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Gemma 4: Byte for byte, the most capable open models
Gemma 4: Byte for byte, the most capable open models Four new vision-capable Apache 2.0 licensed reasoning LLMs from Google DeepMind, sized at 2B, 4B, 31B, plus a 26B-A4B Mixture-of-Experts. Google emphasize "unprecedented level of intelligence-per-parameter", providing yet more evidence that creating small useful models is one of the hottest areas of research right now. They actually label the two smaller models as E2B and E4B for "Effective" parameter size. The system card explains: The smaller models incorporate Per-Layer Embeddings (PLE) to maximize parameter efficiency in on-device deployments. Rather than adding more layers or parameters to the model, PLE gives each decoder layer its own small embedding for every token. These embedding tables are large but are only used for quick lookups, which is why the effective parameter count is much smaller than the total. I don't entirely understand that, but apparently that's what the "E" in E2B means! I tried them out using th...