Save up to 90% in AI costs (build and compute) by deploying to smaller and cheaper hardware with the Titan Takeoff Inference Server.
Save up to 90% in compute costs. Deploy LLMs to significantly smaller and cheaper hardware, thanks to the Titan Takeoff Inference Server’s cutting-edge inference optimization and quantization capabilities.
Select the GPU or CPU that is right for your project and budget. The Titan Takeoff Inference Server’s interoperability means it supports a range of accessible GPUs and CPUs, not just high-end NVIDIA A100s and H100s.
Maximize hardware utilization. The Titan Takeoff Inference Server’s LoRA adapters and batching server allow you to run dozens of models on a single GPU.
Make use of legacy hardware. The Titan Takeoff Inference Server supports all hardware types, meaning you can even use older, more easily available hardware for your Generative AI workloads.
Titan Takeoff uses the best-in-class model optimization techniques, these include:
1. Continuous batching
2. Multi-GPU serving
3. Multi-threaded Rust server
For more information, please visit our Technology page.
Quantization in AI refers to the process of reducing the precision of numerical representations within a neural network. This involves converting high-precision floating-point numbers into lower-precision integers, resulting in a more efficient model that requires less computational resources. In large language models, quantization plays a crucial role in optimizing inference, as it helps achieve a balance between model accuracy and computational efficiency. For a deeper dive on quantization, read here.
Inference optimization and quantization are techniques employed to enhance the efficiency of AI models during the inference phase, leading to significant cost savings - Titan Takeoff employs both techniques and customers have reported cost savings in the region of 90%.
Inference Optimization makes model inference faster, meaning less GPU hours are required to complete the same inference.
Quantization reduces the memory requirement of the Generative AI model, allowing for deployment to cheaper and more readily available GPUs.
LoRA is an enhanced method for finetuning, where the focus is not on adjusting all weights in the weight matrix of a large pre-trained language model. Instead, it fine-tunes two smaller matrices which collectively serve as an approximation of this larger matrix, forming what is known as the LoRA adapter. Once this adapter is fine-tuned, it can be integrated into the pre-trained model for the purpose of inference.
In the Titan Takeoff Inference Server customers are able to serve multiple models from a single inference server, by loading one base model and dozens of low resource LoRA adapters. Titan Takeoff manages the routing and batching of these LoRA adapters for seamless integration into applications.
Yes, unlike most offerings on the market, Titan Takeoff supports all hardware types, including legacy hardware.
There are three main ways in which Titan Takeoff reduces customers' AI maintenance costs:
1) It is a robust and battle-tested AI inference server. This allows internal developers to focus on building business-specific applications rather than battling with regular infrastructural challenges.
2) TitanML's experts stay on top of the latest models and methods, so customers need not waste time building new model and method integrations.
3) Best-in-class support throughout a customer's entire AI journey. TitanML becomes a trusted partner for you to ask all of your AI queries and questions to.