What Is RecurrentGemma?
RecurrentGemma is Google DeepMind's implementation of the Griffin architecture — a hybrid model design that replaces most of the global attention in standard transformers with linear recurrent layers, keeping local windowed attention only where it's most useful.
The Griffin architecture was introduced in DeepMind's research as a way to achieve transformer-competitive performance while reducing the memory and compute demands that come with full attention. RecurrentGemma puts this research into practice as a deployable open-weight model series, available in 2B and 9B parameter variants.
For local deployment, the consequence of this architecture is straightforward: RecurrentGemma runs faster and uses less memory than standard Gemma 2 at equivalent sizes — giving you more responsiveness from the same hardware, or the ability to run the model at all on hardware where standard Gemma would be too heavy.
Griffin architecture in plain terms: Standard transformer models attend to every previous token at every layer — powerful but expensive. Griffin replaces most of those layers with recurrent processing that builds up a compressed representation of previous context, using actual attention only within a local window of nearby tokens. The result processes sequences faster and with constant memory overhead regardless of sequence length.
Source: Wikimedia Commons
Model Variants
RecurrentGemma 2B
2B ParametersThe lightweight entry point. Runs on minimal hardware — under 3GB RAM, no dedicated GPU required. Fast responses even on CPU inference. The right starting point for evaluating the Griffin architecture or deploying on genuinely constrained hardware like older laptops, low-spec servers, or edge devices.
RecurrentGemma 9B
9B ParametersThe full-capability variant. Meaningfully better output quality than the 2B while retaining the architectural efficiency advantages. Requires a mid-range GPU (7–8GB VRAM) but generates responses noticeably faster than a standard Gemma 2 9B on the same hardware. The practical choice for teams that need real output quality with efficient inference.
RecurrentGemma vs. Standard Gemma 2
Both come from Google DeepMind, but they make different tradeoffs:
| Feature | RecurrentGemma | Gemma 2 |
|---|---|---|
| Architecture | Griffin (recurrent + local attention) | Standard transformer |
| Sizes available | 2B, 9B | 2B, 9B, 27B |
| Inference speed | Faster | Baseline |
| Memory use | Lower | Higher |
| Benchmark quality | Good | Stronger at same size |
| Best for | Speed, constrained hardware | Maximum output quality |
Gemma 2 produces better benchmark results than RecurrentGemma at comparable sizes — but RecurrentGemma runs faster and uses less memory on the same hardware. If your hardware can comfortably run Gemma 2 9B and output quality is the priority, use Gemma 2. If you're at the edge of your memory budget, need faster responses, or want to understand the Griffin architecture on your own tasks, RecurrentGemma is the right choice.
RecurrentGemma vs. Zamba2
Both RecurrentGemma and Zamba2 are non-transformer hybrid architectures aimed at efficient local deployment. The key differences:
| Feature | RecurrentGemma | Zamba2 |
|---|---|---|
| Architecture | Griffin (recurrence + local attention) | Mamba + attention |
| Developer | Google DeepMind | Zyphra |
| Sizes | 2B, 9B | 7B |
| Smallest variant | 2B — very low resource | 7B — moderate resource |
| Institutional backing | Large research org | Startup |
RecurrentGemma's 2B variant is the lighter option if your hardware constraints are severe. Zamba2 7B sits in the mid-range where the Mamba architecture competes more directly with standard 7B transformer models. Running both on your hardware and comparing output quality on your actual tasks is the most informative approach.
Hardware Requirements
| Model | VRAM Required | RAM (CPU) | Notes |
|---|---|---|---|
| RecurrentGemma 2B | ~2.5 GB | ~6 GB | Runs on minimal hardware; no GPU required |
| RecurrentGemma 9B | ~7 GB | ~16 GB | Mid-range GPU recommended for responsive use |
Deploying RecurrentGemma via Ollama
ollama pull recurrentgemma:2b
# RecurrentGemma 9B — better quality, still efficient
ollama pull recurrentgemma:9b
# Start Open WebUI
docker run -d -p 3000:8080 \
-e OLLAMA_BASE_URL=http://localhost:11434 \
ghcr.io/open-webui/open-webui:main
Open http://localhost:3000 and select recurrentgemma:2b or recurrentgemma:9b from the model dropdown. Response speed is noticeably faster than standard transformer models of comparable size — the architectural advantage is immediately apparent in interactive use.
Use Cases
Minimal Hardware Deployment
The 2B variant runs on any modern machine with 6GB RAM — no GPU, no special hardware. For personal use on older laptops or budget servers, RecurrentGemma 2B is one of the most capable options available at this resource level.
Fast Interactive Assistance
Faster token generation means more responsive conversations. For open-ended use where users notice latency, the speed advantage over standard Gemma is a real quality-of-experience improvement.
Side-by-Side Architecture Comparison
Run RecurrentGemma alongside standard Gemma 2 in Open WebUI and route the same queries to both. Concrete data on the speed vs. quality tradeoff in your actual use cases is more useful than published benchmarks.
Multi-Model Environments
RecurrentGemma's low memory footprint lets it run alongside larger primary models without GPU contention. Use it for quick tasks, routing more complex work to a larger model when needed.
Edge and Embedded Deployment
Devices with strict power and memory budgets — edge servers, on-device AI, offline field tools — benefit from RecurrentGemma's combination of quality and efficiency where transformer models are impractical.
Architecture Research
Teams evaluating non-transformer architectures for their own AI development get a well-backed, production-quality implementation of the Griffin architecture to benchmark against transformer baselines.
The Efficiency Architecture Moment
The LLM landscape in 2024–2025 is seeing genuine architectural experimentation: Mamba, Griffin, RWKV, and various hybrid approaches are competing to show that transformers aren't the only viable path to capable language models. RecurrentGemma is Google DeepMind's contribution — a serious effort from a well-resourced research team, not a hobbyist project.
For local deployment specifically, this experimentation matters practically. If an architecture achieves 85% of transformer quality at 60% of the memory cost, that's not a research curiosity — it's a real improvement for anyone running models on their own hardware. RecurrentGemma represents a concrete, deployable example of that tradeoff, available now through Ollama.
Deployment Options We Offer
Lightsail Entry-Level (RecurrentGemma 2B)
Low-cost Lightsail instances configured with Ollama and RecurrentGemma 2B — accessible private AI at minimal monthly cost. Suitable for individuals and small teams where budget is the primary constraint.
Local Installation (RecurrentGemma 9B)
We configure RecurrentGemma 9B with Open WebUI on your existing hardware as a persistent service. Lower memory requirements compared to equivalent transformer models mean it runs comfortably alongside other applications on shared workstations.