Inference Innovation: How the AI Industry is Reducing Inference Costs

10 min readApr 18, 2024

Finding ways to lower inference costs is perhaps the most critical challenge that businesses face when implementing AI strategies.

In the AI lifecycle, the process of training models is a significant capital expenditure, typically characterized by intense computational and data demands over a defined period. However, it’s inference — the application of those trained models — that represents a recurring operational cost that can quickly surpass initial training expenses due to its ongoing nature.

The AI industry understands this challenge which is why there is intense competition among solutions providers focused on lowering AI inference costs. This progress is enabling broader and more frequent deployment of AI technologies across various industries, making AI accessible to a wider range of businesses, including startups with limited budgets. This concerted effort drives not only economic benefits for solution providers who are able to capture market share but also spurs technological innovations in hardware and software development, crucial for advancing AI applications sustainably and inclusively.

Technical Drivers of Inference Costs

The architectural complexity of a model, which includes the depth and breadth of neural networks, directly impacts the inference costs. More complex models with more layers and parameters not only require more memory but also more computational power to process each inference request.

FLOPS Requirements

Computational Intensity: AI models, particularly deep learning models like GPT-4 or Llama-2, require a significant amount of computational power, measured in FLOPS. This metric indicates the number of floating-point operations a system can perform per second, which is critical in determining the feasibility and cost of running such models.
Cost Implications: The cost of inference is heavily influenced by the FLOPS requirements of the model. Higher FLOPS denote more complex calculations per second, leading to increased use of computational resources and energy, which in turn raises operational costs.

Processing Costs by Application

In the context of AI applications, the inference costs vary significantly across different data types such as text, image, and video, primarily due to differences in data complexity and processing requirements.

Text Processing: Text-based inference primarily depends on token processing where each piece of text (word or part of a word) is a token. The computational cost for text is generally lower per unit of data compared to image or video, as the data structure is less complex. However, the length of the text and the model’s parameter size can increase the FLOPS required, influencing the cost. For example, processing a 512-token input on a model like GPT-4 might require significantly fewer computational resources compared to a high-resolution image analysis, making text inference generally less expensive in terms of computational needs.
Pixel Processing: For image and video processing models, the cost is driven by the resolution and the amount of pixel data to be processed. Higher resolution images and videos naturally require more computational power to analyze, increasing the FLOPS required and thereby the cost.
Image/Video Generation: For image generation tasks — leveraged in applications such as digital art generation, medical imaging, and virtual design — the computational cost primarily hinges on the resolution and the complexity of the images being generated. Video generation steps up the complexity and cost as it essentially involves generating multiple images (frames) per second. For instance, generating a 10-second video clip at 1080p resolution and 30 frames per second can be substantially more demanding and costly due to the multiplied computational load of processing up to 300 individual frames. This process not only multiplies the computational load by the number of frames generated per second but also adds costs associated with temporal coherence and frame interpolation to ensure smoothness and continuity in the generated video. Models used in video generation often operate on sequential frame data, integrating temporal dynamics which adds to the computational overhead.

Inference Pricing Dynamics

Businesses are increasingly savvy about ensuring that the pricing models offered by service providers align with their operational needs and financial goals, blending flexibility, cost-effectiveness, and predictability to maximize their technological investments.

Types of Inference Pricing Models:

Compute Time: Fees are based on the duration of processing time required per task and influenced by the choice of processing unit and region. For example, using a GPU instance such as the NVIDIA Tesla V100 on AWS’s EC2 service might be priced at approximately $3.06 per hour, depending on the region and specific instance configurations.
Query Volume: Providers may charge per individual inference executed, which can rapidly accumulate in user-intensive applications. For example, the pricing might start at $1.50 per 1000 queries for the first 1 million queries per month.
Data Transfer Fees: Costs incurred for data ingress and egress in the AI processing environment, especially significant in cloud-based deployments. For example, a company may charge about $0.087 per GB for the first 10 TB per month of egress.

Large cloud providers like AWS, Google Cloud, and Azure offer scalable infrastructures and might leverage economies of scale to provide certain advantages. However, their pricing models can be complex and unpredictable. Smaller providers often provide more transparent and sometimes more economical options but may lack the extensive infrastructure and scalability offered by larger competitors.

Overview of Inference Pricing (USD/hr/GPU)

Advanced Solutions for Reducing Inference Costs

To effectively lower AI inference costs, companies are actively pursuing innovations across various technical domains. Cost optimization in this sense typically comes from either faster inferencing/lower latency or more efficient use of compute resources. Here are some primary advancements that are contributing to cost reduction:

Hardware Optimization:

GPU Development: Examples such as Google’s TPUs (Tensor Processing Units) and NVIDIA’s Tensor Cores (like the A100 and H100) focuses specifically on accelerating the types of calculations most common in deep learning models. This speed is achieved through architectural improvements that allow for more parallel processing of data, which is vital for handling the large datasets typically used in AI. GPU providers are continually pushing the boundary to produce more efficient machines.
Energy Efficiency: By reducing the power required for each calculation, companies can dramatically decrease the cost per inference, enabling more widespread and continuous use of AI technologies without incurring prohibitive energy bills. Additionally, newer hardware models often integrate enhanced heat dissipation technologies, further improving energy efficiency and reducing the need for costly cooling systems in data centers. This combination of high-speed, low-power consumption, and reduced cooling requirements contributes significantly to the overall reduction in operation costs. Cloud providers can then pass on cost savings to the end customer in the form of lower inference costs.

Software Optimization:

Model Quantization: This technique reduces the precision of the numbers used in computations (from floating-point precision to lower-bit integers), which decreases the model size and speeds up inference without losing significant accuracy. Quantization makes models lighter and faster, thus reducing the computational resources required.
Model Pruning: Pruning involves removing redundant or non-significant weights from a model, which can substantially reduce the complexity and size of neural networks. This streamlined version of the model requires less computational power to run, lowering both energy usage and inference time.

Middleware Enhancements:

Model-Serving Frameworks: Tools like NVIDIA’s Triton Inference Server optimize the deployment of AI models by supporting multi-model serving, dynamic batching, and GPU sharing. These features improve the throughput and efficiency of GPU resources, helping in reducing operational costs.
Load-Balancing Techniques: Advanced load-balancing algorithms ensure that inference requests are efficiently distributed across available computing resources, preventing bottlenecks and maximizing hardware utilization.

API Management:

Managed AI Services: Cloud providers offer AI services through APIs, which abstract the underlying infrastructure complexities and manage scalability. This model allows businesses to pay only for the inference computations they need, without the overhead of training or managing physical servers and data centers.
Auto-Scaling: Modern API management platforms include features that automatically scale the number of active server instances based on the demand. This means that during periods of low demand, fewer resources are used, reducing costs. Conversely, during peak demand, the system can scale up to ensure consistent performance without permanently allocating resources.

Prompt Engineering:

Reduced Computational Overhead: Efficient prompts are designed to elicit the most relevant information from an AI model in the fewest number of tokens or processing steps. This directly cuts down the volume of data processed, thereby reducing the computational power required. For example, a well-designed prompt can avoid the need for follow-up questions or clarifications, streamlining the process to a single inference cycle.
Minimization of Latency and Processing Time: Prompt engineering can also reduce the latency in response times by decreasing the complexity of the computations needed. This not only improves user experience but also minimizes the energy consumption and associated costs for each query processed.

These innovations are integral to reducing the costs associated with running AI models and making AI more accessible and sustainable for a wide range of applications. Each approach addresses different aspects of the inference process, from the initial computation to how models are deployed and interacted with, showcasing a comprehensive effort to optimize efficiency and reduce expenses.

GMI Cloud’s Strategy

Streamlined Operational Efficiency:

GMI Cloud leverages its vertically integrated structure to streamline the deployment and management of AI services. For instance, GMI Cloud might use NVIDIA GPUs tuned for specific AI workloads, paired with custom software that maximizes GPU utilization. By managing the entire stack — from hardware selection to software development and deployment — GMI Cloud eliminates the inefficiencies often encountered when integrating components from multiple vendors. This approach not only speeds up the setup and scaling processes but also significantly reduces operational complexities and costs.

Advanced Software Stacks

GMI Cloud has built powerful a software platform to make it both easier and more efficient to run inference. Here are some key features:

Multi-tenant Kubernetes Environments: GMI Cloud leverages multi-tenant Kubernetes clusters to orchestrate containerized AI workloads with high efficiency, significantly reducing infrastructure costs. These environments enable precise resource isolation and utilization metrics per tenant, ensuring optimal allocation without resource wastage. Kubernetes dynamically orchestrates CPU and GPU resources to handle workload spikes effectively. For instance, during AI model retraining or batch inference tasks, Kubernetes can elastically scale resources, using Horizontal Pod Autoscaling based on real-time metrics such as GPU utilization or custom metrics like queue length. For example, a typical deployment might scale from using 2 GPU instances to 10 during peak load and then back down, optimizing the cost per inference operation from potentially hundreds of dollars to under a dollar per hour depending on the instance types used.
InfiniBand-Linked Containerization: InfiniBand architecture provides a significant advantage in GMI Cloud’s containerized environments, offering low-latency, high-throughput connections that are crucial for AI data throughput demands. InfiniBand supports up to 200 Gbps bandwidth and sub-microsecond latencies, which is critical for reducing communication overhead in distributed AI models like those used in parallel video processing or large-scale machine learning algorithms spanning multiple nodes. By implementing InfiniBand, data transfer between nodes bypasses the CPU, directly accessing memory, which drastically reduces latency and CPU load. This setup minimizes the time and computational overhead associated with large-scale tensor operations in neural networks, thus reducing inference costs per frame or per query, particularly in use cases involving high-resolution image analysis or real-time video streaming analytics.
Compatibility with NVIDIA Network Interface Microservices (NIM): Integrating NVIDIA NIM significantly enhances the network efficiency within GMI Cloud’s infrastructure, tailored specifically for GPU-accelerated tasks. NIM provides advanced networking features that optimize data paths and manage congestion in multi-node deployments, crucial for maintaining throughput in large-scale, distributed AI applications. For example, in an environment running complex models like Transformers, where inter-GPU communication is frequent and intensive, NIM helps in reducing jitter and improving bandwidth utilization, which are key to accelerating inference and training phases. Improved network efficiency ensures that each node can process data at the peak theoretical performance of the GPUs (e.g., NVIDIA’s H100 GPUs with NVLink offering up to 900 GB/s), significantly reducing the time-to-inference per data point and thereby lowering the cost associated with running advanced AI models like GPT for natural language processing tasks or Sora for video generation.

Industry-Specific Customizations:

GMI Cloud enhances client operations by delivering industry-specific customizations, ensuring that both hardware and software are intricately aligned with unique sector demands, such as healthcare, finance, or retail. This tailored approach not only boosts efficiency and speeds up AI-driven processes but also significantly cuts operational costs by reducing unnecessary computational workload and energy consumption. Clients benefit from optimized performance tailored to their specific industry needs. These custom solutions also offer scalability, enabling businesses to adapt to new challenges and grow without substantial reinvestment in technology. Ultimately, this strategic focus provides GMI Cloud’s clients with a competitive edge, leveraging optimized AI solutions that outperform generic alternatives and cut down on inference costs.

Conclusion

Lowering inference costs helps businesses enhance profitability by reducing long-term operational expenses, scale their AI solutions more effectively, and provide a competitive edge by making AI-driven services more economically viable.

The ongoing development of more sophisticated, cost-effective inference solutions will likely open up new possibilities across various sectors, driving innovation and competitiveness. Businesses can look forward to more accessible, efficient, and powerful AI tools that promise not only to transform operations but also to democratize access to AI technology.