Kyle Leaders’ Post

Generating KV cache during #inference requires a lot of compute and memory resources, so efficient use is key to improving model response, accelerating inference, and increasing system throughput. TensorRT-LLM provides advanced reuse features to further optimize TTFT response times for peak performance. Start using TensorRT-LLM KV cache reuse with the documentation on GitHub ➡ https://lnkd.in/gHeHRcyr Technical blog: ➡ https://lnkd.in/gP6WcFtN

Dive into how KV cache early reuse, fine-grained blocks, and efficient eviction algorithms can supercharge TTFT speeds. Efficient KV cache use is key to improving #LLM model response, speeding up #inference, and maximizing throughput. With NVIDIA TensorRT-LLM's advanced KV cache management features, developers can take inference performance to the next level. ➡️ https://nvda.ws/3YJzpe4

🌟WITH llama.cpp [通过 llama.cpp] Significantly boost the performance of your hashtag#AI workloads on GPUs by using llama.cpp on RTX AI PCs. 通过在 RTX AI PC 上使用 llama.cpp 显著提升 GPU 上标签#AI 工作负载的性能。 ➡️ https://nvda.ws/406X6zp 🦙 🌟With llama.cpp, you gain access to a C++ implementation designed for LLM inferencing, packaged in a lightweight installation.     通过 llama.cpp，您可以取用专为 LLM 推理设计的 C++ 执行，并包装在轻量级安装中。   🔎 Explore and begin utilizing llama.cpp through the RTX AI Toolkit. 通过 RTX AI 工具包探索并开始使用 llama.cpp。🛠️  

Introducing NVIDIA Modulus version 24.01, featuring updates to distributed utilities and samples for physics-informed DeepONet and GNNs.

Learn how the right parallelism technique increases #Llama 3.1 405B performance by 1.5x in throughput-sensitive scenarios on an NVIDIA HGX H200 system with NVLink and NVSwitch, and enables a 1.2x speedup in the MLPerf Inference v4.1 Llama 2 70B benchmark.

Learn how the right parallelism technique increases #Llama 3.1 405B performance by 1.5x in throughput-sensitive scenarios on an NVIDIA HGX H200 system with NVLink and NVSwitch, and enables a 1.2x speedup in the MLPerf Inference v4.1 Llama 2 70B benchmark.

Learn how the right parallelism technique increases #Llama 3.1 405B performance by 1.5x in throughput-sensitive scenarios on an NVIDIA HGX H200 system with NVLink and NVSwitch, and enables a 1.2x speedup in the MLPerf Inference v4.1 Llama 2 70B benchmark.

Learn how the right parallelism technique increases #Llama 3.1 405B performance by 1.5x in throughput-sensitive scenarios on an NVIDIA HGX H200 system with NVLink and NVSwitch, and enables a 1.2x speedup in the MLPerf Inference v4.1 Llama 2 70B benchmark.

 With the Akhet Server VarioFlex 5U with dual GPU and the high performance computing platform Akhet VarioScaler xI with multiple dual GPUs, Pyramid Computer GmbH is presenting two new systems at Embedded World that are optimized for machine learning applications. In addition to both systems, a camera bar is shown that includes two 48MP cameras and an ARM Cortex A73 with integrated NPU. The machine vision solution recognizes objects based on a trained model. https://lnkd.in/eBnaCeVc #machinevision #imageprocessing #edgecomputing #embeddedsystems #machinelearning #ai #deeplearning

Learn how the right parallelism technique increases #Llama 3.1 405B performance by 1.5x in throughput-sensitive scenarios on an NVIDIA HGX H200 system with NVLink and NVSwitch, and enables a 1.2x speedup in the MLPerf Inference v4.1 Llama 2 70B benchmark.