
VRAM requirements by quantization
| Quantization | VRAM required |
|---|---|
| Q4_K_M (recommended) | 1.8 GB |
| Q5_K_M | 2.2 GB |
| Q8_0 | 3.2 GB |
| FP16 (no quantization) | 5 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 2B fits an 8 GB consumer card at Q4_K_M (1.8 GB). Stepping up to Q8_0 nearly doubles the footprint to 3.2 GB, and unquantized FP16 weights take 5 GB — plan your GPU around the Q4 or Q5 figure unless you specifically need the higher fidelity.
Without a GPU, Gemma 2 2B needs roughly 4 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 35 tokens/sec on entry-level GPUs, on the order of 100 tokens/sec on a mid-range card, and up to 200 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 2B 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 2B |
|---|---|---|
| 8 GB | RTX 5070 Laptop, RTX 5060, RTX 5060 Ti 8GB | FP16 (5 GB used) |
| 12 GB | RTX 5070, RTX 5070 Ti Laptop, RTX 4080 Laptop | FP16 (5 GB used) |
| 16 GB | RTX 5080, RTX 4080 Super, Radeon RX 9070 XT | FP16 (5 GB used) |
| 24 GB | RTX 4090, Radeon RX 7900 XTX, RTX 5090 Laptop | FP16 (5 GB used) |
| 32 GB | RTX 5090 | FP16 (5 GB used) |
Which GPU should you buy to run Gemma 2 2B?
To run Gemma 2 2B locally at Q4, you need ~1.8 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 | 52.2 |
| HellaSwag | 74.9 |
Scores published by the model author or aggregated from public leaderboards. Re-measured monthly by our editorial team.
To put Gemma 2 2B in context: its MMLU score of 52.2 ranks #33 of the 34 catalog models with a published MMLU result (catalog median 73.4). Scores are author-published and measured under different harnesses, so treat gaps of a few points as noise rather than a verdict.
Strengths
- Runs comfortably in under 2GB VRAM at Q4
- Best-in-class 2B quality for its release window
- Workable on commodity CPUs
- Google's Gemma license permits broad use
Limitations
- 8k context is restrictive for modern RAG
- Falls apart on multi-step reasoning
- No vision, no tool calling out of the box
Typical workloads
In our catalog grid, Gemma 2 2B is filed under Edge, Mobile, Fast Summary — 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 2B · logit-softcapping + local/global attention
Training: 3T tokens, compact Google architecture distilled from larger models.
The best 2B for edge and CPU workloads — just don't expect it to reason.
Quick start
ollama run gemma2:2bOr 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 2B need?
At the recommended Q4_K_M quantization, Gemma 2 2B needs about 1.8 GB of VRAM. Q8_0 takes 3.2 GB, and unquantized FP16 weights take 5 GB.
Can Gemma 2 2B run without a GPU?
Yes — with roughly 4 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 2B support?
Gemma 2 2B supports a 8k-token context window (8,192 tokens).
Can I use Gemma 2 2B commercially?
Gemma 2 2B 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 2B on consumer hardware?
Our compatibility engine estimates on the order of 100 tokens/sec on a mid-range GPU and up to 200 tokens/sec on high-end cards, assuming the quantization fully fits in VRAM.
Which quantization of Gemma 2 2B should I download first?
Start with Q4_K_M (1.8 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 FP16.