Articles tagged "Google DeepMind"

A primary-source deep dive on **DiffusionGemma** (`google/diffusiongemma-26B-A4B-it`, 25.2B total / 3.8B active MoE), released June 10, 2026 by Google DeepMind in coordination with NVIDIA. Grounded in the [official Google blog](https://blog.google/innovation-and-ai/technology/developers-tools/diffusion-gemma-faster-text-generation/), [ai.google.dev model card](https://ai.google.dev/gemma/docs/diffusiongemma/model_card), [Hugging Face card](https://huggingface.co/google/diffusiongemma-26B-A4B-it), and [NVIDIA's blog](https://blogs.nvidia.com/blog/rtx-ai-garage-local-gemma-diffusion/). Where autoregressive (AR) models generate one token at a time left-to-right, diffusion language models (DLMs) **denoise a 256-token canvas in parallel into final text**. 15-20 tokens commit per forward pass, up to 48 denoising steps, 1,000+ tok/sec on H100, 700+ on RTX 5090, ~3.5–4× the throughput of the AR Gemma 4 counterpart. Crucially, Google **openly states that quality lags AR**: MMLU Pro 77.6 vs 82.6, GPQA 73.2 vs 82.3, MMMU Pro 54.3 vs 73.8. Apache 2.0, distributed via Hugging Face / Vertex AI / NVIDIA NIM — the first large-scale open-weight diffusion LLM in the industry. The column covers practical implications for Japanese enterprises (on-prem internal agents, code editing, low-latency workflows) and positioning against Mercury (Inception Labs), LLaDA, and Gemini Diffusion.

A deep dive into Gemma 4 12B, released by Google DeepMind on June 3, 2026, grounded in the [official announcement](https://blog.google/innovation-and-ai/technology/developers-tools/introducing-gemma-4-12b/) and [Developer Guide](https://developers.googleblog.com/gemma-4-12b-the-developer-guide/). The standout property is **encoder-free multimodal architecture** — replacing the prior vision encoder (~550M parameters) with a 35M-parameter lightweight embedder plus a single matrix multiplication, and removing the 12-layer Conformer audio encoder entirely by projecting raw audio straight into the LLM's embedding space. Runs on a 16GB VRAM laptop (Copilot+ PC or Apple Silicon Mac), shipped under Apache 2.0, available through Hugging Face / Ollama / LM Studio / MLX / Vertex AI on day one. Covers the architectural rationale, the "approaches 26B MoE at less than half the memory" benchmark claim, positioning within the Gemma 4 family (E2B / E4B / 26B / 31B), competitive comparison against Llama 4 / Qwen 3.5 / Phi-5, and the fit with Japanese enterprise on-prem AI, voice workflows, and data-sovereignty requirements.

Summary of Google's Deep Research and Deep Research Max, announced April 21, 2026, built on Gemini 3.1 Pro: MCP support, native visualizations, long-horizon research workflows, DeepSearchQA 93.3% / Humanity's Last Exam 54.6%, and paid preview availability via the Gemini API — based on official sources.