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41 件
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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
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 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 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
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
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 2 枚1:00EN
Matthew Berman· 2024年8月8日NVIDIA AI Resources for STEM StudentsMatthew Berman · Forward FutureIn a partnered short, Matthew Berman points students to learning, model development, robotics and local document tools.AI in EducationRoboticsLocal inference
スライド 4 枚15:50EN
Matthew Berman· 2024年7月23日Llama 3.1: Meta’s 405B Model and the Open-Weight EcosystemMatthew Berman · Forward FutureMatthew Berman reviews Meta’s July 2024 release, the smaller models, reported benchmarks, synthetic data and the proposed Llama Stack.Open-weight modelsモデルと学習Benchmarks+2
スライド 4 枚8:53EN
Matthew Berman· 2024年7月8日RouteLLM: Routing Queries Between Strong and Cheaper ModelsMatthew Berman · Forward FutureMatthew Berman explains a 2024 research framework that uses preference data to choose a model for each query.Model routing & gatewaysAI cost & performance評価(Evals)+2
スライド 5 枚13:18EN
Matthew Berman· 2024年6月20日Testing 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 inferenceInferenceモデルと学習+2
スライド 6 枚17:03EN
Matthew Berman· 2024年6月8日Qwen2 0.5B and 72B: A Hands-On ComparisonMatthew Berman · Forward FutureMatthew Berman tests coding, arithmetic and reasoning prompts on the smallest and largest Qwen2 models in a 2024 demonstration.Open-weight modelsBenchmarksLocal inference+2
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