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2 diapositives32:20EN
MBMatthew Berman· 23 oct. 2026Who Is Local AI For? The Costs, Trade-offs and a Hybrid FutureMatthew Berman · Forward FutureMatthew Berman weighs data control and independence against hardware cost, setup complexity and inference speed.Local inferenceOpen-weight modelsAI cost & performance+1
4 diapositives9:15EN
MBMatthew Berman· 17 août 2026Six Open-Source Projects for Local AI, Agents, and 3D DesignMatthew Berman · Forward FutureMatthew Berman tours tools for running and training models, giving agents new skills, coordinating work, browsing, and generating 3D meshes.Open-source ecosystemsAgent skillsLocal inference+2
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Unsupervised Learning: With Jacob Effron· 31 juil. 2026PodcastThe Man Who Helped Stand Up Colossus in 120 Days Is Betting on an AI That Lives in Your HouseAnime : Jacob EffronxAI co-founder Igor Babuschkin left to start River AI, arguing coding agents have crossed a threshold, closed labs are getting squeezed, and the next prize is personal, local intelligence.AgentsAI startups & small teamsCompany Building+3
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Cognitive Revolution "How AI Changes Everything"· 4 juil. 2026Podcast19 Neurons Can Drive a Car. Liquid AI Wants Foundation Models on Every ChipAnime : Nathan LabenzRamin Hasani traces liquid neural networks from a worm's 300-cell brain to a 600-megabyte model that will talk from inside a Mercedes, and explains why architecture search now beats architecture avengers.Modèles et entraînementLocal inferenceScaling Laws+3
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Cognitive Revolution "How AI Changes Everything"· 6 déc. 2025PodcastPolish Is 1% of Frontier Training Data, So Poland Built Its Own ModelAnime : Nathan LabenzMarek Kozlowski of Poland's National Information Processing Institute explains how PLLuM uses language adaptation, organic instructions, and on-prem small models to keep a mid-size country in control of its AI.Entreprise et stratégieOpen-weight modelsData quality & curation+3
3 diapositives5:27EN
MBMatthew Berman· 29 janv. 2025Ways to Try DeepSeek R1: Hosted and Local Distilled ModelsMatthew Berman · Forward FutureMatthew Berman demonstrates DeepSeek chat, Groq’s hosted distill and a local model in LM Studio.Local inferenceOpen-weight modelsQuantization
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MBMatthew Berman· 6 nov. 2024Comparing Three Local Coding ModelsMatthew Berman · Forward FutureMatthew Berman introduces a sponsored comparison of DeepSeek Coder V2, Yi-Coder and Qwen Coder running on a workstation.Local inferenceAgents de codeOpen-weight models
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Cognitive Revolution "How AI Changes Everything"· 30 oct. 2024PodcastMMLU Stays at Chance Until Late Training. Loss Is Only an Alarm.Anime : Nathan LabenzZyphra’s training lead walks through Zamba 2’s hybrid architecture, the evals that actually matter, and the trade secrets small labs still have to rediscover.Modèles et entraînementLocal inferenceÉvaluations+3
9 diapositives19:03EN
MBMatthew Berman· 30 oct. 2024AI News in October 2024: Apple Intelligence, Coding Assistants, Search, and AgentsMatthew Berman · Forward FutureMatthew Berman reviews the first Apple Intelligence release, new coding tools, AI search disputes, local models, and reports of browser agents.AI agentsAI AdoptionAgents de code+2
2 diapositives9:15EN
MBMatthew Berman· 25 sept. 2024Llama 3.2 Adds Vision and Smaller On-Device ModelsMatthew Berman · Forward FutureIn a Meta-partnered 2024 video, Matthew Berman reviews the model family, its tooling and a brief hosted 1B demonstration.Open-weight modelsMultimodal AILocal inference+1
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Cognitive Revolution "How AI Changes Everything"· 11 juil. 2024PodcastScooping Food Into Boxes Is 70 Percent of Plant LaborAnime : Nathan LabenzChef Robotics CEO Rajat Bhageria on 20 million plated meals, a foundation model for food, and why the kitchen may leave the house.RoboticsData quality & curationLocal inference+3
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No Priors: AI, Machine Learning, Tech, & Startups· 27 juin 2024Podcast · 2 voixFive Independent Teams Found the Same 10-to-1 Mix That Beats TransformersAnime : Sarah Guo, Elad GilThe authors of S4 and Mamba argue that mostly state-space layers plus a dash of attention is the architecture that actually works—and they are shipping it first as real-time speech.Modèles et entraînementTransformers & attentionIA vocale+3
5 diapositives13:18EN
MBMatthew Berman· 20 juin 2024Testing Local AI on a Dual RTX 6000 WorkstationMatthew Berman · Forward FutureMatthew Berman tests language models, concurrent inference, and image generation on a Dell Precision tower supplied by Dell and NVIDIA.Local inferenceInferenceModèles et entraînement+2
4 diapositives9:53EN
MBMatthew Berman· 28 mai 2024Testing Microsoft Phi-3 Medium Locally: Mixed Coding and Reasoning ResultsMatthew Berman · Forward FutureMatthew Berman runs a quantized Phi-3 Medium on a MacBook and explores coding, arithmetic and reasoning prompts.Open-weight modelsLocal inferenceBenchmarks+1
4 diapositives25:00EN
MBMatthew Berman· 23 mai 2024Microsoft Build 2024: Copilot+ PCs, Phi-3 and AI DemosMatthew Berman, Satya Nadella, Sam Altman, Seth Juarez · Forward FutureMatthew Berman reviews selected Microsoft keynote announcements, product demonstrations and a conversation with Sam Altman.AI platform engineeringLocal inferenceMultimodal AI+2
29:09EN
No Priors: AI, Machine Learning, Tech, & Startups· 9 mai 2024Podcast · 2 voixThe Hyperscalers Are Spending $200 Billion — and the Next Bottleneck Is a Power PlantAnime : Sarah Guo, Elad GilSarah Guo and Elad Gil walk through music models, on-device LLMs, long context, and why AI capex now sits beside oil and broadband.Infrastructure et déploiementAI cost & performanceIngénierie du contexte+3
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No Priors: AI, Machine Learning, Tech, & Startups· 21 mars 2024Podcast · 2 voixSpeed Is the Thing You Can Buy: Baseten’s Bet on InferenceAnime : Sarah Guo, Elad GilTuhin Srivastava tells Sarah Guo and Elad Gil why a six-person chatbot company running a billion tokens a day should not be building its own serving stack.Infrastructure et déploiementInferenceAI cost & performance+3
2 diapositives5:53EN
MBMatthew Berman· 18 juil. 2023Petals: Distributing Large Language Model Inference Across DevicesMatthew Berman · Forward FutureIn 2023, Matthew Berman explains Petals as a way to use large models through a network of participating computers rather than loading every layer on one device.Distributed training & inferenceLocal inferenceInference+2
2 diapositives10:40EN
MBMatthew Berman· 7 juin 2023Testing Nous Hermes 13B Locally: Mixed Reasoning and Unsafe ResponsesMatthew Berman · Forward FutureMatthew Berman compares a 2023 quantized model on hosted and local hardware, with uneven results across coding, logic and safety.Open-weight modelsLocal inferenceBenchmarks+1
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MBMatthew Berman· 15 avr. 2023Andriy Mulyar on GPT4All, Data Quality and Open Local ModelsMatthew Berman, Andriy Mulyar · Forward FutureThe Nomic AI co-founder explains how GPT4All emerged, why training data matters, and the tradeoffs between local and centralized AI.Local inferenceOpen-weight modelsData quality & curation+2
1:18:48EN
Lex FridmanPodcastData Movement, Not Math: How to Make Deep Learning Fit on Robots and PhonesAnime : Lex FridmanMIT professor Vivienne Sze explains why moving data dominates the energy cost of neural networks and how co-designing hardware and algorithms can cut it by orders of magnitude.Infrastructure et déploiementInferenceAI cost & performance+3