Inside Gemma 4 E2B: 5B Params Running Like 2.3B
dejanseo · x · 2026-08-15
An interactive explainer breaks down the Google DeepMind Gemma 4 E2B-it model. It stores 5B parameters but computes with an effective 2.3B thanks to per-layer embeddings, fitting in under 1.5 GB of memory. The post covers the architecture, self-attention mechanism, and why per-layer embeddings reduce compute costs, offering interactive demos for each concept.
More from Models
- Dev: Claude writes 'zero bugs' on healthcare apps, 10x token efficiency — repligate · 2026-10-16
- Shanghai AI Lab unveils Intern Lumina U2, a diffusion LLM unifying image, video and 3D tasks — Shanghai-AI-Laboratory · 2026-10-16
- This Week in Tech breaks down Gemini 4, OpenAI's Dots agents, and Anthropic's leaked IPO filing — LouMM · 2026-10-16
- Sentry CEO Reveals $8,972 Claude Bill as Opus 'Scope Creeps' Through Team Workloads — zeeg · 2026-10-16
- Opus 5.5 wins: Anthropic reclaims token and spend share from OpenAI in 6 days — FinanceYF5 · 2026-10-16
- will.deeb: asking why a minor lab benchmarked against a 2-month-old frontier model is usually a stupid question — willcb · 2026-10-16