Models & Labs
NVIDIA Blackwell Tops Agentic AI Benchmark
NVIDIA Bloghigh confidence
Why it matters
- →Agentic AI benchmarks provide a new way to measure AI system performance on complex tasks.
- →NVIDIA Blackwell's efficiency could significantly reduce costs for large-scale AI deployments.
- →This sets a new standard for evaluating AI infrastructure in real-world applications.
NVIDIA's Blackwell Ultra NVL72 platform has achieved top performance in the inaugural AgentPerf benchmark for agentic AI, developed by Artificial Analysis. This benchmark evaluates AI systems on their ability to handle complex, multi-step tasks, a departure from traditional AI benchmarks focused on single LLM calls. The Blackwell platform demonstrated the ability to run 20 times more agents per megawatt compared to NVIDIA Hopper, highlighting its efficiency in large-scale AI deployments. This performance is crucial for enterprises looking to optimize their AI infrastructure investments.
Read originalMore from NVIDIA Blog
More in Models & Labs
Models & Labsmodels
llama.cpp b10955 release addresses heap corruption
The latest release of llama.cpp, b10955, tackles a critical issue of heap corruption by disabling the ggml-cpu precompiled header and fixing CACHE_LINE_SIZE ambiguity. This update ensures consistent CACHE_LINE_SIZE values across C++ kernels and C work-buffer sizing code, preventing heap-buffer-overflow and subsequent crashes. By restoring the natural include order and removing the std::hardware_destructive_interference_size branch, the update makes the value deterministic and include-order independent. This release is a technical fix that stabilizes the runtime environment for developers using llama.cpp.
llama.cpp Releases
Models & Labsmodels
llama.cpp b10956 release enhances SYCL backend
The latest llama.cpp release, b10956, introduces significant improvements to the SYCL backend, particularly for handling large k values in TOP_K operations. By implementing a radix select method, the update allows for efficient GPU-resident processing, avoiding previous limitations that forced operations to fall back to the CPU. This change enhances performance, especially in scenarios requiring large k values, such as qwen4exp's sparse-attention indexer. The update ensures that operations are more efficient and scalable, providing a notable boost in processing speed without regressing any measured shapes.