Benchmark results for the various models as well as Gemma 3 comparison:
MACHINE LEARNING
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Edge AI Accelerates Government Decision-Making Operations
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Accelerating Decision-Making: The Edge AI in Government Field Operations! by – Kiersten Schiliro, @FBI @Cisco #BigData #Analytics #AI #MachineLearning #DataScience #IoT #IIoT #Python #RStats #TensorFlow #JavaScript #ReactJS #CloudComputing #Serverless #DataScientist #Linux
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Best Books on Data Science and Neural Networks
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Best Books on #DataScience and Neural Networks. #BigData #Analytics #IoT #IIoT #PyTorch #Python #RStats #TensorFlow #Java #JavaScript #ReactJS #GoLang #CloudComputing #Serverless #DataScientist #Linux #Books #Programming #Coding #100DaysofCode https://
geni.us/Books-DSci-NNs -
Introduction to Neural Networks: AI and Machine Learning Fundamentals
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Intro to Neural Networks! #BigData #Analytics #DataScience #AI #MachineLearning #IoT #IIoT #Python #RStats #TensorFlow #Java #JavaScript #ReactJS #GoLang #CloudComputing #Serverless #DataScientist #Linux #Programming #Coding #100DaysofCode https://
geni.us/Intro-Neural-N
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Illustrated Guide to LSTMs and GRUs for Deep Learning
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Illustrated LSTMs and GRUs. #BigData #Analytics #DataScience #AI #MachineLearning #IoT #IIoT #PyTorch #Python #RStats #TensorFlow #Java #JavaScript #ReactJS #CloudComputing #Serverless #DataScientist #Linux #Programming #Coding #100DaysofCode https://
geni.us/Illustrated-Gu
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Novel Protocol Reconstructs Quantum States with 96-Level Learning
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Novel Protocol Reconstructs Quantum States with 96-Level Learning! #BigData #Analytics #AI #MachineLearning #DataScience #IoT #IIoT #Python #RStats #TensorFlow #JavaScript #ReactJS #CloudComputing #Serverless #DataScientist #Linux #Programming #Coding #100DaysofCode
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NVIDIA Releases Quantized Gemma 4 31B Model on Hugging Face
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NVIDIA just released a quantized Gemma 4 31B on Hugging Face NVFP4 compression delivers 4x smaller weights with frontier-level accuracy. Runs on consumer GPUs with a 256K context window.
→ View original post on X — @huggingface, 2026-04-02 17:17 UTC
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llama.cpp achieves 300 tokens/second on Mac Studio M2 Ultra
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Let me demonstrate the true power of llama.cpp:
— Georgi Gerganov (@ggerganov) 2 avril 2026
– Running on Mac Studio M2 Ultra (3 years old)
– Gemma 4 26B A4B Q8_0 (full quality)
– Built-in WebUI (ships with llama.cpp)
– MCP support out of the box (web-search, HF, github, etc.)
– Prompt speculative decoding
The result:… pic.twitter.com/B3EnpbWJdeLet me demonstrate the true power of llama.cpp: – Running on Mac Studio M2 Ultra (3 years old) – Gemma 4 26B A4B Q8_0 (full quality) – Built-in WebUI (ships with llama.cpp) – MCP support out of the box (web-search, HF, github, etc.) – Prompt speculative decoding The result: 300t/s (realtime video)
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MLPerf Power Selected for IEEE MICRO Top Picks 2025
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Super excited to share that MLPerf Power (HPCA 2025) was selected for IEEE MICRO Top Picks 2025, 1 of the 12 most impactful computer architecture & systems papers of the year! Power consumption is the defining constraint for modern ML systems. Microsoft, Google, Amazon, Meta, and OpenAI have all announced plans for gigawatt-scale datacenters (for context, 5 GW = 5 nuclear reactors = Miami's power footprint). On the other end of the spectrum, we're anticipating billions of AI-enabled devices at the edge. We created MLPerf Power to be the industry-standard to measure, understand, and compare energy use across all deployment scales. We're excited to see that it's already impacting individual companies' strategies and has been incorporated into the IEEE semiconductor roadmap. We @MLCommons also collect and open source over 1,800 reproducible measurements from 60 diverse systems. These reveal several important insights that shed light on the nonlinear scaling of energy efficiency in modern systems and can enable many new data-driven optimization approaches. Just as @MLPerf aligned industry towards shared performance goals, we are hopeful that MLPerf Power will do the same for power and energy efficiency!
→ View original post on X — @askalphaxiv, 2026-04-02 17:00 UTC
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Emotion Vectors Found in Sonnet 4.5 Neural Networks
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We had the model (Sonnet 4.5) read stories where characters experienced emotions. By looking at which neurons activated, we identified emotion vectors: patterns of neural activity for concepts like “happy” or “calm.” These vectors clustered in ways that mirror human psychology.