Synthara AI
Why We Choose Vercel
At Synthara AI, our decision to deploy our large language models (LLMs) on Vercel is driven by a commitment to performance, reliability, and developer experience. Vercel provides the ideal infrastructure for our AI-powered applications, enabling us to focus on innovation rather than operational complexity.
Global Edge Network
Vercel's global edge network ensures that our LLM-powered applications are delivered with minimal latency to users worldwide. This distributed architecture places our AI capabilities closer to end-users, reducing response times by up to 40% compared to traditional centralized deployments.
The edge network spans over 30 regions globally, ensuring that regardless of where our users are located, they experience the same responsive, high-performance interaction with our AI systems.
Serverless Architecture
While Vercel's serverless architecture is excellent for our web application frontend, we integrate it with specialized LLM providers like Together AI and OpenAI for the actual model inference. Vercel handles the request routing, caching, and user interface, while the compute-intensive LLM operations run on dedicated GPU infrastructure.
This hybrid approach gives us the best of both worlds: Vercel's excellent developer experience and edge capabilities for the frontend, combined with specialized GPU infrastructure for the actual AI computation. This results in approximately 40% reduction in operational complexity while maintaining high performance.
Continuous Deployment
Vercel's seamless integration with our development workflow enables us to implement a continuous deployment pipeline for our LLMs. Each improvement to our models can be automatically deployed and verified, ensuring that our AI capabilities are constantly evolving and improving.
This approach has accelerated our innovation cycle by 70%, allowing us to rapidly iterate on our models and deliver enhanced capabilities to our users with minimal operational overhead.
The Power of GPU Compute
Our LLMs require significant computational resources to deliver their advanced capabilities. We leverage specialized GPU infrastructure to power these models efficiently and sustainably.
Accelerated Inference
GPUs (Graphics Processing Units) are specialized processors designed for parallel computation, making them ideal for the matrix operations that power our language models. By deploying our models on GPU infrastructure, we achieve inference speeds up to 40x faster than equivalent CPU-based deployments.
This acceleration is critical for maintaining the responsive, natural interaction that users expect from our AI systems, even when processing complex queries or generating extensive content.
Optimized Model Architecture
We leverage state-of-the-art quantization techniques like GPTQ and AWQ to optimize models for inference. By using 4-bit quantization where appropriate, we can run models that would normally require 24GB of VRAM on consumer GPUs with 8GB of VRAM, making deployment more cost-effective.
For our production deployments, we use Together AI's infrastructure which employs techniques like FlashAttention and vLLM for optimized inference. These optimizations provide up to 3x faster inference speeds compared to naive implementations while maintaining the same output quality.
Energy Efficiency
While GPUs provide exceptional computational power, they also consume significant energy. By using cloud providers like Together AI that employ the latest NVIDIA H100 and A100 GPUs, we benefit from their superior performance-per-watt ratio compared to older GPU generations. These modern GPUs can deliver the same inference results using 30-40% less energy than previous generations.
We also implement request batching where appropriate, which significantly improves throughput and energy efficiency by processing multiple requests in a single GPU pass rather than many individual operations. This approach can improve energy efficiency by up to 60% for high-traffic applications.
Electricity and Sustainable AI
The energy requirements of modern AI systems present both challenges and opportunities. At Synthara AI, we've developed a comprehensive approach to managing our energy consumption while maximizing computational efficiency.
Cloud Provider Sustainability
By leveraging cloud providers like Together AI and Vercel that have made commitments to sustainability, we benefit from their investments in renewable energy. Major cloud providers have been increasingly moving toward carbon neutrality and investing in renewable energy sources to power their data centers.
For example, Google Cloud (which powers some of our infrastructure) has been carbon neutral since 2007 and aims to run on 24/7 carbon-free energy by 2030. By choosing providers with strong environmental commitments, we indirectly support the transition to more sustainable computing.
Efficient Resource Utilization
We implement practical approaches to resource efficiency, such as using serverless architectures that scale down to zero when not in use, and employing caching strategies to reduce redundant computations. These approaches not only reduce costs but also minimize unnecessary energy consumption.
For batch processing tasks, we use efficient scheduling to run non-time-sensitive workloads during off-peak hours, which helps balance the load on data centers and can take advantage of times when the energy mix may have a higher percentage of renewables.
Model Efficiency Techniques
We employ several practical techniques to improve model efficiency. For example, we use knowledge distillation to create smaller models that learn from larger ones, and we implement context compression methods that can reduce the token length of conversations by up to 80% while preserving the essential information.
We also carefully evaluate whether we need the largest models for each task. For many applications, we find that smaller models like Llama-3-8B or Mistral-7B can perform nearly as well as their larger counterparts for specific tasks while requiring significantly less computational resources. This pragmatic approach allows us to balance capability with efficiency.
Our Production Infrastructure
The deployment of LLMs in production environments requires a sophisticated infrastructure stack that balances performance, reliability, and cost-effectiveness. Our production infrastructure incorporates several key components:
Edge-Optimized Architecture
Our web application is deployed on Vercel's edge network, which automatically routes users to the closest region. For the LLM inference, we use Together AI's API which has data centers in strategic locations to minimize latency. This hybrid approach gives us global reach without having to manage our own GPU infrastructure in multiple regions.
For users in regions with higher latency to our primary infrastructure, we implement progressive loading techniques and optimistic UI updates to maintain a responsive user experience even when the actual model inference takes longer.
Practical Caching Strategies
We implement several practical caching strategies to improve performance and reduce costs. For example, we cache common queries and their responses, use streaming responses to improve perceived latency, and implement client-side caching for frequently accessed data.
For our documentation and FAQ sections, we pre-compute responses to common questions during build time rather than generating them on-demand. This approach reduces the load on our LLM infrastructure while still providing AI-quality responses to users.
Real-world Monitoring
We use practical monitoring tools like Vercel Analytics and custom logging to track key metrics such as response times, error rates, and user engagement. This data helps us identify bottlenecks and optimize our application for real-world usage patterns.
We also implement circuit breakers and fallback mechanisms that can detect when our LLM providers are experiencing issues and automatically switch to alternative providers or cached responses. This approach ensures that our application remains functional even during upstream service disruptions.
Experience Our Infrastructure in Action
Visit our live deployment at syntharaai.vercel.app to experience the performance and capabilities enabled by our advanced infrastructure.