--- title: Together AI slug: together-ai-8e50f url: /detay/together-ai-8e50f type: article language: English entity: primary: Together AI type: article disambiguation: Together AI: Open-source LLM cloud solutions. Fast training & inference. RedPajama dataset. categories: - name: Software And Artificial Intelligence slug: yazilim-ve-yapay-zeka url: /kategori/yazilim-ve-yapay-zeka tags: - RedPajama Dataset - AI Infrastructure - Large Language Models - Together AI - San Francisco author: Ömer Said Aydın created_at: 2025-05-18T12:31:02.370954+03:00 updated_at: 2025-05-20T14:55:34.925583+03:00 image: https://cdn.t3pedia.org/media/uploads/2025/05/18/TWUzYgW5t9vE3i0eGrnE2J7INwmJImRa.webp --- # Together AI ## KURE Information Cards ### KURE Information Card: Together AI ![rIUIXph25TUQ10mrh16gxFGfvblTTZAH (1).webp](https://cdn.t3pedia.org/media/uploads/2025/05/18/VhgbfpHgOSHOXTc4JQPU0DwzAAVwu5QI.webp) | Field | Value | |-------|-------| | Website(s) | https://www.together.ai/ | | Founded(Text) | 2022 | | Location | San Francisco / California / USA | | Founder(s) | Vipul Ved Prakash,Percy Liang,Chris Ré,Ce Zhang | ## Article Content **Together AI** is a San Francisco-based AI cloud provider that offers infrastructure and software solutions for training, [fine-tuning](/en/detay/fine-tuning-906c9/llms.txt), and deploying open-source large language models (LLMs) in production environments. Founded in 2022, the company is known for its research-driven engineering approach and contributions to the open-source AI ecosystem, particularly through its RedPajama initiative—a large-scale open dataset project. ### **Founding and Management** Together AI was founded in 2022 by Vipul Ved Prakash (CEO), Ce Zhang (CTO), Chris Ré, Percy Liang, and Tri Dao. The founding team includes AI researchers affiliated with Stanford University and Hazy Research. The company's headquarters is located in San Francisco, California. ### **Technological Infrastructure** Together AI’s infrastructure is designed to support high-performance training, inference, and fine-tuning of LLMs. Its core technology stack is structured around three main components: the Together Inference Engine, the Together Fine-Tuning Framework, and Together [GPU](/en/detay/processing-units-504b3/llms.txt) Clusters. ### **Together Inference Engine** This component provides a high-efficiency inference engine optimized for both open-source and proprietary models in production use. Key technical features include: - **Transformer-optimized kernels**: Custom FP8 (float 8-bit) kernels deliver up to 75% faster inference compared to standard frameworks like PyTorch. - **QTIP (Quantization with Integrity Preservation)**: Enables low-precision computation while maintaining model accuracy. - **Speculative Decoding**: Enhances inference performance using draft models trained on the RedPajama dataset. - **Model Variants**: Each model is available in three formats—“Lite” (lowest cost), “Turbo” (balanced), and “Reference” (full accuracy). - **API Support**: Serverless, OpenAI-compatible APIs and dedicated endpoints for GPU-specific deployments. ### **Together Fine-Tuning Infrastructure** Together AI’s fine-tuning infrastructure allows organizations to retrain models with their own data. Capabilities include: - **LoRA (Low-Rank Adaptation)** for efficient customization. - **Full Fine-Tuning** of all model parameters. - **DPO (Direct Preference Optimization)** and **Continued Fine-Tuning** for preference-based and iterative model updates. - **Support for long contexts** up to 32K tokens. - **JSONL input and CLI support** for automation and integration into development workflows. ### **Together GPU Clusters** These high-performance clusters are tailored for model training and inference. Key hardware and network components include: - **GPU Options**: NVIDIA A100, H100, H200, B200, and GB200 (Grace Blackwell architecture), with up to 384GB HBM3e memory. - **Networking**: - **NVLink** for direct GPU communication - **InfiniBand (3200 Gbps)** for low-latency distributed processing - **Software Stack**: - Together Kernel Collection (CUDA-based) - Slurm and Kubernetes for workload management - Training up to 24% faster and inference up to 75% faster than PyTorch - **Reliability**: 99.9% SLA with redundant infrastructure and expert technical support ### **RedPajama Dataset and Models** Together AI developed **RedPajama**, a 30-trillion-token open dataset (RedPajama-Data-v2), which ranks among the largest publicly available LLM datasets. RedPajama models built on this dataset are used by over 500 open-source AI projects and are intended to support reproducible research and open-access AI development. ### **Research and Innovation** The company actively contributes to AI research through innovations such as: - **FlashAttention-3**: A low-latency attention mechanism - **Cocktail SGD**: A method that reduces network load by up to 117× during distributed training - **QTIP**: Techniques for quantized, high-fidelity inference - **Sub-quadratic architectures**: Including models like **Striped Hyena** and **Monarch Mixer** ### **Clients and Use Cases** Together AI supports a wide range of models, including text, code, image, audio, embeddings, rerankers, and multimodal systems. Organizations using its infrastructure include [Salesforce](/en/detay/salesforce-8cc79/llms.txt), The Washington Post, [Pika Labs](/en/detay/pika-5ef24/llms.txt), Arcee AI, Nexusflow, and Wordware. Application areas include: - Customer support automation - Video content generation - Cybersecurity modeling - AI-driven in-game characters - Text-to-speech solutions - Enterprise document analysis ### **Pricing** Together AI offers three pricing tiers: - **Build**: Pay-as-you-go access to fast, serverless inference. - **Scale**: Reserved GPUs, custom configurations, and Slack-based technical support. - **Enterprise**: VPC deployment, 99.9% SLA, geo-redundancy, and dedicated support teams. ### **Future Outlook** Together AI’s vision is to bring open-source AI technologies into enterprise production with fast, cost-effective, and controllable models. The company aims to lead in core algorithm research (e.g., [FlashAttention](/en/detay/together-ai-dadf3/llms.txt)) while making large-scale model deployment more accessible. It plans to continue advancing infrastructure efficiency and model reliability, and to expand the capabilities of its platform through innovations like self-optimizing training pipelines and customizable inference agents. ## Academic Sources and References 1. “About Us – Team.” Together AI. Accessed on 14 May 2025. https://www.together.ai/about#team“About Us – Values.” Together AI. Accessed on 14 May 2025. https://www.together.ai/about#values“AWS Marketplace Listing.” Amazon Web Services. Accessed on 14 May 2025. https://aws.amazon.com/marketplace/pp/prodview-nueoqauvmoggm“Fine-Tuning Services.” Together AI. Accessed on 14 May 2025. https://www.together.ai/fine-tuning“Forbes Company Profile: Together AI.” Forbes. Accessed on 14 May 2025. https://www.forbes.com/companies/together-ai/?list=ai50“Homepage.” Together AI. Accessed on 14 May 2025. https://www.together.ai/“LinkedIn Profile: Together AI.” LinkedIn. Accessed on 14 May 2025. https://www.linkedin.com/company/togethercomputer/“Pricing – Fine-Tuning.” Together AI. Accessed on 14 May 2025. https://www.together.ai/pricing#fine-tuning“Pricing – GPU Clusters.” Together AI. Accessed on 14 May 2025. https://www.together.ai/pricing#gpu-clusters“Products.” Together AI. Accessed on 14 May 2025. https://www.together.ai/products“Research.” Together AI. Accessed on 14 May 2025. https://www.together.ai/research“Reuters: Together AI Notches $3.3 Billion Valuation After Latest Fundraising.” Reuters. Accessed on 14 May 2025. https://www.reuters.com/technology/artificial-intelligence/together-ai-notches-33-billion-valuation-after-latest-fundraising-2025-02-20/