Today, NVIDIA revealed platform-wide optimisations aimed at speeding up Meta Llama 3, the most recent iteration of the large language model (LLM).
When paired with NVIDIA accelerated computing, the open approach empowers developers, researchers, and companies to responsibly innovate across a broad range of applications.
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Using a computer cluster with 24,576 NVIDIA H100 Tensor Core GPUs connected by an NVIDIA Quantum-2 InfiniBand network, meta engineers trained Llama 3. Meta fine-tuned its network, software, and model designs for its flagship LLM with assistance from NVIDIA.
In an effort to push the boundaries of generative AI even farther, Meta recently revealed its intentions to expand its infrastructure to 350,000 H100 GPUs.
Aims Meta for Llama 3
Meta’s goal with Llama 3 was to create the greatest open models that could compete with the finest proprietary models on the market right now. In order to make Llama 3 more beneficial overall, Meta sought to address developer comments. They are doing this while keeping up their leadership position in the responsible use and deployment of LLMs.
In order to give the community access to these models while they are still under development, they are adopting the open source philosophy of publishing frequently and early. The Llama 3 model collection begins with the text-based models that are being released today. In the near future, the meta objective is to extend the context, enable multilingual and multimodal Llama 3, and keep enhancing overall performance in key LLM functions like coding and reasoning.
Exemplar Architecture
For Llama 3, they went with a somewhat conventional decoder-only transformer architecture in keeping with the Meta design concept. They improved upon Llama 2 in a number of significant ways. With a vocabulary of 128K tokens, Llama 3’s tokenizer encodes language far more effectively, significantly enhancing model performance. In order to enhance the inference performance of Llama 3 models, grouped query attention (GQA) has been implemented for both the 8B and 70B sizes. They used a mask to make sure self-attention does not transcend document borders when training the models on sequences of 8,192 tokens.
Training Information
Curating a sizable, excellent training dataset is essential to developing the best language model. They made a significant investment in pretraining data, adhering to the principles of Meta design. More than 15 trillion tokens, all gathered from publically accessible sources, are used to pretrained Llama 3. The meta training dataset has four times more code and is seven times larger than the one used for Llama 2. More over 5 percent of the Llama 3 pretraining dataset is composed of high-quality non-English data covering more than 30 languages, in anticipation of future multilingual use cases. They do not, however, anticipate the same calibre of performance in these languages as they do in English.
They created a number of data-filtering procedures to guarantee that Llama 3 is trained on the best possible data. To anticipate data quality, these pipelines use text classifiers, NSFW filters, heuristic filters, and semantic deduplication techniques. They discovered that earlier iterations of Llama are remarkably adept at spotting high-quality data, so they trained the text-quality classifiers that underpin Llama 3 using data from Llama 2.
In-depth tests were also conducted to determine the optimal methods for combining data from various sources in the Meta final pretraining dataset. Through these tests, we were able to determine the right combination of data that will guarantee Llama 3’s performance in a variety of use scenarios, such as trivia, STEM, coding, historical knowledge, etc.
Next for Llama 3: What?
The first models they intend to produce for Llama 3 are the 8B and 70B variants. And there will be a great deal more.
The meta team is thrilled with how these models are trending, even though the largest models have over 400B parameters and are still in the training phase. They plan to release several models with more features in the upcoming months, such as multimodality, multilingual communication, extended context windows, and enhanced overall capabilities. When they have finished training Llama 3, they will also release an extensive research article.
They thought they could offer some pictures of how the Meta biggest LLM model is trending to give you an idea of where these models are at this point in their training. Please be aware that the models released today do not have these capabilities, and that the data is based on an early checkpoint of Llama 3 that is still undergoing training.
Utilising Llama 3 for Tasks
Versions of Llama 3, optimised for NVIDIA GPUs, are currently accessible for cloud, data centre, edge, and PC applications.
Developers can test it via a browser at ai.nvidia.com. It comes deployed as an NVIDIA NIM microservice that can be used anywhere and has a standard application programming interface.
Using NVIDIA NeMo, an open-source LLM framework that is a component of the safe and supported NVIDIA AI Enterprise platform, businesses may fine-tune Llama 3 based on their data. NVIDIA TensorRT-LLM can be used to optimise custom models for inference, and NVIDIA Triton Inference Server can be used to deploy them.
Bringing Llama 3 to Computers and Devices
Moreover, it utilizes NVIDIA Jetson Orin for edge computing and robotics applications, generating interactive agents similar to those seen in the Jetson AI Lab.
Furthermore, workstation and PC GPUs from NVIDIA and GeForce RTX accelerate Llama 3 inference. Developers can aim for over 100 million NVIDIA-accelerated systems globally using these systems.
Llama 3 Offers Optimal Performance
The best techniques for implementing a chatbot’s LLM balance low latency, fast reading speed, and economical GPU utilisation.
Tokens, or roughly the equivalent of words, must be delivered to an LLM by such a service at a rate of around double the user’s reading speed, or 10 tokens per second.
Using these measurements, an initial test using the version of Llama 3 with 70 billion parameters showed that a single NVIDIA H200 Tensor Core GPU generated roughly 3,000 tokens/second, adequate to serve about 300 simultaneous users.
Thus, by serving over 2,400 users concurrently, a single NVIDIA HGX server equipped with eight H200 GPUs may deliver 24,000 tokens/second and further optimise expenses.
With eight billion parameters, the Llama 3 version for edge devices produced up to 40 tokens/second on the Jetson AGX Orin and 15 tokens/second on the Jetson Orin Nano.
Progression of Community Models
As a frequent contributor to open-source software, NVIDIA is dedicated to enhancing community software that supports users in overcoming the most difficult obstacles. Additionally, open-source models encourage AI openness and enable widespread user sharing of research on AI resilience and safety.
