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Hosted by mcgrof · 🇺🇸 us · EN · 776 episodes
Established thought leaders with verified media credentials.
AI-generated podcast where hosts Hal Turing and Dr. Ada Shannon discuss the latest research papers and reports in machine learning, AI systems, and optimization. Featuring honest critical analysis, proper citations, and nerdy humor.
mcgrof hosts AI Post Transformers, a technology show with 776 episodes published.
This episode explores TRELLIS, a bounded-memory transformer architecture that replaces the usual ever-growing key-value cache with a fixed set of learned memory slots that are rewritten during inference. It explains why
This episode explores why batch-1 LLM decode for robots, edge copilots, and other single-session agents behaves very differently from high-throughput serving, and why next-token latency cannot be explained by memory band
This episode explores a post-training method for making mixture-of-experts language models cheaper at inference time without retraining them from scratch. It explains how the paper converts a fully trained static MoE int
This episode explores a paper proposing that language models could handle long-context reasoning by periodically pausing, replaying soon-to-be-evicted context offline, and consolidating it into fixed-size fast-weight mem
This episode explores SmolLM2, a 1.7 billion parameter language model from Hugging Face that tries to compete with stronger small models not by changing the transformer architecture, but by radically improving the traini
This episode explores Google’s Snap system, which moves major host-networking functions out of the kernel and into isolated userspace services while trying to keep the performance benefits usually associated with kernel
This episode explores the 2008 Dragonfly network topology paper and why its ideas suddenly matter again for large-scale AI systems in 2026. It explains how Dragonfly uses high-radix routers and router groups to keep most
This episode explores KVzap, a method for pruning transformer KV caches by learning a cheap surrogate for a much stronger oracle, with the goal of making cache eviction practical during both prompt prefilling and token-b
This episode explores KVzip, a query-agnostic method for compressing long-context KV caches so a model can reuse a shared document, codebase, or memory bank across many later questions without optimizing for just one que
This episode explores the DFX system, a four-FPGA appliance designed to accelerate transformer-based text generation by targeting a key weakness of GPUs: low-batch, token-by-token decode. It explains the difference betwe
This episode explores a paper on inference-time scaling for coding agents, asking whether extra test-time compute still helps when tasks are long, messy, and require multi-step tool use rather than a single code completi
This episode explores a systems paper that extends GPU memory through CXL-attached DRAM and SSDs, asking whether accelerators can reach beyond on-board HBM without the usual overhead of software-driven memory migration.
This episode explores Beluga, a systems paper that tackles one of the biggest practical bottlenecks in long-context LLM inference: how to store and retrieve massive KV caches when GPU memory is no longer enough. It expla
This episode explores Generative Recursive Reasoning, a paper that asks whether models can reason more effectively by repeatedly refining an internal latent state instead of externalizing long chains of thought as tokens
This episode explores a 2024 paper on the LPU, a custom processor designed specifically for large language model inference, with an emphasis on reducing the per-token delay that users notice in interactive systems. It ex
Episode title: After Titans: Behrouz on Nested Learning and Hope The followup to our Titans episode, by the same core team a year later. Behrouz, Razaviyayn, Zhong, and Mirrokni (Google Research) generalize the Titans be
This episode explores the Titans paper’s proposal to pair standard attention with a separate learned long-term memory that updates during inference, aiming to preserve distant information without paying full quadratic at
This episode explores how a line of recent systems papers culminates in NanoFlow, a serving approach that breaks LLM inference into very small “nano-batches” so different GPU-intensive operations can overlap instead of r
This episode explores the paper’s claim that decoding cost in large language models is driven less by raw parameter counts and more by hardware-level behavior during autoregressive generation, especially memory bandwidth
This episode explores MegaScale-Infer, a systems paper on serving large mixture-of-experts language models by separating the attention path from the expert feed-forward path and scheduling them independently. It explains
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AI Post Transformers is hosted by mcgrof. The show is categorised under technology and has published 776 episodes.
AI Post Transformers has published 776 episodes.
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