LLM Deployment: How to Deploy Large Language Models at Scale in Production

There’s a common misconception between generative AI vs. LLM. While GenAI focuses on generating text, code, audio, image, video and music, LLM is a subcategory of GenAI focused solely on human text and language.

Tools like Claude by Anthropic or GPT Series by Open AI serve as general-purpose LLM models and lack customization. This is where LLM deployment steps in.

Read on to discover how to deploy LLMs at scale in production.

The five core areas of LLM deployment

LLM deployment is not a plug-and-play solution. It includes packaging LLM into a container, giving it robust hardware, establishing a communication bridge, adapting it to your demands and securing it against threats.

Model containerization for portable LLM deployment

The first step is containerization. It’s like moving to a rented room in a block of flats. Infrastructure (like heating or water) is already there.

The same goes for LLMs. Into the container go:

• LLM’s core inference engine (e.g., vLLM)
• Dependencies (libraries that enable the code to run, e.g., CUDA runtime libraries that unlock the GPU’s power)
• API server code (which handles your questions via APIs and sends back the model’s answers)

Infrastructure like host operating system and physical GPU drivers are already there. Thanks to containerization, LLMs can run anywhere – on cloud, on premise or edge devices.

Containerization can be done with tools like Docker, Kubernetes, Azure Container Apps or IBM Cloud Kubernetes Service.

GPU allocation and hardware requirements for LLMs

Once the containers are ready, it’s time to allocate the graphics processing unit (GPU), which will make calculations.

GPU is like an army of small mathematicians. It determines how fast the model will compute your prompt and provide the reply.

But speed is only the tip of the iceberg. Memory is equally critical. To be more precise, Video Random Access Memory (VRAM). A GPU with too little VRAM does not run the model slowly – it does not run it at all.

Simply put, GPU allocation is the process of distributing:

• model weights: numerical parameters that decide how input turns into output. They must be loaded into VRAM before a model can run.
• activations: temporary working memory of the LLM’s model.
• KV cache: smart memory that can consume as much VRAM as the model itself.

But how to do it properly? With single GPU, load the model if VRAM fits. If model needs more than 85% of your single GPU’s RAM, you should think about implementing multiple GPUs or quantizing the model.

Quantizing means trying to make the giant model smaller, cheaper and sometimes faster by storing their numbers with fewer bytes. You can do it e.g., with llama.cpp tool. For instance, a 70B model in 4-bit fits into ~40 GB.

Creating production-ready API endpoints for LLMs

API endpoint is a digital location that accepts all API requests. In the ecosystem of generative AI for software development, creating production-ready API endpoints for LLMs is critical.

Start with a core engine like vLLM or Text Generation Inference (TGI). Remember that API contracts are non-negotiable. They define what to send, what to receive and how to handle errors.

API endpoints need to be secured as well. Wrap them with a custom FastAPI layer for business logic and robust security measures. Above all, authentication helps to decipher who has access. API keys or JWT tokens will help you with it. Its important to think about HTTPS encryption, as well.

Observability is another layer of production-ready APIs. It includes monitoring, logging, and alerting – so you can catch issues before users notice them.

Autoscaling strategies for LLM workloads

Autoscaling dynamically adjusts capacity based on incoming user traffic. The aim is simple: to make sure you have enough capacity without manually adjusting GPUs, burning tokens and skyrocketing costs.

But traffic flow in LLM applications is unpredictable because you never know if you get a crisp one sentence output or a dense 10-page technical report and if a model won’t trigger some actions in the background (like searching a website or calling other agents).

That is why autoscaling for LLM workloads is a must have. To auto scale LLMs effectively, track two things: queue size (number of prompts waiting in the queue) and batch size (processed prompts). But above all, make sure you have enough VRAM per every model replica (copy of the LLM’s model).

Autoscaling strategies can be divided into three categories: what to add, scaling timing and how much you scale.

What to add:

• Vertical scaling (scale-up): Adding more power: GPUs, VRAM, faster memory bandwidth, higher system RAM. Use it when your model is too big, too slow or when you must improve latency per request.
• Horizontal scaling (scale-out): Adding more replicas. Use it when vertical scaling didn’t improve the LLM’s performance or when you handle massive simultaneous requests.

Scaling time:

• Reactive scaling: Adding or removing power after detecting a traffic spike or performance change. Use it for unpredictable traffic spikes.
• Predictive scaling: Predicting performance before the model goes down. Use it for predictable traffic patterns.

How much you scale:

• Threshold-based scaling: When you exceed the capacity (e.g., user latency, GPU usage, etc.), the system adds or removes copies of the LLM model. Use it for simple and steady traffic.
• Step scaling: The system adjusts your capacity in predefined steps based on real-time demand. Use it for complex and variable traffic.

These strategies can be implemented together to improve the LLM’s performance.

LLM deployment security: OWASP Top 10 for LLMs

Once an LLM model is alive and kicking, we need to secure it.

Security guardrails define how LLM interacts with users, data and tools. They need to be implemented at every layer from input and data access, through runtime and identity management, all the way to the final output.

The OWASP Top 10 for LLMs is the foundational checklist for securing LLM applications:

1. Prompt injection: It means feeding the LLM model with a prompt, image, audio or video, which results in undesired LLM behavior change.
2. Sensitive information disclosure: Exposing credentials, API keys, trade secrets or financial/proprietary data through a channel unauthorized by the user. It can happen, e.g., when oversharing too much data to the LLM.
3. Excessive agency: It happens when you give an AI too much power, too many tools or too much freedom to act on its own.
4. Supply chain: It occurs when a weakness is introduced through third-party elements (e.g., external APIs, cloud infrastructure or pre-trained models with security flaws).
5. Data and model poisoning: It can occur at any stage of the LLM lifecycle (like pre-training or fine-tuning). The model works as usual, but it can quietly alter model behavior or inject malicious code.
6. Unbounded consumption: It occurs when the LLM allows excessive usage. It can result in additional costs, service unavailability or stealing the model by the attacker.
7. Misinformation: LLM model produces incorrect or misleading information. The trick is that they sound credible. When the user acts upon them, hallucinations spread like wildfire.
8. Hidden context exposure: An attacker tricks your LLM into revealing its secrets, policy logic or trust boundaries that run behind the scenes. The hidden content is not visible to the user, but it is accessible to the model.
9. Vector and embedding weakness: An attacker creates a legitimate-looking website and injects malicious instructions for the LLM model inside this website. As a result, the model does what the attacker wants.
10. Improper output handling: It happens when an LLM produces a response, but the system doesn’t properly check, clean or validate that response before passing it down to other components or systems.

To master how to protect yourself against those threats, refer to OWASP Top 10 Security Vulnerabilities.

Self-hosted vs. managed LLM deployment: how to decide

A self-hosted LLM is under full supervision of your organization. But the trade-off is that you must manage several infrastructure components yourself. This includes GPUs, setting up model infrastructure, auto-scaling and monitoring.

A managed LLM is like a subscription service. It lets your application own the user experience, business logic, security controls and orchestration layer while the third-party provider runs the module.

But the question is, when to use which?

Choose self-hosted LLM when you:

• Operate in a strictly regulated environment (e.g., healthcare, finance or government)
• Have high-volume, stable traffic
• Have strong platform engineering and MLOps capabilities
• Need low latency
• Plan to fine-tune and scale on proprietary data

Choose managed LLM when you:

• Must launch LLM quickly and demand top-tier quality
• Have unpredictable traffic
• Have no dedicated MLOps team
• Can accept moderate latency
• Can accept general-purpose models and don’t need deep customization

In practice, a lot of organizations adopt a hybrid approach. They begin with managed LLM but as the system grows, some workloads gravitate towards self-hosted models for better cost efficiency and system control.

To learn more about deploying lagre language models safely and effectively, reach out to our team.

FAQ

What hardware do I need to deploy an LLM in production?

To deploy LLM in production, you need 4 – 6 GB VRAM for small models to 16 – 24 GB VRAM for large models. You will also need a CPU, system RAM, fast SSD storage (e.g. PCIe Gen4 or Gen5 NVMe SSDs) for model loading and stable network bandwidth for API traffic.

How do I create a production – ready API endpoint for an LLM?

Start with a core serving engine like vLLM or TGI. Then wrap it with a custom FastAPI layer for authentication (API keys or JWT), rate limiting, request validation and observability (logging, metrics, traces).

How do you autoscale LLM deployments to handle traffic spikes?

Monitor queue size and GPU utilization. Use reactive scaling for unexpected spikes and predictive scaling for known traffic patterns.

What are the main security risks in LLM deployments?

The main security risks in LLM deployments include prompt injection, sensitive information disclosure and model poisoning.

Should I self-host an LLM or use a managed cloud service?

It depends on your KPIs. Select self-hosted LLM for control and scaling capabilities. Select managed LLM if you must launch fast, want flexibility and no infrastructure headaches.