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29 件
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スライド0:22EN
Matthew Berman· 2026年10月18日DeepSeek's Memory Use: A Short Claim About KV CacheMatthew Berman · Forward FutureMatthew Berman links lower cache memory use to serving more tokens, but the excerpt does not state the comparison behind its figures.KV caching & prefix reuseInferenceインフラとデプロイ
0:50EN
Matthew Berman· 2026年9月2日Matthew Berman on Using Idle Computers for AI InferenceMatthew Berman · Forward FutureBerman points to Darkbloom as a way to put spare computing capacity to work on AI models, while raising the question of how far distributed inference can go.Distributed training & inferenceインフラとデプロイInference+2
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 9 枚13:09EN
Matthew Berman· 2026年8月14日AI News: Ultrafast Inference, Claude Watermarks and New ModelsMatthew Berman · Forward FutureMatthew Berman reviews an ultrafast coding preview, text watermarking, Grok Bot, open-weight models and Computer History.InferenceOpen-weight modelsコーディングエージェント+2
スライド 2 枚0:24EN
Matthew Berman· 2026年1月26日Matthew Berman Checks His Clawdbot Token CostsMatthew Berman · Forward FutureA short usage check reveals unexpectedly high model spending over two days.AI cost & performancePersonal agentsInference
スライド1:33EN
Matthew Berman· 2026年1月15日OpenAI’s Cerebras Deal and the Limits of a 100× Speed ClaimMatthew Berman, Andrew Feldman · Forward FutureMatthew Berman discusses OpenAI’s 2026 agreement for Cerebras inference capacity and a speed chart. The contract and comparison require more context than the video’s headline provides.OpenAIInferenceSemiconductors+1
スライド 2 枚1:38EN
Matthew Berman· 2025年12月29日NVIDIA’s Groq Licensing Deal and the Inference MarketMatthew Berman · Forward FutureMatthew Berman discusses why NVIDIA licensed Groq’s inference technology and hired members of its team.SemiconductorsInferenceAI cost & performance+1
スライド 2 枚1:02EN
Matthew Berman· 2025年10月31日Inside a Cerebras Data Center in Oklahoma CityMatthew Berman · Forward FutureMatthew Berman tours the facility and explains its wafer-scale chips, racks and cooling system.InferenceインフラとデプロイAI cost & performance
スライド 3 枚14:07EN
Matthew Berman· 2025年10月23日Inside Cerebras’s Oklahoma City AI Data CenterMatthew Berman, Andrew Feldman, Billy Wooten · Forward FutureMatthew Berman tours the facility with Cerebras CEO Andrew Feldman and Scale Data Centers COO Billy Wooten, exploring wafer-scale chips, liquid cooling and backup power. Their performance and expansion figures are presented as company claims from 2025.SemiconductorsインフラとデプロイInference+2
スライド 4 枚8:47EN
Matthew Berman· 2025年9月16日Why Identical LLM Prompts Can Produce Different Answers at Temperature ZeroMatthew Berman · Forward FutureMatthew Berman explains Thinking Machines Lab’s work on batch-invariant inference and what its reproducibility experiment actually showed.InferenceAI researchオブザーバビリティと信頼性+1
スライド 4 枚17:51EN
Matthew Berman· 2025年8月28日AI News in August 2025: Claude for Chrome, New Models and AI InfrastructureMatthew Berman · Forward FutureMatthew Berman reviews a browser-agent preview, model releases, reported Apple discussions and research on inference and weather.AI agentsOpen-weight modelsGenerative media+2
スライド 5 枚10:01EN
Matthew Berman· 2025年2月5日DeepSeek R1 Code Optimization and a $3 Visual Counting ExperimentMatthew Berman · Forward FutureMatthew Berman examines a human-guided coding improvement and a small reinforcement-learning experiment, then considers their implications for AI research.Reinforcement LearningGPU programming & kernelsAI research+2
スライド 2 枚30:29EN
Matthew Berman· 2025年1月3日How Researchers Think o1-Style Reasoning Models WorkMatthew Berman · Forward FutureMatthew Berman explains a research survey of policy initialization, rewards, search and reinforcement learning, and the open questions behind reasoning models.Reinforcement Learningモデルと学習AI research+1
スライド 5 枚9:38EN
Matthew Berman· 2024年10月15日Liquid Foundation Models: Company Benchmarks and a Hands-On TestMatthew Berman · Forward FutureMatthew Berman reviews Liquid AI’s 2024 model and memory claims, then tries the 40B preview on coding, logic, and instruction-following prompts.Foundation Models評価(Evals)Benchmarks+2
スライド0:54EN
Matthew Berman· 2024年9月16日OpenAI’s o1 Preview: A First Look at Longer ReasoningMatthew Berman · Forward FutureMatthew Berman introduces the o1-preview and o1-mini models and tries a Python game prompt.モデルと学習Inference
スライド0:44EN
Matthew Berman· 2024年8月29日NVIDIA Adds Multi-LoRA Serving to Its RTX AI ToolkitMatthew Berman · Forward FutureIn an NVIDIA-partnered video, Matthew Berman describes using multiple adapters with one base model and a vendor-reported throughput gain.ファインチューニングInferenceインフラとデプロイ
スライド 3 枚5:50EN
Matthew Berman· 2024年7月27日A Mixture-of-Agents Demo Using Groq and StreamlitMatthew Berman · Forward FutureMatthew Berman walks through a 2024 Groq-powered multi-agent project, from setup to inspecting its layered responses.Multi-agent systemsAgent orchestrationInference+1
スライド 6 枚11:20EN
Matthew Berman· 2024年7月1日Matthew Berman Adapts Mixture of Agents to Groq InferenceMatthew Berman · Forward FutureA 2024 tutorial changes an open-source multi-model ensemble to use Groq, then tests its interactive output and fixes a streaming error.Multi-agent systemsAgent orchestrationInference+1
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
27:37EN
Matthew Berman· 2024年6月17日Leo Pekelis on Extending Llama 3 to a Million-Token ContextMatthew Berman, Leo Pekelis · Forward FutureGradient’s chief scientist explains long-context training, coding use cases, inference costs and why retrieval benchmarks tell only part of the story.コンテキストエンジニアリングモデルと学習Inference+2
スライド 5 枚12:51EN
Matthew Berman· 2024年4月21日Llama 3 70B on Groq: Fast Inference and an Informal Prompt TestMatthew Berman · Forward FutureMatthew Berman tests coding, reasoning and repeated prompts on Groq’s April 2024 Llama 3 service, with mixed results and several scoring caveats.Open-weight modelsInference評価(Evals)+2
スライド 6 枚9:02EN
Matthew Berman· 2024年4月1日Matthew Berman Tries LLM Colosseum, a Street Fighter Model BenchmarkMatthew Berman · Forward FutureA 2024 tutorial shows language models choosing fighting-game moves, explains the game-state prompt and walks through local setup.評価(Evals)Multi-agent systemsExperimentation+1
スライド 4 枚14:11EN
Matthew Berman· 2024年3月23日LM Studio in 2024: Local Models, Parallel Inference and a Local APIMatthew Berman · Forward FutureMatthew Berman demonstrates model downloads, hardware settings, JSON output and serving multiple local models.Open-weight modelsInference
スライド 3 枚51:11EN
Matthew Berman· 2024年3月22日Inside Groq’s LPU: Compilers, Chip Networks and Fast InferenceMatthew Berman, Andrew Ling, Igor Arsovski · Forward FutureGroq engineers Andrew Ling and Igor Arsovski explain their hardware and software design in a sponsored 2024 interview.インフラとデプロイInferenceAI cost & performance
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