Managing the Cost of AI
What practical strategies can be used to slash the cost of deploying LLMs?

Managing the Cost of AI

Large Language Models (LLMs) have revolutionized artificial intelligence, setting new benchmarks in tasks ranging from text generation to sentiment analysis. Yet, their deployment in production environments is not without challenges. These models demand extensive computational resources, substantial storage, and considerable energy, posing significant operational costs, which are usually computed based on the number of tokens processed in both training and inference, where a token is a word or a fragment of a large word. The cost of processing these tokens is proportional to the size of the model, with large models being the most expensive. ?The expense of running an LLMs are starkly emphasized when considering that the price for a single AI-driven query can be nearly a thousandfold compared to a standard Google search, significantly narrowing the profit potential for AI-driven services. Moreover the rapid pace of AI innovation necessitates frequent updates with increasingly sophisticated models, intensifying the need for cost-effective deployment strategies. This article delves into practical techniques for deploying LLMs efficiently, balancing performance with cost considerations to facilitate wider access to AI advancements across sectors.

Cost-Effective Model Deployment

In the context of Large Language Models (LLMs), cost-effective model deployment involves strategies aimed at reducing the computational and financial overheads associated with developing, training, and deploying these advanced AI systems. Key strategies include:

  • Using Smaller Models: This approach focuses on developing or selecting models that are inherently less complex and thus require fewer computational resources for both training and inference phases. Smaller models can significantly lower hardware requirements and operational costs, making AI projects more accessible and sustainable, especially for organizations with limited resources. One such smaller model is Microsoft's Orca 2 LLM, which outperforms models that are 10x larger[i]. Many more leaner models are constantly being developed and LLM leader boards can be used to track the model size and performance for open[ii] and proprietary[iii] models.
  • Open Source Models Utilization: By leveraging popular open-source LLMs, organizations can avoid the costs tied to proprietary models[iv]. The open-source ecosystem provides a wealth of models that have been pre-trained on diverse datasets, offering a solid foundation for a wide range of applications. Utilizing these models not only saves on licensing fees but also benefits from the collective advancements and optimizations contributed by the global research community.
  • Fine-Tuning Pre-trained Models: This technique involves customizing pre-trained LLMs with domain-specific data to tailor the model's performance to specific needs without the need to train a model from scratch. Fine-tuning allows organizations to achieve high levels of accuracy and efficiency by adapting a general-purpose model to their unique requirements, thereby reducing the time and computational power needed to develop effective AI solutions[v]. This approach allows us to get even more performance from smaller, more cost effective models, assuming that the cost of fine tuning is negligible compared to life-time inference costs.
  • Retrieval-Augmented Generation (RAG): High operational costs associated with Large Language Models (LLMs) can be reduced by integrating RAG based retrieval mechanisms that leverage existing knowledge bases. This approach not only enhances response quality through the enrichment of model outputs with diverse, contextually relevant information but also significantly reduces computational demands. By employing smaller, more efficient models that work in tandem with expansive databases, RAG pipelines effectively lower both computational costs and latency. Furthermore, the retrieval component of RAG contributes to mitigating bias and improving fairness by sourcing a broader array of perspectives, thus offering a more balanced and comprehensive view. This dual benefit of cost reduction and improved output quality makes RAG an attractive strategy for deploying AI solutions more sustainably[vi].
  • LLM Memory Management: Frameworks such as memGPT introduce a cost-effective way to manage the operational demands of Large Language Models (LLMs) by optimizing context length handling. Unlike traditional transformer models that see a quadratic increase in computational time and memory with longer contexts, memGPT leverages a memory mechanism[vii]. This allows for accessing extensive contextual information without the hefty computational burden. It efficiently caches and retrieves context, avoiding the need to process the entire context for each inference, thus reducing computational costs. This approach not only makes memGPT efficient but also maintains the quality of outputs for tasks requiring long-context understanding, offering a practical solution for deploying LLMs more sustainably.
  • Custom Ontology/Semantic Layer Integration: Integrating custom ontologies and semantic layers into LLMs involves embedding domain-specific knowledge structures into the model. This enhances the model's understanding of particular fields or industries, improving its ability to generate accurate and contextually relevant responses without the need for extensive computations on new or unseen data. This method leverages structured knowledge to guide the model's responses, enhancing precision while maintaining efficiency[viii].

Collectively, these strategies facilitate the deployment of LLMs in a manner that balances performance with cost-efficiency, enabling broader access to AI capabilities across various sectors.

Advanced Prompt and Input Management

Advanced prompt and input management is a critical aspect of optimizing Large Language Models (LLMs) for both efficiency and cost-effectiveness. This domain encompasses strategies that refine how models are queried and how their inputs and outputs are handled, focusing on minimizing computational demands without compromising the quality of interactions. Key strategies include:

  • Prompt Compression and Adaptation: This approach involves the use of methodologies to compress prompts[ix], reducing the number of tokens required to query the model, thereby reducing cost, while preserving the essential meaning and context of the input. Adaptation strategies further refine this process, ensuring that prompts are not only concise but also tailored to elicit the most accurate and relevant responses from the LLM, effectively maintaining semantic integrity with minimal input[x].
  • Efficient Prompt Engineering: This strategy emphasizes the crafting of prompts that are both succinct and potent, aiming to reduce unnecessary computational overhead by eliminating verbosity without losing the prompt's effectiveness. Efficient prompt engineering is about finding the optimal way to communicate with the LLM, ensuring that the model receives all necessary information in the most compact form possible, thus reducing cost by streamlining the processing workload[xi].
  • Caching and Summarization: Implementing caching mechanisms allows for the storage and subsequent reuse of LLM responses, significantly reducing the need for repeat computations for frequently asked questions or common prompts[xii]. Summarization techniques complement this by efficiently processing and condensing long documents or chat histories, enabling LLMs to grasp the gist of extensive information without sifting through every detail, thereby accelerating response times and decreasing computational expenses[xiii].

Together, these advanced prompt and input management techniques enhance the operational efficiency of LLM deployments. They enable faster, more cost-effective interactions with LLMs by reducing the volume of data processed and focusing the model's attention on the most relevant information, thereby optimizing both the user experience and the utilization of computational resources.

Divide and Conquer

The optimization of Large Language Models (LLMs) for practical use involves innovative strategies that enhance performance while managing computational costs. Three notable concepts that contribute to this optimization include LLM Cascades, Task Breakdown and Planning, and Custom Ontology/Semantic Layer Integration.

  • LLM Cascades: This concept involves chaining together a series of LLMs in a sequential manner, where the output of one model serves as the input for the next. This cascade continues until a model produces an acceptable response. The primary advantage of LLM cascades is the potential for significant savings in computational costs, as it allows for the use of simpler models at initial stages, reserving more complex and computationally demanding models for refining outputs only when necessary. This approach ensures efficient use of resources by escalating the complexity of model interaction based on the demands of the task[xiv].
  • Task Breakdown and Planning: Complex tasks often require a level of understanding and processing that can be taxing on a single LLM. By breaking down these tasks into simpler, more manageable components, each part can be processed more efficiently and effectively. This strategy involves planning how to decompose tasks and sequence the processing steps, ensuring that each component is addressed optimally. This not only streamlines the workflow but also enhances the overall performance of the LLM by focusing its computational power on smaller, more defined problems.
  • Function Calling: Function calling to external APIs[xv] presents a cost-efficient method for deploying Large Language Models (LLMs). By offloading specific, computationally intensive tasks to specialized external services, LLMs can operate within a leaner scope, focusing on tasks that require deep language understanding while relying on APIs for ancillary data processing or supplementary tasks. This approach can significantly reduce the computational load on the LLM itself, leading to lower operational costs. Additionally, external APIs can be invoked on an as-needed basis, which means that costs are incurred only when these services are used, rather than maintaining a larger, more expensive infrastructure constantly. This modular approach allows for the flexible scaling of services, aligning operational expenditure more closely with actual usage and needs, and ensuring that resources are allocated efficiently.
  • Ensembles: Ensemble techniques, like Mixtral 8x7B, a Sparse Mixture of Experts language model, offer an innovative approach to enhancing Large Language Models (LLMs) performance while concurrently reducing operational costs[xvi]. By strategically combining the outputs of multiple, diverse models, ensembles leverage the strengths of each to produce a more accurate, robust output than any single model could achieve on its own. This method not only elevates performance through the aggregation of varied perspectives but also introduces a cost-efficient model deployment strategy. Specifically, ensemble techniques can utilize a blend of smaller, less resource-intensive models alongside larger models, optimizing computational resource allocation. This balance ensures that simpler tasks are handled by more economical models, reserving the computational power of larger, more expensive models for complex queries. Consequently, ensemble approaches like MixTRL represent a sophisticated method to achieve superior LLM performance, optimizing the trade-off between accuracy and operational expenditure.
  • Multi-Agents: Multi-agent frameworks represent a sophisticated approach to optimizing the deployment and operation of Large Language Models (LLMs), offering significant potential for cost reduction[xvii]. By distributing tasks across multiple specialized agents, this framework allows for a more efficient allocation of computational resources, where each agent is tasked with a specific aspect of a problem, based on its expertise and capabilities. Multi-agent frameworks offer a robust strategy for enhancing the efficiency and reducing the cost of LLM implementations. Through task specialization, dynamic resource allocation, and collaborative learning, these frameworks present a scalable and cost-effective solution for leveraging the power of LLMs across a wide range of applications.The key to the cost-saving potential of multi-agent frameworks lies in their ability to parallelize processing and reduce the computational burden on any single system. Instead of relying on a monolithic LLM to process complex queries, tasks are broken down and managed by several agents, each handling a part of the process. This division of labor not only speeds up the overall processing time but also ensures that less powerful, and consequently less costly, models can be employed for specific tasks where they are sufficient, thereby reducing the need for high-end, expensive computational resources.Furthermore, multi-agent frameworks facilitate a dynamic and adaptive approach to task management. Agents can negotiate tasks among themselves, dynamically reallocating resources based on demand and availability. This flexibility allows the system to adapt to changing workloads without overburdening any single component, optimizing resource use in real-time and minimizing unnecessary expenditures.Additionally, by incorporating specialized agents that can cache responses or utilize pre-computed results, the system can avoid redundant computations for frequently encountered queries, further reducing operational costs. The collaborative nature of multi-agent systems also means that they can collectively learn and improve over time, leading to more efficient operations and a continuous reduction in the cost of implementing LLMs.However, multi-agent systems can be more expensive for simple tasks where agents become “chatty”, driving up costs by passing many messages back and forth, driving up the number of tokens. In order to ensure costs are manages, there needs to be careful consideration to the multi-agent team composition and conversations need to be orchestrated carefully.

Together, these concepts represent advanced strategies in the field of AI and machine learning, aimed at optimizing the performance of LLMs. By efficiently managing computational resources, breaking down complex tasks, and integrating domain-specific knowledge, these strategies enable more effective and practical applications of LLMs across various domains.

Can specialization, separation of concerns and division of labour lead to economies in AI?

Model and Hardware Optimization Techniques

Model optimization techniques are essential strategies used to enhance the efficiency and performance of neural networks, particularly Large Language Models (LLMs), making them more practical and cost-effective for a wide range of applications. These techniques aim to reduce the computational load and memory usage of models without significantly compromising their accuracy or performance. Key optimization strategies include:

  • Pruning, Retraining and Sparsity: This technique involves training a dense neural network until it converges to a satisfactory level of performance, followed by the pruning of the network. Pruning removes parts of the model deemed unnecessary or redundant, such as weights close to zero, which do not contribute significantly to the model's output. The model may then be retrained to fine-tune its performance, compensating for any loss in accuracy due to the pruning process[xviii].Sparse activation techniques focus on optimizing the model to efficiently process inputs that result in a significant number of zero activations. By designing the network to bypass or minimize computation on these activations, the overall speed of the model's computations can be increased, making the model more efficient[xix].
  • Quantization (Post-training Quantization): Quantization reduces the precision of the model's parameters from higher precision formats like FP32 to lower precision formats such as FP16 or INT8. This reduction in precision decreases the model's memory footprint and can significantly speed up inference times, especially on hardware optimized for lower precision arithmetic[xx]. . By switching from standard 32-bit floating-point formats (FP32) to lower-precision formats like bfloat16, models can significantly reduce memory usage. This reduction in memory footprint not only accelerates computation by allowing more data to be processed in parallel but also makes it possible to run larger models on hardware with limited memory resources[xxi].
  • Knowledge Distillation: In this process, a smaller "student" model is trained to replicate the behavior of a larger "teacher" model. The student model learns from the output distributions of the teacher model, enabling it to achieve comparable performance while being significantly more lightweight and efficient for inference tasks[xxii].
  • Selective Execution: This approach involves executing only essential parts of the model for each inference request. By dynamically determining which parts of the model need to be activated based on the input data (for instance, skipping over parts of the network that contribute zero activations), computational resources are conserved, leading to faster inference times and reduced power consumption.
  • Efficient Hardware Utilization: Optimizing the use of hardware resources is crucial for computational efficiency. This involves selecting the appropriate hardware for model deployment, such as high-end GPUs (e.g., Nvidia RTX 4090), which are optimized for deep learning computations. Techniques like multithreading and multi-streaming further enhance efficiency by enabling parallel processing of model computations, thereby reducing the time required for training and inference[xxiii].

These model optimization techniques provide a comprehensive toolkit for improving the performance of LLMs, enabling their deployment in resource-constrained environments and facilitating faster, more responsive AI-powered applications.

Sometimes less is more

Additional Considerations

These strategies address various aspects of model deployment, from operational cost management to computational efficiency and environmental impact. Key concepts include:

  • Rate Limiting and Quota Management: Implementing mechanisms to control the frequency and volume of model usage can help manage operational costs and prevent resource overutilization. By setting rate limits and quotas, organizations can ensure that computational resources are allocated efficiently, avoiding unnecessary expenses.
  • Energy-Efficient Computing: With increasing awareness of the environmental impact of computing, considering the energy efficiency of hardware and algorithms becomes essential. Optimizing models and computational processes for energy efficiency not only reduces operational costs but also contributes to sustainability efforts by lowering the overall power consumption of LLM deployments.
  • Input Pre-processing: By applying pre-processing techniques to filter or modify inputs before they reach the LLM, the computational cost can be significantly reduced. Collaborative filtering helps in identifying and prioritizing relevant information, ensuring that the model focuses its computational efforts on data most likely to influence the output.
  • Cloud vs. Local Execution: Deciding between cloud-based and local execution of models involves weighing factors such as cost, latency, and resource availability. Cloud execution offers scalability and ease of access, while local execution may reduce latency and offer better control over data security and operational costs.

Managing LLMs in the Enterprise

At Accenture we are bringing these ideas together as part of our Specialized Services to Help Companies Customize and Manage Foundation Models, designed to help companies customize and scale the value of generative AI[xxiv]:

  • Switchboard for model selection: 埃森哲 's switchboard is our proprietary tool that allows users to select a combination of large-language models (LLMs) based on their business context or factors such as cost or accuracy. For example, a major entertainment company is testing the model switchboard to compare different models before choosing one
  • Customization and managed services for LLMs: Accenture offers services to customize LLMs for specific use cases and data sources, as well as provide ongoing fine-tuning and prompt engineering. These services can help companies contextualize AI models for their unique needs and drive tangible value from generative AI.
  • Training and certification programs for working with LLMs: 埃森哲 provides comprehensive training and certification programs to help clients effectively use and manage LLMs. Accenture also collaborates with leading academic institutions to create foundation model scholar programs and certifications related to LLM skills.

Conclusion

In conclusion, effectively managing the operational costs of deploying Large Language Models (LLMs) hinges on adopting a multifaceted strategy that includes the optimal use of powerful foundational models, smaller, open-source, and finely-tuned models, advanced prompt management, ensemble techniques, multi-agent frameworks and other optimisations. These approaches collectively aim to strike a balance between maintaining high performance levels and minimizing resource consumption. Enterprises aiming to optimize the deployment of Large Language Models (LLMs) while managing operational costs effectively can look towards comprehensive solutions like those offered by Accenture. With innovative tools such as the model switchboard, Accenture enables businesses to navigate the complexities of LLM deployment by selecting the most efficient models based on performance, cost, and specific business needs. This strategic approach ensures that organizations can harness the transformative power of LLMs in a cost-efficient manner, enabling broader access to cutting-edge AI capabilities across various sectors. The future of LLM deployment lies in continuous innovation and optimization, ensuring that as the field of AI progresses, it remains accessible and sustainable for all.

Advanced AI Innovation 埃森哲 Accenture The Dock


[i] Microsoft's Orca 2 LLM Outperforms Models That Are 10x Larger, https://www.infoq.com/news/2023/12/microsoft-orca-2-llm/.

[ii] Open LLM Leaderboard, https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard

[iii] LMSYS Chatbot Arena Leaderboard, https://huggingface.co/spaces/lmsys/chatbot-arena-leaderboard

[iv] Irugalbandara, Chandra, Ashish Mahendra, Roland Daynauth, Tharuka Kasthuri Arachchige, Krisztian Flautner, Lingjia Tang, Yiping Kang, and Jason Mars. "Scaling Down to Scale Up: A Cost-Benefit Analysis of Replacing OpenAI's GPT-4 with Self-Hosted Open Source SLMs in Production."?arXiv preprint arXiv:2312.14972?(2023), https://arxiv.org/abs/2312.14972.

[v] Gu, Yu, Robert Tinn, Hao Cheng, Michael Lucas, Naoto Usuyama, Xiaodong Liu, Tristan Naumann, Jianfeng Gao, and Hoifung Poon. "Domain-specific language model pretraining for biomedical natural language processing."?ACM Transactions on Computing for Healthcare (HEALTH)?3, no. 1 (2021): 1-23, https://dl.acm.org/doi/abs/10.1145/3458754.

[vi] https://www.forbes.com/sites/forbestechcouncil/2023/11/30/the-power-of-rag-how-retrieval-augmented-generation-enhances-generative-ai/

[vii] Packer, Charles, Vivian Fang, Shishir G. Patil, Kevin Lin, Sarah Wooders, and Joseph E. Gonzalez. "Memgpt: Towards llms as operating systems."?arXiv preprint arXiv:2310.08560?(2023), https://arxiv.org/abs/2310.08560.

[viii] https://www.getdbt.com/blog/semantic-layer-as-the-data-interface-for-llms

[ix] LLMLingua, Designing a Language for LLMs via Prompt Compression, https://www.microsoft.com/en-us/research/project/llmlingua/

[x] Chen, Lingjiao, Matei Zaharia, and James Zou. "FrugalGPT: How to Use Large Language Models While Reducing Cost and Improving Performance."?arXiv preprint arXiv:2305.05176?(2023) https://arxiv.org/abs/2305.05176.

[xi] https://community.deeplearning.ai/t/cost-effective-prompts/414195/2

[xii] https://github.com/zilliztech/GPTCache

[xiii] https://www.aitidbits.ai/p/reduce-llm-latency-and-cost

[xiv] FrugalGPT: How to Use Large Language Models While Reducing Cost and Improving Performance, https://arxiv.org/abs/2305.05176.

[xv] https://platform.openai.com/docs/guides/function-calling

[xvi] Jiang, Albert Q., Alexandre Sablayrolles, Antoine Roux, Arthur Mensch, Blanche Savary, Chris Bamford, Devendra Singh Chaplot et al. "Mixtral of experts."?arXiv preprint arXiv:2401.04088?(2024), https://arxiv.org/abs/2401.04088.

[xvii] How to reduce 78%+ of LLM Cost, AI Jason, https://www.ai-jason.com/learning-ai/how-to-reduce-llm-cost#:~:text=Another%20strategy%20is%20to%20set,rates%20while%20significantly%20reducing%20costs.

[xviii] Zimmer, Max, Megi Andoni, Christoph Spiegel, and Sebastian Pokutta. "PERP: Rethinking the Prune-Retrain Paradigm in the Era of LLMs."?arXiv preprint arXiv:2312.15230?(2023), https://arxiv.org/abs/2312.15230.

[xix] Hoefler, Torsten, Dan Alistarh, Tal Ben-Nun, Nikoli Dryden, and Alexandra Peste. "Sparsity in deep learning: Pruning and growth for efficient inference and training in neural networks."?The Journal of Machine Learning Research?22, no. 1 (2021): 10882-11005, https://dl.acm.org/doi/abs/10.5555/3546258.3546499.

[xx] Understanding the Impact of Post-Training Quantization on Large Language Models, https://huggingface.co/papers/2309.05210

[xxi]? Optimize PyTorch Performance for Speed and Memory Efficiency, https://towardsdatascience.com/optimize-pytorch-performance-for-speed-and-memory-efficiency-2022-84f453916ea6

[xxii] Walsh, P., Bera, J., Sharma, V.S., Kaulgud, V., Rao, R.M. and Ross, O., 2021, November. Sustainable AI in the Cloud: Exploring Machine Learning Energy Use in the Cloud. In 2021 36th IEEE/ACM International Conference on Automated Software Engineering Workshops (ASEW) (pp. 265-266). IEEE, https://ieeexplore.ieee.org/abstract/document/9680315.

[xxiii] Which GPU(s) to Get for Deep Learning: My Experience and Advice for Using GPUs in Deep Learning, https://timdettmers.com/2023/01/30/which-gpu-for-deep-learning/

[xxiv] https://newsroom.accenture.com/news/2023/accenture-launches-specialized-services-to-help-companies-customize-and-manage-foundation-models

Lester Oliver

Leveraging AI tech for real-world challenges

8 个月

Insightful ??

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