Gemma
product on 14 shows · 12 statements across 9 episodes · said 120 times in 44 episodes since 2024
Latent Space 61 
the Startup Ideas Podcast 25 
the Y Combinator Startup Podcast 7 
the MAD Podcast 7 
TBPN 5 
Big Technology 4 
20VC 4 
All-In 2 
We Live to Build 1 
American Optimist 1 
the Neon Show 1 
Sourcery 1 
the a16z Podcast 1 
Cheeky Pint
Mentions by year, every show
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Latent Space 61the Startup Ideas Podcast 25the MAD Podcast 7the Y Combinator Startup Podcast 7TBPN 5Big Technology 420VC 4All-In 25 more shows
2026 78 mentions in 21 episodes 4 per episode
- I'm Obsessed With Local AI. Here's Why
- ⚡️ Google's Open AI Strategy — Omar Sanseviero, Google DeepMind
- Demis Hassabis: Agents, AGI & The Next Big Scientific Breakthrough · Y Combinator
- Next 100x in AI: Inference, Networking, & Self-Optimizing Models — Philip Kiely & Ali Taha, Baseten
- Arena CEO: There Will be a $100BN US Open-Source Model & Data is a Trillion Dollar Market
- The Inference Frontier: from 100 to 10,000 tokens per second — Sean Lie, Cerebras CTO
- More Details on OpenAI-Hugging Face, SpaceX SPV Investors Get Rugged, Demis and DeepMind | Diet TBPN
- The Biggest AI Deployment Nobody Talks About | Samsara CEO Sanjit Biswas
- 13 more episodes that year, every mention in 2026 →
2025 38 mentions in 20 episodes 2 per episode
- The Utility of Interpretability — Emmanuel Amiesen
- Information Theory for Language Models: Jack Morris
- Better Data is All You Need — Ari Morcos, Datology
- Google DeepMind CTO: Advancing AI Frontier, New Reasoning Methods, Video Generation’s Potential
- Jeremy Howard on Building 5,000 AI Products with 14 People (Answer AI Deep-Dive)
- Erotic ChatGPT, Zuck’s Apple Assault, AI’s Sameness Problem
- ⚡ Open Model Pretraining Masterclass — Elie Bakouch, HuggingFace SmolLM 3, FineWeb, FinePDF
- Google Makes MAJOR Cancer Research Breakthrough | Diet TBPN
- 12 more episodes that year, every mention in 2025 →
2024 4 mentions in 3 episodes 1 per episode
every mention on every show, scene by scene, with the transcript →
12 statements about Gemma, every show
Angelopoulos: Google's Gemma sits on the performance-versus-cost Pareto curve
“Gemma, by the way, is pretty good in terms of efficiency. If you look at arena, you'll see the, on the Pareto curves of like performance versus cost. Gemma's on there.”
Cerebras cloud achieves 10x inference speedup over fast GPUs on Gemma
“Say, if you run Gemma four, On your GP, you might get like a hundred tokens per second if you have a fast card. If you run it in their cloud, you get anywhere from 800 to 1500 tokens per second. So call it 10 X faster.”
Sanseviero: Gemini Nano on Pixel and Samsung phones is built on Gemma
“If you buy a Pixel phone or a high-end Samsung, they come with a Gemini Nano, and Gemini Nano is packed into the operating system, and Gemini Nano is really built on top of Gemma.”
Sanseviero: Smaller Gemma 4 models process audio and 30-60 second videos
“Multimodal wise, the smaller models can understand audio Images and short videos, so, 30 to 62nd videos and audios.”
Sanseviero: 31B is the largest quantized model fitting consumer GPUs
“The 31 is really like the largest model size that quantize would fit in a consumer GPU.”
Soldaini: Most open AI models are open weights, not open source
“Majority of models that get release I think the best term to describe them is open weights. Your Quinn, your Gemma, your Lama you know, Kimi it's what gets release is a set of weights that correspond either to the final state of model, that's the most common, …”
Kantrowitz: DeepMind and Yale Cancer AI Model Validated in Living Cells
“The DeepMind researchers in collaboration with Yale released a twenty seven billion parameter foundational model for single cell analysis. I'm not even going to try to name it. It's called C to S scale in shorthand. It's built on Google's open source gamma fam…”
Morcos: Arcee 4.5B beat Gemma before reaching one trillion tokens
“It was beating Gemma pretty consistently before the one trillion mark, which was pretty cool to see.”
Morris: Open model creators do not use differential privacy or anonymization
“I would be extremely surprised if they do any type of like private training. Like there are these mechanisms for doing like differentially private language model training, or even just anonymization in the pre-training pipeline. I bet they don't do any of that…”
Ameisen: Multi-Hop Reasoning Circuits Are Extremely Similar Across Small and Large Models
“The way the circuit looks in Gemma, like a really small model is extremely similar to the way that it looks like a huge model, which that in itself is, I think like a pretty novel discovery. It's like, oh, you have these models that are like super different. Y…”
Vibhu: Gemma Activates Abstract Behavioral Traits Over Simple Token Completion
“It also shows internally that there's more than just token completion of, you know, this plus this equals this. No, it has some under understanding of characteristics, right? Like this is a pretty stubborn dog. It has a stubborn feature. Pretty high up that ac…”
Agarwal: Filtered 9B Synthetic Data Outperforms 27B Self-Generated Data
“One thing we found consistently, so here what we had two models, nine Gemma, nine B and Gemma, 27 B, and we found consistently that actually generating data from nine B in a compute match setting is always better, even better for distilling or actually improvi…”