Scan First: Function Gemma ships at 270 million parameters and processes nearly 2000 tokens per second prefill on a Pixel 7. 00:00 What quantization is 00:33 Why quantization matters 00:42 GPU compute vs memory bandwidth 02:12 How

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00:00 What quantization is 00:33 Why quantization matters 00:42 GPU compute vs memory bandwidth 02:12 How zml/attnd replaces dense attention with a sparse, predictive attention algorithm that operates in log-linear time, dramatically ... Function Gemma ships at 270 million parameters and processes nearly 2000 tokens per second prefill on a Pixel 7.

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  • 00:00 What quantization is 00:33 Why quantization matters 00:42 GPU compute vs memory bandwidth 02:12 How
  • zml/attnd replaces dense attention with a sparse, predictive attention algorithm that operates in log-linear time, dramatically ...
  • Function Gemma ships at 270 million parameters and processes nearly 2000 tokens per second prefill on a Pixel 7.

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Visual Notes

How to make your AI models faster, smaller, cheaper, greener? - AI Engineer Paris
LLM Quantization: Smaller, Faster, Cheaper AI Models
State of Open LLMs in 2025 - AI Engineer Paris 2025
Towards unlimited contexts: faster-than-GPU sparse logarithmic attention on CPU - AI Engineer Paris
Rewriting all of Spotify's code base, all the time. - AI Engineer Paris 2025
Building MCP's at GitHub Scale - AI Engineer Paris 2025
From 46% to 90%: Fine-Tuning Tiny LLMs for On-Device Agents — Cormac Brick, Google
Everything I Learned Training Frontier Small Models — Maxime Labonne, Liquid AI
Malleable Evals: Why Are We Evaluating Adaptive Systems with Static Tests? — Vincent Koc, OpenClaw
Fast Models Need Slow Developers — Sarah Chieng, Cerebras
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How to make your AI models faster, smaller, cheaper, greener? - AI Engineer Paris

How to make your AI models faster, smaller, cheaper, greener? - AI Engineer Paris

Read more details and related context about How to make your AI models faster, smaller, cheaper, greener? - AI Engineer Paris.

LLM Quantization: Smaller, Faster, Cheaper AI Models

LLM Quantization: Smaller, Faster, Cheaper AI Models

00:00 What quantization is 00:33 Why quantization matters 00:42 GPU compute vs memory bandwidth 02:12 How

State of Open LLMs in 2025 - AI Engineer Paris 2025

State of Open LLMs in 2025 - AI Engineer Paris 2025

Read more details and related context about State of Open LLMs in 2025 - AI Engineer Paris 2025.

Towards unlimited contexts: faster-than-GPU sparse logarithmic attention on CPU - AI Engineer Paris

Towards unlimited contexts: faster-than-GPU sparse logarithmic attention on CPU - AI Engineer Paris

zml/attnd replaces dense attention with a sparse, predictive attention algorithm that operates in log-linear time, dramatically ...

Rewriting all of Spotify's code base, all the time. - AI Engineer Paris 2025

Rewriting all of Spotify's code base, all the time. - AI Engineer Paris 2025

Read more details and related context about Rewriting all of Spotify's code base, all the time. - AI Engineer Paris 2025.

Building MCP's at GitHub Scale - AI Engineer Paris 2025

Building MCP's at GitHub Scale - AI Engineer Paris 2025

Read more details and related context about Building MCP's at GitHub Scale - AI Engineer Paris 2025.

From 46% to 90%: Fine-Tuning Tiny LLMs for On-Device Agents — Cormac Brick, Google

From 46% to 90%: Fine-Tuning Tiny LLMs for On-Device Agents — Cormac Brick, Google

Function Gemma ships at 270 million parameters and processes nearly 2000 tokens per second prefill on a Pixel 7. Out of

Everything I Learned Training Frontier Small Models — Maxime Labonne, Liquid AI

Everything I Learned Training Frontier Small Models — Maxime Labonne, Liquid AI

Read more details and related context about Everything I Learned Training Frontier Small Models — Maxime Labonne, Liquid AI.

Malleable Evals: Why Are We Evaluating Adaptive Systems with Static Tests? — Vincent Koc, OpenClaw

Malleable Evals: Why Are We Evaluating Adaptive Systems with Static Tests? — Vincent Koc, OpenClaw

Read more details and related context about Malleable Evals: Why Are We Evaluating Adaptive Systems with Static Tests? — Vincent Koc, OpenClaw.

Fast Models Need Slow Developers — Sarah Chieng, Cerebras

Fast Models Need Slow Developers — Sarah Chieng, Cerebras

Read more details and related context about Fast Models Need Slow Developers — Sarah Chieng, Cerebras.