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When running LLMs at scale, the real limitation is GPU memory rather than compute, mainly because each request requires a KV cache to store token-level data. In traditional setups, a large fixed memory block is reserved per request based on the maximum sequence length, which leads to significant unused space and limits concurrency. Paged Attention […]
Paged Attention in Large Language Models LLMs Read More »
Researchers from FAIR at Meta, Cornell University, and Carnegie Mellon University have demonstrated that large language models (LLMs) can learn to reason using a remarkably small number of trained parameters. The research team introduces TinyLoRA, a parameterization that can scale down to a single trainable parameter under extreme sharing settings. Using this method on a
World Models (WMs) are a central framework for developing agents that reason and plan in a compact latent space. However, training these models directly from pixel data often leads to ‘representation collapse,’ where the model produces redundant embeddings to trivially satisfy prediction objectives. Current approaches attempt to prevent this by relying on complex heuristics: they
With so much happening in AI and machine learning today, figuring out where to start can feel overwhelming. Different learners prefer different approaches! Some want visuals, others prefer coding. Some prefer short form, others lean toward long-form learning. While many simply want a clear path into ML. This article is here to fix that. Instead
Top 10 YouTube Channels to Learn Machine Learning Read More »
In this tutorial, we explore the capabilities of the pymatgen library for computational materials science using Python. We begin by constructing crystal structures such as silicon, sodium chloride, and a LiFePO₄-like material, and then investigate their lattice properties, densities, and compositions. Also, we analyze symmetry using space-group detection, examine atomic coordination environments, and apply oxidation-state
Deploying a new machine learning model to production is one of the most critical stages of the ML lifecycle. Even if a model performs well on validation and test datasets, directly replacing the existing production model can be risky. Offline evaluation rarely captures the full complexity of real-world environments—data distributions may shift, user behavior can
NVIDIA has announced the release of Nemotron-Cascade 2, an open-weight 30B Mixture-of-Experts (MoE) model with 3B activated parameters. The model focuses on maximizing ‘intelligence density,’ delivering advanced reasoning capabilities at a fraction of the parameter scale used by frontier models. Nemotron-Cascade 2 is the second open-weight LLM to achieve Gold Medal-level performance in the 2025
In this tutorial, we explore how to solve differential equations and build neural differential equation models using the Diffrax library. We begin by setting up a clean computational environment and installing the required scientific computing libraries such as JAX, Diffrax, Equinox, and Optax. We then demonstrate how to solve ordinary differential equations using adaptive solvers
The scaling of inference-time compute has become a primary driver for Large Language Model (LLM) performance, shifting architectural focus toward inference efficiency alongside model quality. While Transformer-based architectures remain the standard, their quadratic computational complexity and linear memory requirements create significant deployment bottlenecks. A team of researchers from Carnegie Mellon University (CMU), Princeton University, Together
Large language models (LLMs) can generate credible but inaccurate responses, so researchers have developed uncertainty quantification methods to check the reliability of predictions. One popular method involves submitting the same prompt multiple times to see if the model generates the same answer.But this method measures self-confidence, and even the most impressive LLM might be confidently
A better method for identifying overconfident large language models Read More »
