Meta has released Llama 3.2, a new series of vision-language models (VLMs) and text-only small language models (SLMs), optimized by NVIDIA for performance across various devices. The VLMs support both text and image inputs, with up to 128,000 text tokens and high-resolution image inputs, while the SLMs are designed for efficient text processing. NVIDIA’s optimizations ensure low latency and high throughput for these models, whether deployed in data centers, on local workstations, or on edge devices like NVIDIA Jetson.
Using advanced NVIDIA technologies like TensorRT and TensorRT-LLM libraries, Llama 3.2 models are optimized for faster and cost-efficient performance. A custom FP8 quantization method, leveraging NVIDIA’s Hopper architecture, boosts model throughput without sacrificing accuracy. These improvements make Llama 3.2 ideal for tasks like visual reasoning and text generation in real-time applications.
Enterprises can deploy Llama 3.2 efficiently through NVIDIA NIM microservices, enabling powerful AI applications from the cloud to the edge.
Grey Matterz Thoughts
Meta’s Llama 3.2 models, optimized by NVIDIA, deliver high performance and efficiency for text and image tasks across platforms. These advancements enable faster, cost-effective AI applications from the cloud to edge devices.
Source: https://shorturl.at/JGVzw
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