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スライド 2 枚32:20EN
Matthew Berman· 2026年10月23日Who 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
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
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 4 枚21:12EN
Matthew Berman· 2026年1月26日Clawdbot 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.Personal agentsエージェント工学Local inference+2
スライド 4 枚18:09EN
Matthew Berman· 2025年12月15日Training 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.Reinforcement LearningPost-trainingOpen-weight models+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 4 枚18:23EN
Matthew Berman· 2025年4月7日Llama 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.Open-weight modelsBenchmarksコンテキストエンジニアリング+2
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 9 枚19:03EN
Matthew Berman· 2024年10月30日AI 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 Adoptionコーディングエージェント+2
スライド 6 枚23:29EN
Matthew Berman· 2024年10月19日AI News in October 2024: Swarm, Blackwell, Video Models and Edge AIMatthew Berman · Forward FutureMatthew Berman reviews a wide range of announcements, including OpenAI’s experimental agent framework and new models from NVIDIA and Mistral.AI agentsSemiconductorsGenerative media+2
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