AI cost and latency

Extracted Attributes

• DeepSeek Model Type

  Open weight

• Impact of High Latency

  Increased processing times, reduced system responsiveness, hindering real-time/large-scale AI applications, increased job completion times (JCT), underutilization of GPU resources.

• Definition of AI Latency

  The delay in processing input data and generating output by an AI model; time it takes to process inputs and generate outputs.

• Causes of Compute Latency

  Complex models, inefficient algorithms, hardware limitations, increased computational overhead from more parameters.

• OpenAI GPT-4 Training Cost

  Over $100 million USD (reported in 2023)

• Benefits of Reducing Latency

  Increased efficiency, cost savings, competitive advantage, improved decision-making.

• DeepSeek-R1/V3 Training Cost

  $6 million USD

• DeepSeek Cost Reduction Techniques

  Mixture of Experts (MoE) layers, use of less powerful AI chips due to trade restrictions

• Factors in AI System Design Balance

  Latency, throughput, cost, accuracy

• DeepSeek-R1/V3 Computing Power Usage

  Approximately one-tenth of Meta's Llama 3.1

Timeline

• DeepSeek (Hangzhou DeepSeek Artificial Intelligence Basic Technology Research Co., Ltd.) was founded by Liang Wenfeng in Hangzhou, Zhejiang, China. (Source: Wikipedia)

  2023-07

• OpenAI's GPT-4 training cost was reported to be over $100 million USD. (Source: Wikipedia)

  2023

• Sam Altman, CEO of OpenAI, emphasized the paramount importance of reducing AI cost and latency and building robust AI chip infrastructure during an interview on the All-In Podcast. (Source: Document 8905c897-bf22-4c6e-a62d-73123999ebf4)

  2023-11

• Nvidia's share price dropped sharply, losing US$600 billion in market value, following 'shock waves' through the industry caused by DeepSeek's breakthrough in cost-effective and high-performing AI models. (Source: Wikipedia)

  Unknown

• DeepSeek launched its eponymous chatbot alongside its DeepSeek-R1 model. (Source: Wikipedia)

  2025-01

DeepSeek

Hangzhou DeepSeek Artificial Intelligence Basic Technology Research Co., Ltd., doing business as DeepSeek, is a Chinese artificial intelligence (AI) company that develops large language models (LLMs). Based in Hangzhou, Zhejiang, Deepseek is owned and funded by the Chinese hedge fund High-Flyer. DeepSeek was founded in July 2023 by Liang Wenfeng, the co-founder of High-Flyer, who also serves as the CEO for both of the companies. The company launched an eponymous chatbot alongside its DeepSeek-R1 model in January 2025. Released under the MIT License, DeepSeek-R1 provides responses comparable to other contemporary large language models, such as OpenAI's GPT-4 and o1. Its training cost was reported to be significantly lower than other LLMs. The company claims that it trained its V3 model for $6 US million—far less than the $100+ US million cost for OpenAI's GPT-4 in 2023—and using approximately one-tenth the computing power consumed by Meta's comparable model, Llama 3.1. DeepSeek's success against larger and more established rivals has been described as "upending AI". DeepSeek's models are described as "open weight," meaning the exact parameters are openly shared, although certain usage conditions differ from typical open-source software. The company reportedly recruits AI researchers from top Chinese universities and also hires from outside traditional computer science fields to broaden its models' knowledge and capabilities. DeepSeek significantly reduced training expenses for their R1 model by incorporating techniques such as mixture of experts (MoE) layers. The company also trained its models during ongoing trade restrictions on AI chip exports to China, using weaker AI chips intended for export and employing fewer units overall. Observers say this breakthrough sent "shock waves" through the industry which were described as triggering a "Sputnik moment" for the US in the field of artificial intelligence, particularly due to its open-source, cost-effective, and high-performing AI models. This threatened established AI hardware leaders such as Nvidia; Nvidia's share price dropped sharply, losing US$600 billion in market value, the largest single-company decline in U.S. stock market history.

Web Search Results

• A practical guide to Amazon Bedrock latency-optimized inference

  In AI applications, there’s a constant balancing act between model sophistication, latency, and cost, as illustrated in the diagram. Although more advanced models often provide higher quality outputs, they might not always meet strict latency requirements. In such cases, using a less sophisticated but faster model might be the better choice. For instance, in applications requiring near-instantaneous responses, opting for a smaller, more efficient model could be necessary to meet latency goals, [...] In production environments, overall system latency extends far beyond model inference time. Each component in your AI application stack contributes to the total latency experienced by users. For instance, when implementing responsible AI practices through Amazon Bedrock Guardrails, you might notice a small additional latency overhead. Similar considerations apply when integrating content filtering, user authentication, or input validation layers. Although each component serves a crucial [...] even if it means a slight trade-off in output quality. This approach aligns with the broader need to optimize the interplay between cost, speed, and quality in AI systems.

• Understanding Latency in AI: What It Is and How It Works

  When designing AI systems, you must balance key factors like latency, throughput, cost, and accuracy. While optimizing one area can improve performance, it often comes at the expense of the others. For example, reducing latency might increase costs or affect the system’s accuracy. Balancing Latency and Throughput [...] ### Compute Latency in AI Systems Compute latency is the delay in processing input data and generating output by an AI model. Cause: Complex models, inefficient algorithms, or hardware limitations that slow down computation. Impact: Increased processing times reduce system responsiveness, hindering real-time or large-scale AI applications. [...] ### Economic and Operational Advantages of Reducing Latency Lower latency brings clear economic and operational benefits: Increased Efficiency: Faster systems can handle more tasks at once. Cost Savings: More efficient systems may reduce the need for additional resources. Competitive Advantage: Faster AI systems can outperform competitors, giving you an edge in the market. ### Improved Decision-Making in AI Applications

• Latency in AI Networking: Inevitable Limitation to Solvable Challenge

  In AI back-end networking, different types of latency metrics exist, including head, average, and tail latency. In AI networking, tail latency plays the most significant role in determining network efficiency, GPU utilization, and overall performance, especially for distributed and time-sensitive AI workloads. Workload balance: the slowest path (also referred to as worst-case tail latency) is the one that most influences job completion time (JCT). [...] Why is latency a critical factor in AI networking performance?Latency, the delay in data transfer between systems, significantly impacts AI workload efficiency. In distributed AI tasks, especially during training and inference, high latency can lead to increased job completion times (JCT) and underutilization of GPU resources. Minimizing latency ensures timely data delivery, optimal synchronization across GPUs, and overall improved system performance. [...] Latency – the delay in data transfer between systems – is a critical factor in AI back-end networking. Latency impacts the efficiency of data processing and model training, and it plays a significant role in the overall performance of AI applications.

• AI Cost Optimization Strategies For AI-First Organizations - CloudZero

  Here’s what we mean: Training: Switch to more cost-efficient GPU instances or use pre-trained models to reduce compute expenses. For early experiments, train on smaller datasets before scaling up. Inference: Balance latency and cost using smaller models or dedicated inference accelerators. Deploy lightweight models for production to minimize resource consumption. Data storage and transfer: Watch out for cross-region transfer costs, an often “hidden” cost in the cloud. [...] For example, AWS Mumbai and Google Cloud São Paulo offer significantly cheaper AI compute than US-based regions. Also consider cloud region arbitrage to optimize costs while maintaining acceptable network latency. A good real-world example here is ByteDance. It trains its AI models in Singapore instead of the US. This helps the team shave costs without sacrificing performance. ### 15. Enable AI model caching for repeat queries [...] This approach can reduce data transfer and cloud inference costs, especially for latency-sensitive applications. ### 10. Take advantage of open-source AI models instead of proprietary APIs Many companies pay steep fees for API-based AI services like OpenAI’s GPT-4, Google’s Gemini, or AWS Bedrock. A cost-effective alternative? Switch to open-source LLMs and host them in-house or on a private cloud. Here’s why it makes sense:

• Sources of Latency in AI and How to Manage Them - Telnyx

  This delay can significantly impact AI applications' performance and user experience, particularly those requiring real-time interactions. Understanding latency and optimizing it is crucial for the efficiency of AI systems. ## Understanding latency in AI systems Latency in AI systems is the time it takes to process inputs and generate outputs. [...] This delay includes various operational components such as data preprocessing, mathematical computations within the model, data transfer between processing units, and postprocessing outputs. ## Sources of latency ### Compute latency Compute latency is the time the AI model takes to perform computations and execute its inference logic. Complex models with more parameters, such as large deep learning models, typically have higher compute latency due to increased computational overhead. [...] Latency is a critical factor in the performance and usability of AI systems. Understanding the sources of latency and implementing strategies to optimize it can significantly enhance user experience and expand the viability of AI applications. By streamlining model architecture, optimizing data transfers, and leveraging hardware advancements, developers can create responsive and efficient AI systems. Contact our team of experts to discover how Telnyx can power your AI solutions.