NVIDIA Nemotron-3-Nano-30B LLM Benchmarks Vulkan and RPC
The article discusses benchmarking results for NVIDIA's Nemotron-3-Nano-30B large language model, focusing on Vulkan and RPC performance across different hardware configurations.
Why it matters
Benchmarking the performance of large language models on different hardware and configurations is crucial for understanding their real-world capabilities and limitations, which can inform deployment decisions and future model development.
Key Points
- 1Benchmarking the Nemotron-3-Nano-30B LLM on various systems, including AMD Ryzen 6800H CPU, Nvidia GTX 1080Ti, and Nvidia P102-100 GPUs
- 2Comparing performance of the model with different quantization settings (Q4_K, IQ4_XS, Q4_1) and backend configurations (Vulkan, RPC)
- 3Analyzing the impact of hardware and quantization on inference speed for different test cases (pp512, tg128)
Details
The article presents detailed benchmarking results for NVIDIA's Nemotron-3-Nano-30B large language model, a 31.58 billion parameter Mamba2-Transformer Hybrid Mixture of Experts (MoE) model. The author tests the model's performance on various hardware configurations, including AMD Ryzen 6800H CPU with Radeon 680M iGPU, Nvidia GTX 1080Ti, and Nvidia P102-100 GPUs. The model is too large to fit on a single GPU, so the author uses dual Nvidia GPUs and the RPC backend to avoid CPU offloading. The benchmarks compare the model's inference speed (tokens per second) for different quantization settings (Q4_K, IQ4_XS, Q4_1) and test cases (pp512, tg128). The results show that the hardware and quantization settings can have a significant impact on the model's performance, with the iGPU 680M performing best with the Q4_1 quantization for the pp512 test, and the IQ4_XS quantization for the tg128 test.
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