LLMOPs: Best Practices for LLM Deployment

Reeshabh Choudhary
5 min readJan 6, 2024

👷‍♂️ Software Architecture Series — Part 17.

When we deal with LLMs, training or fine tuning LLMs takes the center stage of attention, however, it is equally important to make sure that systems leveraging LLMs are working consistently and reliably. And to ensure that, there are certain industry standard practices which are leveraged at various stages of making an LLM ready for production. Adhering to these practices to ensure speed development, deployment and management of LLM models throughout their lifecycle is LLMOPs, which stands for “large language model operations”.

Let us look at the steps involved to prepare a production ready LLM, and best practices involved:

Selection of Base Model

Training an LLM from scratch is a humongous task and might not be in the interest of every organization intending to leverage upon LLM capabilities, as it incurs huge cost. Hence, foundational LLMs, pre-trained on large amounts of data that can be used for a wide range of tasks, can be leveraged and then fine-tuned to specific use cases. Certain organizations have open sourced their models (BLOOM, LLaMa, etc.) out in public, while enterprises like Microsoft, Google, etc. have trained large models which are available on pay per usage basis.

Model adaptation to specific task

LLM models in their raw form may not be producing the desired outputs as per the task requirements and often it calls for the need to tweaking LLMs to improve accuracy and remove hallucinations. Prompt engineering aims to improve the input to LLM so that outputs are matched to expectation criterion. While fine-tuning LLMs involve training the LLMs to improve the accuracy on specific tasks. Fine tuning can be a costly operation, but it eventually leads to a reduction in the cost of inference.

Data Evaluation

In majority of cases, we might not be involved in proper training of LLM models, however, we must be cognizant about the data coming in and responses coming out of the LLMs. It is possible by carefully monitoring the entire system and logging the observations and predictions at different stages.

Monitoring the data at input stage ensures that the data being fed into your models is of high quality. Detecting anomalies or inconsistencies in input data can prevent biased or inaccurate predictions. Being cognizant of the response being produced by LLMs helps in making sure that customers are getting reliable response, which gets very critical in LLMs specific to domains like healthcare, fintech, etc. Regular security audits and tests should be conducted to look for vulnerabilities.

Performance evaluation

Apart from monitoring the data, the performance of the model must be tracked vigilantly. It helps detect early performance degradation or anomalies due to changes in data patterns or issues with the model itself.

At the ground level, tracking system health, resource utilization, and infrastructure performance give a good insight into the performance of an LLM. Tracking the number of incoming requests helps in understanding the workload and usage patterns. Keeping track of token consumption helps to manage the resources efficiently and aids in cost analysis. Response time metric points towards the optimization required and ensures timely customer interaction. These metrics can be captured by standard system monitoring tools.

Using an LLM out of the box may not produce desired results, which often leads to fine tuning of LLMs based upon specific requirements of tasks to be performed by the LLM in question. Classification and Regression metrics can be used to evaluate LLMs doing numeric predictions or classification tasks. For text generating LLMs, there are certain industry accepted metrics like Perplexity, Reading Level, Non-Letter Characters, etc. which can be leveraged. One alternate approach can be to analyze the embeddings of the LLM output and look for unusual patterns. LLM outputs can also be evaluated against evaluation datasets, which allows for the comparison of textual output to a baseline of approved responses. There are also metrics to gauge the toxicity level in the LLM outputs. And last but not the least, human feedback is also one important consideration which LLM providers heavily rely upon.

There are certain evaluation frameworks which help determine the safety, accuracy, reliability, or usability issues of LLMs in various applications. Some popular evaluation frameworks are Big Bench, GLUE, Super GLUE, Open AI Evals, LAMBADA, etc. Evaluating LLMs is a complex task, and these benchmarks rely on some industry standard methods and metrics. However, we must note that these frameworks evaluate the model in terms of different set of factors, and there is no one stop solution or industry accepted benchmark yet. While evaluating LLMs, authenticity, speed, biasness, safety, readability, etc. are some key factors which come under consideration.

Balancing latency, throughput, and cost in the context of concurrent requests to an LLM is a delicate task that requires careful consideration and testing. This tradeoff often involves finding an optimal point, where increasing the load (concurrent requests) doesn’t significantly degrade latency while improving throughput, all within acceptable cost constraints. A good idea is to define an acceptable latency threshold based on the application’s requirements. For some applications, low latency is crucial (real-time interactions), while others might allow for slightly higher latency.


While deploying LLMs, we must strive to remove manual intervention and prevent human induced errors. A good approach can be the adoption of continuous delivery pipelines, which goes through following phases:

Code base management and quality checks:

When a data scientist merges changes into the training code or parameters, it triggers a series of automated quality checks (linting, tests) to ensure code integrity and consistency. The merged changes initiate a training job automatically. During this process, all model metrics and metadata are logged automatically for later analysis.

Model Registry and Validation:

A Model Registry serves as a central repository for storing and managing trained machine learning models, their associated metadata, and artifacts. It maintains different versions of models, enabling easy retrieval, comparison, and rollback to earlier versions if needed. A model being deployed must have its performance validated against predefined criteria or benchmarks. The LLM’s output must be validated for various scenarios, edge cases, or against adversarial attacks to ensure reliability in real-world use. If the model being deployed is not at par with the performance, deployment can be rolled back to the initial working model from model registry. Once the model is successfully validated, it is automatically deployed into the inference environment without further manual intervention.

LLM Infrastructure requirements

LLMs demand high computational resources, often requiring GPUs or specialized hardware, due to their complexity and resource-intensive nature. The infrastructure supporting LLM must be able to withstand the increase of workloads and concurrent requests without compromising performance. There are various model servers such as TGI, VLLM, OpenLLM, etc., each with its own set of features and configurations to optimize performance, which can be leveraged to host LLMs. GPUs can be leveraged from prominent clouds like AWS, GCP and Azure to small-scale cloud providers like Runpod, Fluidstack, Paperspace, and Coreweave.


Implementing LLMOPs in AI based systems helps in enhancing their reliability, robustness, and automation. Once these practices are integrated, deploying new models becomes a streamlined, largely automated process. Logging helps in ensuring complete traceability of the deployment process, allowing audits and analysis of every step and change made to the system. With such robust mechanisms in place, development teams can react swiftly to unforeseen behaviors or events, minimizing downtimes or issues. It also ensures the resources are allocated efficiently and gives optimal performance.



Reeshabh Choudhary

Software Architect and Developer | Author : Objects, Data & AI.