nvidia-smi
PulseAugur coverage of nvidia-smi — every cluster mentioning nvidia-smi across labs, papers, and developer communities, ranked by signal.
1 day(s) with sentiment data
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LLM efficiency, scaling, and deployment strategies detailed across multiple sources
A recent paper benchmarks the energy efficiency of locally deployed large language models (LLMs) on consumer hardware, finding that factors beyond parameter count, such as model architecture and quantization, significan…
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Guide Explains VRAM Needs for Local LLM Deployment
Running large language models locally requires careful VRAM management, as model size and quantization significantly impact memory usage. While there's no exact formula, VRAM needs can be estimated by considering the mo…
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LingBot-Map tutorial shows GPU-aware 3D reconstruction
A tutorial demonstrates the use of LingBot-Map for GPU-aware 3D reconstruction and point cloud export. The process involves configuring input sources, reconstruction settings, and output formats, then automatically tuni…
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NVIDIA AI Infrastructure Certification Program Launched with Training Resources
A training program and associated resources are available for the NVIDIA-Certified Associate: AI Infrastructure and Operations (NCA-AIIO) certification. The program includes recorded sessions, an exam guide, and practic…
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Deploying 1-Bit Bonsai-27B Model with PrismML llama.cpp and OpenAI-Compatible Server
This tutorial details the deployment of the 1-bit Bonsai-27B language model using a specialized fork of llama.cpp that includes CUDA kernels for its unique quantization format. The process involves setting up the enviro…
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DGX Spark GPU overheating solved by clock-locking with nvidia-smi
A developer has found a workaround for overheating issues with the DGX Spark GPU when running large language models like Ollama and Qwen2.5. The GPU, specifically the GB10, lacks user-accessible power and fan controls, …
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KV cache memory problem plagues LLM serving, vLLM's PagedAttention offers solution
The KV cache is a critical component in LLM inference, storing past computations to avoid recomputing them for each new token. However, its memory footprint can become a significant bottleneck, especially in production …
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Local LLM Hardware Guide: VRAM, Quantization, and Performance
Running large language models (LLMs) locally, particularly those with 70 billion parameters, presents significant hardware challenges, primarily concerning VRAM capacity. While marketing often suggests minimal requireme…
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User doubles LLM inference speed by fixing PCIe slot bottleneck
A user building a multi-GPU setup for local LLM inference discovered a significant performance bottleneck caused by a misconfigured PCIe slot. One of the four RTX 3090 GPUs was incorrectly placed in a slot that only sup…
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Utilyze offers open-source tool for deeper GPU performance insights beyond load
Utilyze is a new open-source tool designed to provide deeper insights into GPU performance beyond simple load percentages. It directly accesses GPU performance counters to measure the actual utilization and efficiency o…