A good friend of mine is deep in the world of startups and spends a lot of his time doing idea validation—that is, trying to understand if there is a market for a given idea or product. Despite the fact that in the startup world success is eventually judged by sales or profits, whereas in ML4H it is more likely to be adoption by clinicians and, hopefully, an associated improvement in clinical outcomes, both rely on designing something that people actually want and will use. Therefore, I think many of the tools that help entrepreneurs validate ideas can be repurposed to help researchers undertake projects with real impact.

LLM decoding is bottlenecked for large batches and long contexts by loading the key-value (KV) cache from high-bandwidth memory, which inflates per-token latency, while the sequential nature of decodi...

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When we run algorithms on hardware, we're bounded by three things: how fast it can do math (OPs/second), the bandwidth available for moving data around (bytes/second), and the total memory available t...

Multi-Head Latent Attention (MLA) vs. Group Query Attention (GQA): Transformer inference optimization in DeepSeek V3 with lower KV cache and higher FLOPs/s.

Simulation-based inference (SBI) is the preferred framework for estimating parameters of intractable models in science and engineering. A significant challenge in this context is the large computation...

How to migrate to bsky without a boring feed.

Championing Open-source Development in Machine Learning.

I have met a lot of developers in my life. Late…

We’re introducing Ironwood, our seventh-generation Tensor Processing Unit (TPU) designed to power the age of generative AI inference.

How WaveSpeedAI and DataCrunch achieved an up to 6x faster image inference by optimizing FLUX-dev's latency and efficiency: NVIDIA B200 vs. H100 benchmark.

- Added brand colors 2025 palette - Updated all plots to use the new color scheme - Regenerated all plot images with the new colors - Set consistent style across all plots 🤖 Generated with [Claude...

Long-context large language models (LLMs) have achieved remarkable advancements, driven by techniques like Rotary Position Embedding (RoPE) (Su et al., 2023) and its extensions (Chen et al., 2023; Liu...