Nyun AI

Thank you KaroStartup for covering our story and featuring our CTO Arnav Chavan. Exciting times for us to be building something incredible out of India :)

Thank you Priya M. Nair for your belief in us and featuring us in your discussion :)

We're #hiring a new Software Engineer Intern in Karnataka. Apply today or share this post with your network.

🚀 Vue, Django, and SQLite Developer (Full-Time or Intern) We’re looking for a talented developer with experience in Vue, Django, and SQLite to join our team! 📝 What’s on Offer? Full-time role (6-month contract with potential for permanent conversion based on performance) OR a Full-Time Internship. Competitive compensation. 💼 If you’re ready to work on exciting projects and grow with us, we’d love to hear from you! Please send your resume and portfolio to connect@nyunai.com. Know someone who is a good match? Share this post with them! 🌟

🚀 We’re Hiring: Machine Learning Engineer at Nyun AI! We at Nyun AI are revolutionizing the deployment of foundational AI solutions on edge devices, and we’re looking for a passionate Machine Learning Engineer to join our growing team. 🔍 What We’re Looking For: • Experience: Prior work experience (excluding internships) in Deep Learning is required. • Preferred Qualifications: PhD in Deep Learning. • Skills: Strong coding skills and proficiency in Pytorch and/or TensorFlow is a must. 🌟 Why Join Nyun AI? • Work on cutting-edge projects, including edge AI solutions for industry leaders. • Contribute to the development of our Zero platform. • Be part of a dynamic team that’s shaping the future of AI on the Edge. • Solve real-world challenges with innovative technologies. 💼 Ready to make an impact? If you meet the requirements and are excited about this opportunity, send your resume to connect@nyunai.com or directly ping Arnav Chavan. Join us in our mission to democratize AI and create the next wave of innovation! #Hiring #MachineLearningEngineer #DeepLearning #PyTorch #TensorFlow #AIontheEdge #NyunAI

🚀 Announcing the Edge LLM Leaderboard – Now Live with Support from Hugging Face! We’re thrilled to introduce the Edge LLM Leaderboard – a platform to benchmark Compressed LLMs on real edge hardware, starting with the Raspberry Pi 5 (8GB) powered by the ARM Cortex A76 CPU and optimized using llama.cpp. 🔑 Key Highlights 🔹 Real-World Performance Metrics: We focus on critical metrics that matter for edge deployments: • Prefill Latency (Time to First Token) • Decode Latency (Generation Speed) • Model Size (Efficiency for limited storage) 🔹 130+ Models at Launch: We’ve benchmarked sub-8B models with ARM-optimized quantizations like: • Q8_0 • Q4_K_M • Q4_0_4_4 (ARM Neon Optimized) This provides a comprehensive, real-world comparison of throughput, latency, and memory utilization on accessible, low-cost devices. 🔮 What’s Next? 📈 Expanded Backend Support: Adding frameworks with ARM compatibility. 🖥️ Additional Edge Hardware: Exploring underutilized devices for LLM deployment. 💡 Your Feedback Shapes This Initiative We’re building this as a community-driven resource and would love your input: • What models or hardware would you like to see added? • Are there specific optimizations we should prioritize? 📩 Share your ideas or model requests at: edge-llm-evaluation@nyunai.com #EdgeAI #LLM #MachineLearning #Benchmarking #HuggingFace #ARM #EdgeComputing

🚀 Announcing the Edge LLM Leaderboard – Now Live with Support from Hugging Face! We’re thrilled to introduce the Edge LLM Leaderboard – a platform to benchmark Compressed LLMs on real edge hardware, starting with the Raspberry Pi 5 (8GB) powered by the ARM Cortex A76 CPU and optimized using llama.cpp. 🔑 Key Highlights 🔹 Real-World Performance Metrics: We focus on critical metrics that matter for edge deployments: • Prefill Latency (Time to First Token) • Decode Latency (Generation Speed) • Model Size (Efficiency for limited storage) 🔹 130+ Models at Launch: We’ve benchmarked sub-8B models with ARM-optimized quantizations like: • Q8_0 • Q4_K_M • Q4_0_4_4 (ARM Neon Optimized) This provides a comprehensive, real-world comparison of throughput, latency, and memory utilization on accessible, low-cost devices. 🔮 What’s Next? 📈 Expanded Backend Support: Adding frameworks with ARM compatibility. 🖥️ Additional Edge Hardware: Exploring underutilized devices for LLM deployment. 💡 Your Feedback Shapes This Initiative We’re building this as a community-driven resource and would love your input: • What models or hardware would you like to see added? • Are there specific optimizations we should prioritize? 📩 Share your ideas or model requests at: edge-llm-evaluation@nyunai.com #EdgeAI #LLM #MachineLearning #Benchmarking #HuggingFace #ARM #EdgeComputing

We're #hiring a new Deep Learning Engineer Intern in Karnataka. Apply today or share this post with your network.

🚀 Join Us as a Deep Learning Developer Intern! 🚀 We're excited to offer internship opportunities for our open-source project, #Nyuntam, on GitHub! Nyuntam is dedicated to compressing large models like LLMs to enable foundational AI on edge devices. See here: https://lnkd.in/gfSuhJGQ We're looking for passionate Deep Learning Developer Interns to contribute to Nyuntam by adding new features under the guidance of our expert team. Why Join? Roles: Full-time or part-time Perks: Co-author a paper for a top ML conference upon successful completion and integration of assigned tasks. Stipend: Generous compensation If you're ready to work on cutting-edge AI optimization and gain recognition in the community, this opportunity is for you! 📋 Apply here: https://lnkd.in/dZ4bn_eX Be part of the journey to bring AI to the edge! ✨ #AI #DeepLearning #Internships #OpenSource #AIOnEdge #MachineLearning

As Generative AI expands into edge computing—enabling real-time decision-making on smartphones, IoT devices, and more—inaccuracy has become a critical concern. According to McKinsey's 2024 report, many businesses adopting GenAI on the edge struggle with inaccurate outputs, especially in tasks such as summarization and content generation. These inaccuracies can have severe consequences in sectors like healthcare or customer service, where reliability is paramount.This challenge is further exacerbated by the hardware limitations of edge devices, which make it difficult to run large, complex models without performance trade-offs. Many organizations are working to mitigate this by fine-tuning models for specific tasks and investing in better governance and explainability to ensure trust in AI-generated results. At Nyun AI, we are focused on deploying highly efficient GenAI models on the edge while maintaining the integrity and performance of the models. Our edge-optimized solutions prioritize accuracy and speed, ensuring that businesses can harness the power of GenAI without compromising on quality. If you're exploring edge AI solutions, let's connect and discuss how we can drive impactful results together. Ping us at connect@nyunai.com. Link: https://lnkd.in/eADN79Cd