Key Takeaways:
I. Together AI's strategic advantage lies in its combined focus on open-source AI models and optimized NVIDIA GPU infrastructure, particularly the forthcoming Blackwell architecture, enabling it to potentially offer superior performance-per-watt and cost-effectiveness compared to hyperscalers.
II. The economic viability of Together AI's cloud hinges on achieving superior performance-per-watt, managing data center costs (which can be 50% higher in Europe), and navigating potential supply chain constraints for high-bandwidth memory (HBM), DRAM, and NAND flash, with projected demand increases of 60-65%, 40-45%, and 30-35% respectively by 2026.
III. While hyperscalers possess scale and resource advantages, Together AI's open-source commitment, deep NVIDIA integration, and focus on enterprise-grade services (including ISO 27001 and SOC 2 compliance) position it to capture a share of the rapidly expanding AI cloud market, particularly from users prioritizing flexibility and avoiding vendor lock-in.
Together AI's recent $305 million Series B funding, achieving a $3.3 billion valuation, signals a significant shift in the AI cloud landscape. This isn't merely another large funding round; it's a strategic maneuver placing a substantial bet on the convergence of open-source AI and NVIDIA's advanced GPU technology, specifically the Blackwell architecture. This valuation surpasses many AI startups and approaches that of established players like Dataiku ($3.7 billion) and approaches Hugging Face ($4.5 billion), indicating strong investor confidence in Together AI's disruptive potential. The funding round occurs amidst a backdrop of massive investment in generative AI, with over $25 billion invested in 2023 alone, and projections for continued growth. This article will dissect Together AI's strategy, moving beyond surface-level reporting to analyze the technical underpinnings, economic viability, and long-term strategic implications of this ambitious venture. We'll examine whether Together AI can truly challenge the entrenched hyperscalers – AWS, Google Cloud, and Microsoft Azure – and, if so, what that means for the future of AI development and deployment, particularly given the projected 25%-35% annual growth in AI workloads through 2027 and the increasing demand for trillion-parameter model training.
Decoding the AI Acceleration Cloud: GPU Utilization, Bottlenecks, and Optimization Strategies
Together AI's core infrastructure is built upon NVIDIA's A100 and H100 GPUs, the current industry standards for AI training and inference. The A100, with its 6912 CUDA cores and 40GB/80GB of HBM2e memory, delivers a theoretical peak performance of 312 teraFLOPS (FP16) for matrix operations, crucial for deep learning. The H100, based on the Hopper architecture, further enhances this with up to 3.95 terabytes per second of memory bandwidth and a dedicated Transformer Engine optimized for large language models (LLMs). Together AI leverages a distributed network of these GPUs, meticulously configured to maximize utilization and minimize latency. This strategic choice isn't just about adopting the latest technology; it's about providing the computational foundation necessary to handle the increasingly complex demands of modern AI workloads, including the training and deployment of trillion-parameter models.
Beyond raw GPU power, Together AI's architecture focuses on mitigating potential bottlenecks. High-speed interconnects like NVIDIA's NVLink and InfiniBand are crucial. NVLink, in the GB200 configuration, provides up to 900 GB/s of chip-to-chip interconnect bandwidth, significantly reducing inter-GPU communication latency compared to standard Ethernet connections, which typically offer 10-100 GB/s. This low-latency communication is critical for distributed training of large models, where frequent data exchange between GPUs is essential. InfiniBand, with speeds up to 400 Gb/s per port, further enhances data throughput between compute nodes. The storage system utilizes high-performance NVMe SSDs in a distributed, fault-tolerant configuration, optimized for both low latency and high throughput, essential for handling the massive datasets required by AI models. This careful orchestration of compute, networking, and storage is paramount for achieving optimal performance.
Together AI's software stack is meticulously optimized to extract maximum performance from the underlying hardware. They employ techniques like FlashAttention-3 kernels, which significantly reduce the memory footprint and computational cost of the attention mechanism, a core component of LLMs. Quantization, reducing the precision of model parameters (e.g., from FP32 to FP16 or INT8), further accelerates inference. Together AI claims these optimizations result in up to 3x faster inference speeds on models like Llama 2 and Mistral, compared to standard implementations. For example, internal benchmarks show a 2.5x improvement in tokens/second during inference on DeepSeek-R1 compared to AWS SageMaker using comparable p4d.24xlarge instances (which also utilize A100 GPUs). This performance advantage stems from Together AI's deep integration with NVIDIA's CUDA platform and their focus on optimizing for open-source models, allowing for more fine-grained control over the entire software stack.
NVIDIA's CUDA platform provides a comprehensive ecosystem of tools, libraries, and frameworks, creating a significant barrier to entry for competing hardware vendors. With over 4 million developers worldwide using CUDA, the platform benefits from a vast network effect, driving continuous innovation and optimization. This ecosystem advantage extends to open-source AI, with NVIDIA actively supporting frameworks like PyTorch and TensorFlow. Together AI leverages this ecosystem, attracting developers and enterprises who benefit from the readily available tools and expertise. This strategic alignment with NVIDIA and the open-source community fosters a collaborative environment, accelerating the development and deployment of AI applications. The tight integration with CUDA allows Together AI to fine-tune performance for specific workloads and benefit from ongoing advancements in the NVIDIA software stack, including new compilers, debuggers, and profiling tools.
Performance per Watt and Economic Viability: Can Together AI Outcompete the Hyperscalers?
The economic viability of an AI cloud hinges on performance per watt. While NVIDIA GPUs offer exceptional performance, they are also energy-intensive. Together AI's challenge is to minimize operational costs and environmental impact. They are actively implementing liquid cooling solutions, which are significantly more efficient than traditional air cooling, potentially reducing cooling energy consumption by up to 40%. While precise performance-per-watt figures for Together AI's infrastructure are not publicly available, the theoretical efficiency of NVIDIA GPUs can be compared to competitors. For instance, AMD's MI300X competes with NVIDIA's offerings, but overall system efficiency depends on factors beyond the GPU itself, including the software stack and cooling infrastructure. Geographic location also plays a crucial role; European data centers face operating costs that are typically 50% higher than those in the United States, primarily due to higher energy prices and stricter environmental regulations. This necessitates strategic data center placement to optimize for energy costs and potentially leverage renewable energy sources.
The environmental impact of AI is increasingly scrutinized, with energy consumption being a major concern. NVIDIA addresses this through features like dynamic power management, allowing GPUs to adjust power consumption based on workload, potentially reducing energy waste by 10-20% during periods of low utilization. The Blackwell architecture, powering the forthcoming GB200, incorporates further enhancements in energy efficiency, although specific details are still emerging. The key metric is cost per inference – the total cost (energy, hardware, operations) divided by the number of inferences. NVIDIA GPUs, with features like Tensor Cores and sparsity acceleration, aim to minimize this cost by maximizing throughput and minimizing latency. The projected increase in GPU-based computing power consumption of an additional 4.8 GW in 2025 (34% of the 14.3 GW total datacenter capacity forecasted by S&P Global Market Intelligence) underscores the urgency of energy-efficient AI solutions. Together AI's ability to offer a lower cost per inference while minimizing environmental impact will be a key differentiator.
Together AI's pricing strategy is crucial for competing with established hyperscalers like AWS SageMaker, Azure ML, and Google Vertex AI. These hyperscalers offer various pricing options, including pay-as-you-go, reserved instances, and spot instances. While specific pricing details for Together AI are not publicly available, their focus on open-source models suggests a potential strategy of offering competitive pricing, particularly for users leveraging pre-trained open-source models or bringing their own models. They might also explore subscription-based models for users with consistent workloads. The success of their pricing strategy depends on accurately forecasting demand, optimizing resource utilization, and offering transparent and flexible pricing options to cater to diverse customer needs, from startups to large enterprises. The ability to undercut hyperscaler pricing, even marginally, while maintaining profitability, will be a key factor in attracting customers.
The scale of modern data centers is escalating rapidly, with costs reflecting this trend. Current large data centers range from 50 MW to over 200 MW, with costs between $1 billion and $4 billion. Projections for the near future envision gigawatt-scale data centers, potentially reaching $10 billion to $25 billion within five years. This escalating cost presents a significant barrier to entry for new players. Together AI's ability to optimize GPU utilization through virtualization (NVIDIA vGPU) and containerization (Kubernetes), coupled with its focus on potentially less resource-intensive open-source models, is crucial for managing these costs. S&P Global Market Intelligence forecasts 14.3 GW of global data center capacity by 2025, with GPU-based computing consuming an additional 4.8 GW (34% of the total). By 2029, GPU-based workloads are projected to account for 68% of net new data center capacity. This underscores the massive capital expenditure required and the importance of efficient resource management for Together AI's long-term financial sustainability.
Strategic Foresight: Open-Source, Scalability, and the Future of Together AI
Together AI's commitment to open-source AI models is a defining strategic choice, aligning with the broader trend of democratizing AI and fostering collaboration. Open-source models like Meta's Llama, Mistral, and TII's Falcon are rapidly gaining traction, offering viable alternatives to proprietary solutions. This empowers smaller companies and developers, allowing them to participate in the AI ecosystem without vendor lock-in. The rise of open-source also fuels innovation, as researchers and developers can freely build upon existing models. Furthermore, the emergence of sovereign AI initiatives, where nations prioritize developing their own AI capabilities, often favors open-source models for greater control and transparency. For example, several European nations are investing heavily in sovereign AI, with projections suggesting a potential market size of $10 billion in revenue from governments' sovereign AI investments in 2024. Together AI is strategically positioned to benefit from this trend, attracting a community of developers and enterprises valuing openness and flexibility.
Scaling infrastructure to support over 450,000 developers and enterprises presents significant challenges. The demand for AI compute is growing exponentially, driven by increasingly complex models and broader AI adoption. Together AI must continuously expand its infrastructure, requiring substantial capital investment and technical expertise. Managing the costs of GPUs, power, cooling, and networking is a constant balancing act. They optimize resource utilization through virtualization (NVIDIA vGPU) and containerization (Kubernetes), maximizing efficiency. Ensuring low latency and high availability across geographically distributed data centers is crucial for a reliable service. Together AI's ability to effectively manage these complexities, including potential supply chain constraints and the escalating costs of data center infrastructure, will be a key determinant of its long-term success. The company's ability to adapt to the rapidly evolving technological landscape and maintain a competitive edge will be paramount.
The Verdict: A Calculated Risk with Disruptive Potential
Together AI's $3.3 billion valuation reflects a bold vision: to create a leading AI cloud platform leveraging open-source models and NVIDIA's cutting-edge GPU technology. Our analysis reveals both significant potential and formidable challenges. The technical feasibility is strong, with NVIDIA's A100, H100, and the upcoming Blackwell-powered GB200 providing unparalleled computational power. Together AI's software optimizations and open-source commitment further enhance their value proposition. However, the economic realities of operating a massive AI cloud, including energy costs, infrastructure expenses, intense competition, and potential supply chain bottlenecks, cannot be ignored. Ultimately, Together AI's success hinges on flawless execution, navigating a complex landscape of technological advancements, economic pressures, and evolving market demands. Their commitment to open-source, deep NVIDIA integration, and focus on enterprise-grade services position them as a potential disruptor. While hyperscalers hold advantages in scale and resources, Together AI's agility and focus on a specific niche – the open-source AI community – could allow them to capture significant market share. The $305 million investment is a calculated risk, but one with the potential to reshape the AI cloud, particularly given the global scramble for AI resources, evidenced by national governments ordering tens of thousands of GPUs. The evolving industry structure, with increased verticalization, further underscores the dynamic nature of this market and Together AI's opportunity to establish a strong foothold.
----------
Further Reads
I. MI300X vs H100 vs H200 Benchmark Part 1: Training – CUDA Moat Still Alive
