Cerebras Runs Google DeepMind's Gemma 4 at 1,500 Tokens per Second
Gemma 4 31B lands on Cerebras Inference at 1,500 tokens/sec — 15x faster than Claude Haiku — with a 24-hour hackathon kicking off this Sunday
- Speed milestone: Gemma 4 31B runs at 1,500+ tokens/sec on Cerebras — 15x faster than Claude Haiku at comparable quality.
- First multimodal on Cerebras: Gemma 4 is the first image-capable model on the platform, enabling real-time vision + agentic loops.
- 24-hour hackathon this Sunday: Register on Luma for early access, $5K in prizes, and a Q&A with the Gemma 4 research team.
- Model specs: 30.7B dense params, 256K context window, Apache 2.0, supports OCR, PDF parsing, UI understanding, function calling, and 140+ languages.
- Private preview now, GA end of June: Available via Cerebras Inference; weights free on Hugging Face.
- Best for: Agentic loops, document/screenshot workflows, long-context summarization, and coding tasks where inference speed is the bottleneck.
Cerebras and Google DeepMind just crossed a milestone together: Gemma 4 31B is now running on Cerebras Inference, making it the first multimodal model on the platform and the first Google DeepMind model Cerebras has ever hosted. To celebrate, they're running a 24-hour virtual hackathon this Sunday with $5,000 in prizes and early API access for participants.
Speed that changes what's possible
The headline number is hard to ignore. Cerebras runs Gemma 4 at over 1,500 output tokens per second. By comparison, Claude Haiku runs at roughly 100 tokens per second. That's a 15x speedup against one of the most popular production-grade models on the market, at roughly equivalent quality.
But raw speed only matters if it changes what you can build. Here it does. Multimodal and agentic loops rarely call a model once: they inspect a visual input, reason over it, produce structured output, call tools, check the result, and try again. At 100 tokens per second those loops are too slow to provide real-time input. At 1,500 TPS, the application and user can work together at the same time. Front-end iteration feels near-instant, document and screenshot workflows return in a fraction of the time, and developers can fit more verification and more retries into the same product.
What Gemma 4 31B actually is
Gemma 4 31B is the flagship of Google DeepMind's open-weight Gemma family , a dense model (meaning all parameters are active on every forward pass, unlike sparse Mixture-of-Experts models) built for quality and efficiency rather than raw parameter count. Dense models achieve high model intelligence without the large memory footprint of MoE models. Gemma 4 hits a sweet spot: strong enough for serious work, efficient to serve, and open enough to build around without vendor lock-in.