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1:05EN
Matthew Berman· 2026年9月18日NVIDIA 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.Local inferenceインフラとデプロイAI agents
スライド0:48EN
Matthew Berman· 2026年8月25日Matthew 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.Distributed training & inferenceInferenceLocal inference+1
スライド 4 枚9:15EN
Matthew Berman· 2026年8月17日Six 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
スライド2:22EN
Matthew Berman· 2026年5月29日Pioneer 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.ファインチューニング評価(Evals)Local inference+2
スライド 5 枚9:47EN
Matthew Berman· 2026年4月3日Gemma 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.Open-weight modelsLocal inferenceFoundation Models+2
スライド1:09EN
Matthew Berman· 2026年3月18日The 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.Local inferenceOpen-weight modelsAI cost & performance
スライド1:02EN
Matthew Berman· 2026年3月16日Matthew 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.Local inferenceインフラとデプロイファインチューニング+1
0:45EN
Matthew Berman· 2026年3月13日A 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.Local inferenceAI Adoption
スライド 6 枚14:44EN
Matthew Berman· 2026年1月27日Kimi 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.Foundation ModelsMultimodal AIAgent orchestration+2
スライド 6 枚9:10EN
Matthew Berman· 2025年5月15日Guiding 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.Generative media画像と動画Local inference+1
スライド 2 枚1:14EN
Matthew Berman· 2025年3月27日NVIDIA 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.インフラとデプロイLocal inferenceAI agents
スライド 4 枚12:30EN
Matthew Berman· 2025年3月7日QwQ-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.Reasoning & test-time computeReinforcement LearningOpen Source+2
スライド 3 枚6:50EN
Matthew Berman· 2025年2月12日DeepScaleR: 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.Reinforcement LearningBenchmarksLocal inference+2
スライド 5 枚7:41EN
Matthew Berman· 2025年1月30日CES 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.Local inferenceOpen-weight modelsSemiconductors+2
スライド 3 枚5:27EN
Matthew Berman· 2025年1月29日Ways 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
スライド0:55EN
Matthew Berman· 2025年1月23日A Quick RTX 5090 Local AI TestMatthew Berman · Forward FutureMatthew Berman runs an 8B DeepSeek R1 distillation locally and reports the speed of one generation.Local inferenceSemiconductorsOpen-weight models
スライド 5 枚8:52EN
Matthew Berman· 2025年1月21日Industry 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.Open-weight modelsReasoning & test-time computeBenchmarks+2
スライド1:03EN
Matthew Berman· 2024年11月29日AnythingLLM’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.Local inferenceAI agentsAgent skills+1
スライド 4 枚1:00EN
Matthew Berman· 2024年11月7日Connect 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.Local inferenceProductivityHuman-AI Collaboration
0:28EN
Matthew Berman· 2024年11月6日Comparing 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 inferenceコーディングエージェントOpen-weight models
スライド 4 枚12:50EN
Matthew Berman· 2024年11月2日Comparing Three Local Coding Models on Games and Programming ChallengesMatthew Berman · Forward FutureMatthew Berman runs DeepSeek-Coder-V2 Lite, Yi-Coder 9B, and Qwen2.5-Coder 7B on a sponsored workstation. Their results vary by task and include manual corrections.Open-weight models評価(Evals)Software engineering+2
スライド 2 枚9:15EN
Matthew Berman· 2024年9月25日Llama 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
スライド1:00EN
Matthew Berman· 2024年9月5日An ASUS ProArt Laptop with RTX 4070 for AI WorkloadsMatthew Berman · Forward FutureMatthew Berman describes a laptop sent by NVIDIA, a local Llama demonstration and RTX software features.Local inferenceSemiconductorsComputer Vision
スライド0:58EN
Matthew Berman· 2024年8月22日NVIDIA ACE Brings AI Conversations to Game CharactersMatthew Berman · Forward FutureMatthew Berman explains how NVIDIA ACE combines speech, language models and facial animation, with Mecha BREAK as an early showcase.Generative mediaMultimodal AI音声 AI+1
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