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📈 Looking to run local GraphRAG or other graph analytics use cases? You can prepare your local text files for graph use cases at your desk, with NVIDIA DGX Spark. 📹 Full demo video: https://youtu.be/8iA_f_UpzHc In this demo, a Llama-3.1-70B model served by Ollama runs locally in DGX Spark’s 128GB of unified memory. After uploading local biomedical research papers into the LLM, the model extracts nodes and relationships that are embedded into a knowledge graph. By processing entire documents at
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📈 Looking to run local GraphRAG or other graph analytics use cases? You can prepare your local text files for graph use cases at your desk, with NVIDIA DGX Spark. 📹 Full demo video: https://youtu.be/8iA_f_UpzHc In this demo, a Llama-3.1-70B model served by Ollama runs locally in DGX Spark’s 128GB of unified memory. After uploading local biomedical research papers into the LLM, the model extracts nodes and relationships that are embedded into a knowledge graph. By processing entire documents at
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🎊 As of today, high performance large graph analytics in NetworkX is possible with zero code changes. The NVIDIA cuGraph backend brings in-memory GPU acceleration to over 60 NetworkX algorithms, with more on the way, resulting in up to 50-500x speedups on large graphs. 📝 Tech blog “NetworkX Introduces Zero Code Change Acceleration Using NVIDIA cuGraph” ➡️ https://nvda.ws/3Ca4Sy8 📝 cuGraph documentation: https://nvda.ws/4hhQIva 📗 Try the example Colab notebook: https://nvda.ws/4dYmN8n 📹 YouT
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🎊 As of today, high performance large graph analytics in NetworkX is possible with zero code changes. The NVIDIA cuGraph backend brings in-memory GPU acceleration to over 60 NetworkX algorithms, with more on the way, resulting in up to 50-500x speedups on large graphs. 📝 Tech blog “NetworkX Introduces Zero Code Change Acceleration Using NVIDIA cuGraph” ➡️ https://nvda.ws/3Ca4Sy8 📝 cuGraph documentation: https://nvda.ws/4hhQIva 📗 Try the example Colab notebook: https://nvda.ws/4dYmN8n 📹 YouT
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Two teams, equal priority. Team A runs small jobs continuously. Team B needs to run one large job. Every time GPUs free up, Team A's jobs fit first. Team B waits indefinitely. The problem: There is no memory of who just ran versus who has been waiting. NVIDIA Run:ai v2.24 introduces time-based fairshare to solve this. The scheduler can now track historical usage. Teams that consumed more recently get lower priority for over-quota allocation. Teams that have been waiting get boosted. Result: prop
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