Topic
Speculative Decoding
9 episodes
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Beam is now available — Reflection
Vince and Ava talk through Reflection's Beam becoming available as an open-weight release, focusing on what changes now that the model, serving stack, and customization tooling are all shipping together. They stay skeptical about vendor-reported benchmarks, but land on the real story being the adoption path: a model you can actually download, serve, and adapt across a surprisingly broad ecosystem.
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NVIDIA Nemotron 3.5 Super VL Powers Specialized Agents with Multimodal Reasoning | NVIDIA Technical Blog
NVIDIA drops Nemotron 3.5 Super VL — a 120B-parameter open-weight multimodal MoE model built for specialized enterprise agents. Jessica and Cathy dig into the real argument: post-training on your own data beats the frontier on your specific task, and the efficiency story (40x inference cost reduction, 10x latency) is where the product actually lives.
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GitHub Nehanth/pooled: Peer to peer LLM inference in the browser: pool your devices to run big open models, for chat and coding agents.
Pooled is a browser-native peer-to-peer LLM inference system that splits open models (Qwen 3.6 35B MoE, Qwen 3.8 27B) across multiple devices' GPUs via WebRTC, with no server, no installation, and a built-in coding agent that can write and fix apps. Cody opens skeptical of the latency trade-offs and speculative decoding reliance; Justy counters that the zero-install, browser-only UX and multi-device pooling solve a real friction point for teams wanting local models without infrastructure. They dig into whether the speed hit (40–46 tok/s plain on the MoE vs. 85.5 on native llama.cpp, but 65–84 with speculative) is acceptable for the use case, and whether the coding agent is genuinely useful or a demo artifact.
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NVIDIA Brings Simplified Local AI Support To NVIDIA GPUs Carrying 24+ GB VRAM While vLLM & & llama.cpp Optimizations Boost Compute By Up To 1
NVIDIA ships kernel optimizations for local AI inference on RTX and DGX platforms, delivering up to 1.9x performance gains through vLLM and llama.cpp, paired with one-click agent setup (Perplexity Portable Computer, Hermes Agent, OpenClaw) for GPUs with 24+ GB VRAM. The move collapses the friction between local-first and cloud-capable workflows.
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The efficient frontier of LLM inference
Baseten's 'Efficient Frontier of LLM Inference' argues that inference engineering involves two fundamentally different types of techniques: those that let you trade off between latency and throughput along an existing efficiency curve, and those that push the entire curve outward, creating universal gains. The article maps concrete techniques (batch sizing, parallelism strategies, quantization, kernel optimization, speculative decoding, prefill/decode disaggregation) onto this framework and shows that the frontier is jagged and empirically discovered, not smooth.
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Overview: Token Efficiency
We finally sit down and define something we've been throwing around since episode one: token efficiency. What it actually means, how it works on both the input and output side, and why it's quietly one of the most load-bearing ideas in how real AI systems get built and paid for.
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Kimi K3 Is Here: Efficient Day 0 Support on vLLM
Vince and Ava unpack Moonshot AI's Kimi K3, a 2.8‑trillion‑parameter multimodal MoE, and its day‑zero support in vLLM. They walk through the model’s hybrid attention, the engineering tricks that make a 1 M‑token context feasible, the practical deployment recipe, and how it stacks up against other frontier models.
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Overview: Decoding Strategy
We finally slow down on decoding strategy, the rule that turns a model's next-token odds into the actual words you see. We use one hallway-and-doors picture to make greedy decoding, sampling, top-k, top-p, beam search, and newer decoding work feel less like magic knobs.
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Exploring Next Overview: Speculative Decoding
We finally slow down and unpack speculative decoding from the ground up: the draft model, the verify step, and why it can make generation faster without changing the output. We keep it concrete, because that trick sounds like cheating until the mechanism actually clicks.