Gus

Gemma Product Manager @google DeepMind - Gemma 💎 - Google AI ⚙️🧠

Articles & links

What would happen if we tried the diffusion generation on LLMs? We get Diffusion Gemma! 4x speed up! ⚡⚡⚡💎 blog.google/innovation-a...

DiffusionGemma: 4x faster text generation blog.google
AI Weekly's analysis
  • DiffusionGemma generates 256 tokens per forward pass using bidirectional attention, reaching 1,000+ tokens/sec on a single H100 GPU.
  • With only 3.8B active parameters during inference and an 18GB VRAM footprint when quantized, it runs on consumer hardware without server-grade resources.
  • Google recommends DiffusionGemma only for speed-critical workloads like in-line editing and code infilling, not for applications requiring maximum quality.
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View on Bluesky · ♥ 24 ↻ 3 ↩ 1 · 6 from the directory shared this · 27d ago

Gemma 4 12B is live! 🚀 An encoder-free multimodal model (text/img/audio) for local 16GB laptops. Elite reasoning nearing 26B MoE in half the size, fast, and open (Apache 2.0). This is the main reason I was not posting much!! Glad it is launched!! blog.google/innovation-a...

Introducing Gemma 4 12B: a unified, encoder-free multimodal model blog.google
AI Weekly's analysis
  • The 35M-parameter vision embedder replaces 27 vision transformer layers, keeping the full model inside 16GB with complete image and audio understanding.
  • Audio projects from raw 16 kHz waveforms in 40ms frames directly to the LLM backbone, bypassing any separate ASR encoder used in competing designs.
  • Single-pass LoRA fine-tuning updates vision, audio, and text weights simultaneously, eliminating the engineering overhead of co-tuning frozen encoders.
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View on Bluesky · ♥ 111 ↻ 16 ↩ 8 · 2 from the directory shared this · 35d ago

Gemma 4 technical report is out! lots of cool stuff, check it out! arxiv.org/abs/2607.02770

[2607.02770] Gemma 4 Technical Report arxiv.org
AI Weekly's analysis
  • Gemma 4 is a new open-weight multimodal family spanning 2.3B to 31B parameters, with both dense and Mixture-of-Experts variants.
  • The 12B model uses a unified, encoder-free architecture that ingests raw audio and image patches directly.
  • A thinking mode lets Gemma 4 emit reasoning traces before responding, with claimed leaps on STEM, multimodal and long-context benchmarks.
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View on Bluesky · ♥ 75 ↻ 10 ↩ 1 · 2 from the directory shared this · 1d ago

This is a great use of Gemma! having an open model running at +1000 tokens per second can enable some pretty cool use cases! The voice assistant is a good one, but I'm sure there are many others! huggingface.co/blog/cerebra...

Hugging Face and Cerebras bring Gemma 4 to real-time voice AI huggingface.co
AI Weekly's analysis
  • Hugging Face and Cerebras have shipped an open cascaded speech-to-speech pipeline chaining Nvidia's Parakeet, Google DeepMind's Gemma 4 VLM on Cerebras, and Alibaba's Qwen3TTS.
  • The pitch focuses on P95 tail latency stability, not median speed, arguing that occasional multi-second stalls are what break conversational voice apps.
  • Hugging Face says the same pipeline already powers more than 9,000 Reachy Mini robots in the wild, giving the demo a real deployment story.
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View on Bluesky · ♥ 25 ↻ 3 ↩ 4 · 2 from the directory shared this · 7d ago

great post about AI trends specifically mentioning the Gemma 4 success!! 🤩 www.interconnects.ai/p/some-ideas...

interconnects.ai
View on Bluesky · ♥ 1 ↻ 0 ↩ 0 · 3 from the directory shared this · 43d ago

I've been playing all day with Hugging Face Chat + Gemma 4 31B (deployed on Cerebras at +1000 tokens per second) and I'm still surprised how amazing it is!! I play with Gemma models basically everyday and this integration + speed still surprised me! take a look: huggingface.co…

google/gemma-4-31B-it - HuggingChat huggingface.co
View on Bluesky · ♥ 7 ↻ 1 ↩ 1 · 6d ago

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