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2 diapositives32:20EN
Matthew 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
Matthew 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
5 diapositives9:47EN
Matthew Berman· 3 avr. 2026Gemma 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
4 diapositives18:09EN
Matthew Berman· 15 déc. 2025Training 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
3 diapositives5:27EN
Matthew 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
Diapositives0:55EN
Matthew Berman· 23 janv. 2025A 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
5 diapositives8:52EN
Matthew Berman· 21 janv. 2025Industry 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· 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
9 diapositives19:03EN
Matthew 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
Matthew 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
5 diapositives13:18EN
Matthew 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
6 diapositives17:03EN
Matthew Berman· 8 juin 2024Qwen2 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
4 diapositives9:53EN
Matthew 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
7 diapositives21:47EN
Matthew Berman· 24 avr. 2024Phi-3 Mini: Microsoft’s Small Model, Phone Deployment and an Uncertain Local TestMatthew Berman · Forward FutureMatthew Berman reviews Microsoft’s 2024 technical report and runs informal prompts, with an unresolved model label in the demonstration.Local inferenceModèles et entraînementBenchmarks+1
5 diapositives9:25EN
Matthew Berman· 31 janv. 2024Matthew Berman Tests Meta’s Code Llama 70B on a Snake GameMatthew Berman · Forward FutureA 2024 look at Meta’s coding-model release, a SQL-focused fine-tune and a hands-on test that did not work on the first attempt.Open-weight modelsSoftware engineeringBenchmarks+2
2 diapositives0:42EN
Matthew Berman· 31 janv. 2024LLaVA 1.6 Adds Higher-Resolution Image UnderstandingMatthew Berman · Forward FutureMatthew Berman reviews the January 2024 release of LLaVA 1.6 and its local availability through Ollama.Multimodal AIModèles et entraînementLocal inference+1
8 diapositives13:19EN
Matthew Berman· 18 janv. 2024Running a Local Language Model as an Offline BackupMatthew Berman · Forward FutureMatthew Berman demonstrates a 2024 local-model setup, from selecting a quantized model and storing it on an external drive to planning backup power.Local inferenceFoundation ModelsInfrastructure et déploiement+1
2 diapositives5:53EN
Matthew 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
7 diapositives4:36EN
Matthew Berman· 20 juin 2023Testing WizardCoder 15B as a Local Coding AssistantMatthew Berman · Forward FutureMatthew Berman runs the 2023 WizardCoder model locally, testing small Python exercises and a longer Snake game.Modèles et entraînementLocal inferenceSoftware engineering+2
2 diapositives10:40EN
Matthew 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