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32:20ENWho 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.
1:05ENNVIDIA PAIR Routes Local AI Requests Across ComputersMatthew Berman · Forward FutureMatthew Berman demonstrates NVIDIA's local-network router for distributing AI inference work among compatible machines.
0:48ENMatthew Berman on Earning From Idle Mac Compute With DarkbloomMatthew Berman · Forward FutureA proposed network of Apple Silicon Macs could serve AI inference, but the earnings shown are projections that depend on hardware, availability and demand.
9:15ENSix 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.
2:22ENPioneer Agent and Automated Fine-Tuning of Small ModelsMatthew Berman · Forward FutureIn a Fastino-sponsored video, Matthew Berman describes a system that automates parts of model adaptation and discusses its reported benchmark results.
9:47ENGemma 4: Smaller Open-Weight Models for Local AIMatthew Berman · Forward FutureMatthew Berman reviews Google’s model sizes, multimodal features and reported benchmarks, with an eye toward local agents.
1:09ENThe Benefits and Tradeoffs of Running AI Models LocallyMatthew Berman · Forward FutureMatthew Berman weighs the privacy, control and costs of local models against technical complexity and performance tradeoffs.
1:02ENMatthew Berman’s Plans for a GB300 AI Workstation at HomeMatthew Berman · Forward FutureAfter NVIDIA visits with a Dell Pro Max GB300 system, Berman describes his plans for local models, fine-tuning and future AI agents.
0:45ENA DGX Spark Giveaway Connected to NVIDIA GTCMatthew Berman · Forward FutureMatthew Berman announces a 2026 giveaway for a DGX Spark and highlights GTC sessions on robotics and AI.
14:44ENKimi K2.5: Visual Coding, Agent Swarms, Benchmarks and Local DeploymentMatthew Berman · Forward FutureMatthew Berman examines Moonshot AI’s multimodal open-weight model, its parallel-agent demonstrations and the practical costs of running it.
21:12ENClawdbot as a Personal AI Assistant: Workflows, Costs and RisksMatthew Berman · Forward FutureMatthew Berman demonstrates a local agent connected to messaging, cloud services and models, and reports what worked, failed and cost more than expected.
18:09ENTraining a GPT-OSS 2048 Strategy With Verifiable RewardsMatthew Berman · Forward FutureMatthew Berman walks through a local Unsloth notebook, reward checks and one 2048 training run on an RTX 5090.
9:10ENGuiding AI Image Generation with Blender and ComfyUIMatthew Berman · Forward FutureMatthew Berman walks through NVIDIA’s RTX blueprint for turning a simple Blender scene and a text prompt into a generated image, from installation to editing the result.
18:23ENLlama 4 Reactions: Benchmarks, Long Context and Early TestsMatthew Berman · Forward FutureMatthew Berman reviews early industry reactions to Meta's Llama 4 release, from benchmark and price comparisons to long-context claims and local inference.
1:14ENNVIDIA NIM Microservices Bring Packaged AI Models to RTX PCsMatthew Berman · Forward FutureIn an NVIDIA-sponsored short, Matthew Berman introduces local AI microservices, compatible applications and the planned PDF-to-podcast workflow.
12:30ENQwQ-32B: A Smaller Reasoning Model and Its TradeoffsMatthew Berman · Forward FutureMatthew Berman examines Qwen’s reinforcement-learning recipe, tests a coding task, and compares benchmark scores and hardware requirements with DeepSeek-R1.
6:50ENDeepScaleR: A Small Reasoning Model Tested on Competition MathMatthew Berman · Forward FutureMatthew Berman reviews a 1.5B model’s reported benchmark results and runs a quantized version locally on one AIME problem.
7:41ENCES 2025 and Running DeepSeek R1 Distills on an RTX 5090Matthew Berman · Forward FutureMatthew Berman tours AI demonstrations at CES and runs two quantized DeepSeek R1 distilled models locally on an NVIDIA GPU.
5:27ENWays 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.
0:55ENA Quick RTX 5090 Local AI TestMatthew Berman · Forward FutureMatthew Berman runs an 8B DeepSeek R1 distillation locally and reports the speed of one generation.
8:52ENIndustry Reactions to DeepSeek R1’s 2025 ReleaseMatthew Berman · Forward FutureMatthew Berman reviews early reactions to DeepSeek R1, from open-weight reasoning models and local hardware to benchmark results and API pricing.
1:03ENAnythingLLM’s Community Hub and Local AI Agents on NVIDIA RTXMatthew Berman · Forward FutureIn a sponsored short, Matthew Berman introduces local agent workflows, shared prompts and integrations, with important limits on privacy and performance claims.
1:00ENConnect Obsidian to a Local AI Model with LM StudioMatthew Berman · Forward FutureMatthew Berman outlines a way to connect an Obsidian note-taking plugin to a model running on your own computer.
0:28ENComparing 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.
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