The Together.ai platform offers cutting-edge tools and infrastructure for developers working on AI models. It focuses on making AI development more accessible, scalable, and cost-efficient. This blog post dives into key components of Together.ai’s offerings: Inference, Fine-Tuning, GPU Clusters, and Custom Models.
Together Inference: Powering Real-Time AI Applications
Inference is the process where AI models make predictions or generate outputs based on input data. Together’s Inference Engine is designed to deliver high-speed, cost-effective AI solutions for businesses and developers. With its FlashAttention-3 technology and Flash-Decoding, the platform dramatically improves inference performance, ensuring faster processing times for even the largest models. These features help developers manage complex models such as Llama-2 or Falcon-40B, reducing latency in real-time applications.
Another standout feature of Together’s inference engine is speculative decoding, which combines multiple decoding strategies to speed up the generation of large language models without sacrificing accuracy. This engine is optimized for memory efficiency, making it ideal for inference tasks, which are typically memory-bound rather than compute-bound.
Benefits for Developers:
- Blazing fast processing times for large models.
- Cost-efficiency with an 11x lower cost compared to other providers.
- Optimized kernels to improve memory management during inference.
Together Fine-Tuning: Tailoring AI Models for Specific Tasks
Fine-tuning is essential when developers want to adapt a pre-trained model to a specific task. Together.ai allows users to fine-tune leading open-source models, such as Llama and RedPajama, with just a few commands. Fine-tuning on Together is simple but powerful. You upload a dataset and configure key hyperparameters, such as epochs and learning rate, to optimize your model for your specific domain.
Together’s fine-tuning process supports a variety of models, including those optimized for dialogue, coding, and specific domains like healthcare or finance. You also have control over data privacy, ensuring that fine-tuned models remain your intellectual property.
Key Features:
- Rapid fine-tuning for LLMs (Large Language Models) with as few as two commands.
- Customization of batch size and epochs for optimal model quality.
- Integration with tools like Weights & Biases for monitoring model training.
Together GPU Clusters: High-Performance Infrastructure for AI Training
When it comes to training and fine-tuning large models, access to powerful GPUs is critical. Together.ai offers GPU clusters powered by the latest NVIDIA H100 and H200 GPUs. These clusters are designed for high-speed distributed training, allowing you to scale your AI efforts easily. With Infiniband interconnects and SXM form factor GPUs, these clusters provide ultra-fast communication between GPUs, making them ideal for high-performance computing (HPC) tasks and AI model training.
Flexible commitments allow users to rent clusters on-demand, from as short as one month to multi-year reservations, making it easy to scale resources based on project needs. Whether you need a few GPUs for fine-tuning or thousands for large-scale training, Together’s infrastructure is designed to handle your needs seamlessly.
Performance Highlights:
- NVIDIA H200 GPUs offer up to 40% faster inference performance compared to H100 GPUs, making them a top choice for LLM tasks like Llama-2.
- Pre-configured clusters optimized for distributed training and quantization techniques that reduce computational load.
Together Custom Models: Build Your Own AI from Scratch
For developers looking to create unique models, Together Custom Models offers full flexibility. You can start with data preparation, selecting datasets and model architecture, and move through to training and deploying your custom AI. The platform includes advanced tools like DoReMi, which optimize data slices for model training, and RedPajama-v2 quality signals that ensure the highest accuracy.
One of the best aspects of custom models on Together.ai is that once trained, the model is yours to keep, and you can deploy it in any environment. This freedom makes it an attractive option for developers who need specialized models for niche applications or want to retain full ownership of their AI.
Why Custom Models Matter:
- Full ownership of the model, with no restrictions on deployment.
- Data-driven optimizations during training ensure that your model performs at its best.
- Ability to build highly specialized AI models tailored to unique business needs.
Conclusion: Together.ai Simplifies AI Development
Together.ai stands out as a versatile platform for both experienced developers and newcomers to AI. With cutting-edge tools for inference, fine-tuning, GPU clusters, and custom models, it offers a one-stop solution for creating, training, and deploying AI models. Whether you’re handling complex LLMs, working on specialized AI tasks, or simply looking for a more affordable infrastructure for AI development, Together.ai provides everything you need to succeed.
Have you worked with Together.ai’s tools? Let me know in the comments or reach out if you have questions on getting started!
