#Quantization

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#Quantization Reel by @futuregenquantum (verified account) - Quantum mechanics is the branch of physics that explains the behavior of matter and energy at the smallest scales - atoms and subatomic particles. It
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@futuregenquantum
Quantum mechanics is the branch of physics that explains the behavior of matter and energy at the smallest scales — atoms and subatomic particles. It challenges classical physics by introducing concepts like wave-particle duality, uncertainty, and quantization of energy. Phenomena such as superposition and entanglement reveal that particles can exist in multiple states or locations until observed. Quantum mechanics forms the foundation for modern technologies like semiconductors, lasers, and quantum computers, revolutionizing our understanding of the universe’s fundamental nature. #QuantumMechanics #Physics #QuantumTheory #WaveParticleDuality #UncertaintyPrinciple #Superposition #Entanglement #QuantumPhysics #ModernScience #QuantumComputing
#Quantization Reel by @parasmadan.in (verified account) - LLM Quantization explained 👨‍💻 This is a method used to reduce the size of the LLM so that they can be used on local system as well. Follow @the.
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@parasmadan.in
LLM Quantization explained 👨‍💻 This is a method used to reduce the size of the LLM so that they can be used on local system as well. Follow @the.ai_kid for more on Tech #llm #generativeai #tech #reels
#Quantization Reel by @edhonour (verified account) - Quantization Aware Training (QAT) is a workaround to for the loss in precision when you run quantized models locally.
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@edhonour
Quantization Aware Training (QAT) is a workaround to for the loss in precision when you run quantized models locally.
#Quantization Reel by @automatewithakshay - Want to run a 70B parameter AI model locally? This video explains how quantization lets you shrink huge models and run them on your own device - even
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@automatewithakshay
Want to run a 70B parameter AI model locally? This video explains how quantization lets you shrink huge models and run them on your own device – even on a Mac! From understanding Q4_K_M to deploying LLaMA 3.3 70B, this guide breaks it down like a pro. Why wait on cloud servers when your machine’s ready to go? #AIOptimization #Quantization #LLaMA3 #RunAILocally #MachineLearningTips #LocalLLM #TechExplained #n8n #AIDevelopment #OpenSourceAI
#Quantization Reel by @genieincodebottle (verified account) - LLM Inference Speed vs Quality Across Different Quantization Levels Visual Elements in the Reel: 1. Moving dots = Token generation speed (more dots
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@genieincodebottle
LLM Inference Speed vs Quality Across Different Quantization Levels Visual Elements in the Reel: 1. Moving dots = Token generation speed (more dots moving faster means higher throughput) 2. Wave signal = Output quality ( Smoother wave means higher precision, noise means quality degradation) 3. Memory bar = VRAM/RAM consumption 4. Speed multiplier (right side) = Relative inference speed vs baseline (1x is baseline at FP32) Quantization Methods (top to bottom, slowest to fastest): 1. FP32 (32-bit) - Full precision, maximum quality, highest memory usage 2. FP16 (16-bit) - Half precision, nearly lossless, 2x memory savings 3. INT8 (8-bit) - Integer quantization, balanced trade-off INT4 (4-bit) - Aggressive quantization, significant speedup 4. GPTQ (4-bit optimized) - Calibration-based quantization preserving quality 5. GGUF (2-6 bit mixed) - CPU-friendly format used by llama.cpp 6. 1-bit (1.58-bit) - Extreme compression like BitNet Video shows how lower-bit quantization increases inference speed while reducing memory, but introduces progressively more noise in the output quality wave. #genai #generativeai #machinelearning
#Quantization Reel by @learningsound (verified account) - Episode 21 Let's look at quantization and resolution. ☺️ The last episode of the year. 😍
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@learningsound
Episode 21 Let’s look at quantization and resolution. ☺️ The last episode of the year. 😍
#Quantization Reel by @priyal.py - Quantization overview #datascience #machinelearning #womeninstem #learningtogether #progresseveryday #tech #consistency
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@priyal.py
Quantization overview #datascience #machinelearning #womeninstem #learningtogether #progresseveryday #tech #consistency
#Quantization Reel by @amarchenkova (verified account) - Einstein said 'God does not play dice'-the 2025 Nobel Prize in Physics went to the discovery that made quantum computing possible. The laureates: Joh
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@amarchenkova
Einstein said ‘God does not play dice’—the 2025 Nobel Prize in Physics went to the discovery that made quantum computing possible. The laureates: John Clarke (UC Berkeley), Michel Devoret (Yale/UCSB), John Martinis (UCSB) The Prize was for: “For the discovery of macroscopic quantum mechanical tunneling and energy quantization in an electric circuit” Their 1984-1985 experiments at UC Berkeley were inspired by Anthony Leggett’s theoretical predictions about macroscopic quantum behavior in superconducting systems. The circuit they built used a Josephson junction—two superconductors separated by a thin insulating barrier—cooled to millikelvin temperatures. - Superconducting qubits are one of the leading platforms for quantum computing - These qubits form the basis for quantum error correction experiments - The same principles enable ultra-sensitive quantum sensors (SQUIDs) and quantum communication devices - The race to build fault-tolerant quantum computers continues, scaling from hundreds of qubits today to millions needed for practical applications. More Sources: - Nobel Prize: https://www.nobelprize.org/prizes/physics/2025/press-release/ - Scientific background: https://www.nobelprize.org/prizes/physics/2025/advanced-information/ #NobelPrize #Physics #QuantumComputing #QuantumMechanics #Einstein #tech DeepTech #Science
#Quantization Reel by @harpercarrollai (verified account) - Building effective AI products comes down to 3 key pillars: latency (response speed), scalability (serving many users), and energy efficiency (minimiz
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@harpercarrollai
Building effective AI products comes down to 3 key pillars: latency (response speed), scalability (serving many users), and energy efficiency (minimizing power use per query). Hardware upgrades—such as high-speed networking and energy-optimized architectures—are critical for handling large-scale demands. On the software side, techniques like quantization (reducing calculation precision for efficiency) and distributed inference (splitting tasks across machines) enable smoother, faster, and greener AI deployments in production. The right balance of these factors leads to reliable, resource-conscious AI systems that keep innovating as user needs grow. @nvidiaai ‘s hardware solutions and software tools are leading examples of how these optimizations are being achieved in real-world AI infrastructures. . Let me know in the comments if you have any questions about this or anything else about AI. . . If you don’t know me, hey! I’m Harper - a machine learning engineer turned AI/ML educator, with about 10 years of experience engineering AI and machine learning (ML) at Stanford (I have Master’s and Bachelor’s degrees in Computer Science specializing in AI), Meta building ML systems, and then as Founding Engineer and then Head of AI/ML at a startup acquired by NVIDIA. I’m here to make AI clear & understandable to everyone. . Learn artificial intelligence | science research technology update . #AI #machinelearning #techeducation #nvidia #learnai #artificialintelligence #aiengineer #ad
#Quantization Reel by @themathcentral - Integrals are the mathematical formalization of finding the exact area under a curve by summing an infinite number of infinitesimally small rectangles
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@themathcentral
Integrals are the mathematical formalization of finding the exact area under a curve by summing an infinite number of infinitesimally small rectangles. This concept begins with discretization, where the area is approximated by dividing the interval into finite rectangles, as in Riemann sums. Each rectangle’s width represents a small segment of the interval, dx, while its height corresponds to the function’s value at that point, f(x). As the width of the rectangles approaches zero, the sum of their areas becomes a precise measure of the total area, which is the integral. #math #learning #integral #animation #reels

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