With the recent wave of innovation in large language models (LLMs) like GPT-3, ChatGPT, and others, many organizations are exploring ways to leverage these powerful AI models to build next-generation applications. However, deploying LLM-based apps at scale requires significant computational resources, often in the form of GPU-accelerated cloud infrastructure. In this post, we examine the total cost of ownership (TCO) implications as well as the potential return on investment (ROI) for enterprises taking this cutting-edge path.
The High Costs of Inference at Scale:
Training an LLM is an extremely resource-intensive process that can cost millions of dollars for models at the scale of GPT-3’s 175 billion parameters. Fortunately, major AI research labs like OpenAI, Google, and others have made some of these large models available via APIs and cloud services.
However, using these API services for inference (running user queries/inputs through the model) at enterprise scale is still very expensive. For example, OpenAI charges $0.06 per 1,000 tokens for their davinci model. With many enterprise use cases requiring much higher throughput, companies often choose to run LLM inference in-house on GPU clusters.
Renting high-end GPU instances like NVIDIA A100s or V100s on cloud platforms can cost $10-$20 per hour. Operating an LLM app serving hundreds or thousands of concurrent requests could easily translate to $100,000+ per month in just cloud compute costs.
The TCO Picture:
Beyond just cloud compute costs, enterprises deploying LLM apps must consider:
- Data ingestion, preparation, and management costs
- Cluster infrastructure and DevOps costs.
- Model finetuning and customization costs.
- Application development and integration costs
- Talent recruitment and training for AI/ML skills
It’s not uncommon for the TCO over a 3-year period to run into the tens of millions of dollars range for mature LLM-based applications deployed at scale across an enterprise.
Potential ROI Factors:
While the expenses are substantial, the potential ROI enabled by LLM capabilities like natural language processing, knowledge synthesis, code generation, and general task automation is immense.
- Enhancing employee productivity and efficiency via AI assistants
- Accelerating software development and reducing technical debt
- Improving customer experience via intelligent chatbots/agents
- Extracting insights and knowledge from vast data repositories
- Automating manual processes and knowledge work tasks
- Enabling entirely new AI-driven product/service offerings
For enterprises that can effectively harness LLMs as force multipliers, the ROI potential dwarfs the upfront costs. Analysts estimate LLM-enabled enterprise productivity gains reaching $3+ trillion annually across major economies.
As with any transformative technology, mastering the TCO economics while capitalizing on the ROI opportunities posed by large language models will be a key challenge for businesses in the coming years. Those that can strike this balance will be well-positioned to maintain their competitive edge.
Few questions to ask before planning a LLM based projects are:
- Do I really need a 170+ billion parameter-based model for my use case?
- Do I really need less than a second response time from my LLM application?
If the answer to above questions is “No” or “don’t know” how about trying the https://jiva.live/ based on-premise or on-cloud ready use LLM solutions inferencing on CPU with very less to minimum TCO.
