Using the toolkit for this specific use case provides multiple benefits: Config-driven workflows The toolkit helps shift the project from a rigid script to a flexible research platform. Instead of hard-coding the interactions between agents, you define the system’s logic—including personas, tools, and constraints—entirely within a YAML configuration. This modularity makes it trivial to swap models for different tasks. For example, you can assign a high-reasoning model to handle hypothesis generation while using a faster, more cost-effective model for the code agent without modifying the underl
Automating and Optimizing Financial Signal Discovery with Multi-Agent Systems | NVIDIA Technical Blog…The following kernel code isolates the exponential instructions to measure the raw cycle count without interference from global memory latency or other arithmetic operations. This test harness launches a grid of threads…
…Crucially, the low-latency inference techniques and the open source code presented in the following tutorial are fully compatible with both architectures. This ensures that the same optimized kernels that deliver high…
…agents, coding co-pilots, and “deep research” assistants. Recent advances in algorithmic and model efficiency have reduced the cost of training and inference , as demonstrated by the DeepSeek R1 model family. With…
…Automatically converts Hugging Face models into TensorRT LLM graphs without manual rewrites Single source of truth : Keeps the original PyTorch model as the canonical definition Inference optimization : Applies sharding, quantization, KV cache…
…Custom kernels fused with nvmath-python nvmath-python integrates with Python compilers such as numba-cuda , enabling high-performance custom Python code to be compiled just in time (JIT) and used alongside…
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