How to Deploy a Large Language Model on Private GPU Infrastructure

What It Takes to Deploy a Large Language Model in Production

Deploying a large language model is not simply running a model on a GPU and opening an API endpoint. Production LLM deployment involves several interconnected requirements that must be designed and validated together.

Compute capacity must match the model's size and the expected request volume. Large language models range from 7 billion parameters to 70 billion and beyond, and each size class demands different GPU configurations. The model weights must fit in GPU VRAM, and the serving framework must have additional VRAM headroom for KV cache, which stores the attention state of active conversations. Underestimating VRAM requirements is one of the most common causes of deployment failures.

Inference serving software is the layer between the model and incoming requests. Production LLM serving frameworks like vLLM, NVIDIA TensorRT-LLM, and Hugging Face TGI (Text Generation Inference) are designed specifically for LLM workloads. They provide continuous batching (combining multiple requests into single GPU forward passes), PagedAttention for efficient KV cache management, and support for multi-GPU inference through tensor parallelism. Running a large language model without an optimized serving framework results in dramatically lower throughput and higher latency.

Security and compliance controls must be configured around the LLM deployment, including network segmentation, API authentication, TLS encryption, access controls, and audit logging. For enterprises in healthcare, financial services, or other regulated industries, deploying on private infrastructure ensures that all data processed by the LLM remains within the organization's security perimeter.

Choosing the Right Model to Deploy

The model you deploy determines your GPU requirements, inference performance, and the range of tasks the deployment can handle. Enterprise teams typically evaluate models across several dimensions.

Open-source models like Meta's Llama series, Mistral, and their fine-tuned variants are the most common choices for private deployment. They can be deployed on dedicated infrastructure without licensing restrictions that prevent commercial use, and their weights can be inspected, modified, and fine-tuned on enterprise data. The open-source model ecosystem has matured significantly, with models available across a range of sizes and specializations.

Model size directly determines GPU requirements. A 7B to 8B parameter model in FP16 precision requires approximately 14 to 16 GB of VRAM and can run on a single GPU. A 13B model requires approximately 26 GB of VRAM. A 70B model in FP16 requires approximately 140 GB of VRAM, which means at least two 80 GB GPUs using tensor parallelism. Quantized versions (INT8 or INT4) reduce these requirements significantly, enabling larger models to run on fewer GPUs with acceptable quality trade-offs for many use cases.

Fine-tuned domain models are base models that have been further trained on domain-specific data, such as clinical text, financial documents, or legal language. Deploying a fine-tuned model often delivers better task performance than deploying a general-purpose base model with prompt engineering alone. The GPU requirements for deploying a fine-tuned model are the same as deploying its base model, but the fine-tuning process itself requires additional GPU capacity during the training phase.

Task alignment matters for deployment efficiency. If the primary use case is classification, extraction, or routing, a smaller model (7B to 13B) may deliver sufficient quality at lower GPU cost and latency. If the use case requires complex reasoning, long-context analysis, or creative generation, a larger model (70B+) may be necessary despite higher infrastructure requirements. Deploying the smallest model that meets quality requirements is one of the most effective cost optimization strategies.

GPU Requirements for Deploying Large Language Models

GPU selection and sizing are the most consequential infrastructure decisions in LLM deployment. The right configuration depends on model size, precision, expected concurrency, and latency targets.

These figures represent model weights only. Production serving requires additional VRAM for KV cache, which grows with the number of concurrent requests and the context length of each request. A practical guideline is to plan for KV cache to consume 30 to 50 percent of available VRAM under expected production concurrency, meaning the total VRAM budget should be significantly larger than the model weight size alone.

NVIDIA H100 (80 GB HBM3) offers the highest inference throughput currently available at scale, with FP8 support that enables efficient quantized inference without the quality loss associated with INT8 or INT4 quantization. For organizations deploying 70B+ models or serving high request volumes, H100 clusters provide the most capable deployment platform.

NVIDIA A100 (40 GB or 80 GB HBM2e) remains a strong deployment option, particularly for 7B to 34B models or quantized 70B models. The 80 GB variant provides sufficient VRAM for most single-model deployments.

NVIDIA L40S (48 GB GDDR6) is cost-effective for inference-optimized deployments of 7B to 13B models, particularly when the workload does not require the HBM bandwidth of A100 or H100.

Inference Serving Frameworks for LLM Deployment

The serving framework is the software engine that processes incoming requests, manages GPU memory, and generates model outputs. Choosing and configuring the right framework significantly affects deployment throughput, latency, and GPU utilization.

vLLM is the most widely adopted open-source LLM serving framework. It introduced PagedAttention, which manages KV cache as virtual memory pages rather than contiguous blocks, dramatically reducing memory waste from fragmentation. vLLM supports continuous batching, tensor parallelism for multi-GPU inference, and a growing ecosystem of integrations. It is the default choice for many enterprise LLM deployments due to its balance of performance, community support, and ease of use.

NVIDIA TensorRT-LLM is optimized for maximum inference throughput on NVIDIA GPUs. It compiles models into optimized inference engines with kernel fusion, custom CUDA kernels, and support for FP8 precision on H100 and newer architectures. TensorRT-LLM typically delivers the highest raw throughput but requires a model compilation step and is more tightly coupled to NVIDIA's software ecosystem.

Hugging Face TGI (Text Generation Inference) provides a production-ready serving framework with built-in support for the Hugging Face model ecosystem. It offers continuous batching, tensor parallelism, and a straightforward deployment path for models hosted on Hugging Face Hub. TGI is a practical choice for teams already working within the Hugging Face ecosystem.

Ollama and similar lightweight frameworks are designed for development and small-scale deployment rather than production serving at enterprise scale. They are useful for prototyping and testing but typically lack the throughput optimization, multi-GPU support, and operational features required for production LLM deployments handling significant request volumes.

The framework choice should be validated during staging deployment using representative workloads, measuring tokens per second, time-to-first-token (TTFT), latency under concurrent load, and GPU utilization. Performance varies across models, hardware, and request patterns, so benchmarking on the target deployment configuration is essential.

The Deployment Process: Step by Step

Deploying a large language model to production follows a structured process that reduces the risk of performance issues and reliability failures.

Step 1: Define deployment requirements. Document the target use cases, expected request volume (requests per minute and concurrent users), latency requirements (time-to-first-token and total generation time), context length requirements, and any compliance or security constraints. These requirements drive every subsequent decision.

Step 2: Select the model and precision. Choose a model that meets quality requirements for the target use cases. Evaluate whether FP16, FP8, INT8, or INT4 precision provides the best balance of quality, VRAM efficiency, and throughput for the deployment hardware.

Step 3: Size the GPU infrastructure. Calculate total VRAM requirements including model weights, KV cache at expected concurrency, and serving framework overhead. Determine the GPU model and count. Plan for peak load with headroom for traffic growth.

Step 4: Configure the serving framework. Install and configure the chosen serving framework with optimized settings for the target GPU hardware and model. Configure batching parameters, tensor parallelism, memory management, and API endpoints.

Step 5: Integrate storage and RAG (if applicable). Connect the deployment to storage systems for model weights and, if using retrieval-augmented generation, to vector databases and document stores. Validate that storage access does not create latency bottlenecks.

Step 6: Configure networking and security. Set up API endpoints, load balancing, TLS encryption, authentication, and network segmentation. For multi-node deployments, configure high-speed inter-node networking for tensor parallelism communication.

Step 7: Run validation and load testing. Test the deployment under realistic request patterns, measuring throughput, latency distributions, error rates, and GPU utilization. Identify bottlenecks and optimize configuration before production launch.

Step 8: Deploy to production with monitoring. Launch the deployment with active monitoring for latency, throughput, error rates, GPU temperature, VRAM utilization, and model output quality. Configure alerting for threshold breaches and failure conditions.

Optimizing LLM Deployment Performance and Cost

Once a large language model is deployed, several optimization techniques can improve throughput, reduce latency, and lower infrastructure costs.

Quantization reduces the precision of model weights from FP16 to lower formats. FP8 quantization on H100 GPUs delivers near-FP16 quality with approximately half the VRAM requirement and higher throughput. INT8 and INT4 quantization provide further VRAM savings with varying quality trade-offs depending on the model and task. For many enterprise use cases, FP8 or INT8 quantization delivers acceptable quality with significant infrastructure savings.

Continuous batching allows the serving framework to add new requests to an ongoing batch as existing requests complete, rather than waiting for the entire batch to finish before processing new requests. This dramatically improves GPU utilization and throughput compared to static batching, particularly when request lengths vary.

KV cache optimization manages the memory consumed by attention state during generation. PagedAttention (in vLLM) reduces memory waste from fragmentation. KV cache quantization (storing the cache in FP8 or INT8 instead of FP16) reduces per-request memory consumption, enabling higher concurrent request capacity on the same GPU.

Speculative decoding uses a smaller, faster model to generate candidate tokens that the larger model verifies in a single forward pass. When the candidate tokens are correct, the larger model processes multiple tokens per forward pass, accelerating generation speed. This technique is particularly effective for tasks with predictable output patterns.

Prefix caching reuses KV cache entries for requests that share common prompt prefixes, such as system prompts or few-shot examples. This avoids recomputing attention states for the shared prefix, reducing time-to-first-token for subsequent requests.

Right-sizing deployments means matching GPU allocation to actual workload demand. Over-provisioned deployments waste GPU capacity and budget. Under-provisioned deployments create latency and queue issues. Regular monitoring of utilization metrics enables teams to adjust GPU allocation as usage patterns evolve.

Scaling LLM Deployments for Growing Enterprise Demand

As LLM usage grows across an organization, deployment scaling becomes an ongoing concern.

Horizontal scaling adds more GPU instances to handle increased request volume. This requires load balancing across instances and may require a routing layer that distributes requests based on instance availability and queue depth. On private infrastructure, horizontal scaling requires procuring and deploying additional GPU capacity, which involves lead time and capacity planning.

Request prioritization and queuing manage periods of peak demand when GPU capacity cannot serve all requests simultaneously. Priority queues ensure that latency-sensitive or high-value requests are served first, while lower-priority requests are queued or batched during peak periods.

Capacity planning involves monitoring usage trends and procuring additional GPU capacity before demand exceeds supply. Organizations that plan capacity on a quarterly cycle, informed by usage growth metrics, avoid the service degradation that occurs when deployments hit GPU limits unexpectedly.

Common Mistakes When Deploying Large Language Models

Several recurring mistakes undermine LLM deployment outcomes for enterprise teams.

Underestimating KV cache requirements. Teams often size GPU VRAM based on model weights alone and discover that KV cache at production concurrency levels consumes far more memory than expected. This leads to request failures, excessive swapping, or the need to reduce concurrent request capacity. Always plan VRAM budget for model weights plus KV cache plus serving framework overhead.

Skipping load testing before production launch. A deployment that performs well under light testing may degrade significantly under production request volumes. Load testing with realistic concurrency patterns and request lengths is essential for identifying bottlenecks before they affect users.

Running models without an optimized serving framework. Deploying an LLM with a generic inference framework or without continuous batching and PagedAttention wastes GPU capacity. The throughput difference between optimized and unoptimized serving can be 5x to 10x or more.

Ignoring model quality monitoring after deployment. Infrastructure monitoring alone is not sufficient. Model outputs can degrade due to data distribution shifts, prompt changes, or fine-tuning regressions. Production deployments should include output quality sampling and alerting alongside infrastructure metrics.

Deploying the largest available model when a smaller model meets requirements. Larger models require more GPUs, consume more power, and have higher latency. If a 13B model delivers acceptable quality for the target use case, deploying a 70B model wastes infrastructure resources without delivering proportional value.

FAQ

What does it mean to deploy a large language model?

Deploying a large language model means setting up the infrastructure and software to serve LLM inference requests in a production environment. This includes provisioning GPU hardware with sufficient VRAM, installing an optimized inference serving framework, configuring networking and security, integrating with storage and RAG systems, and establishing monitoring and operational processes.

How many GPUs are needed to deploy a large language model?

GPU requirements depend on model size and precision. A 7B to 8B parameter model can deploy on a single GPU with 40+ GB VRAM. A 70B parameter model in FP16 requires at least two 80 GB GPUs. Quantization (FP8, INT8, INT4) reduces VRAM requirements, enabling larger models on fewer GPUs. Production deployments also require VRAM headroom for KV cache at expected concurrent request volumes.

Which serving framework should I use to deploy a large language model?

The most common production serving frameworks are vLLM (widely adopted, strong ecosystem), NVIDIA TensorRT-LLM (highest throughput on NVIDIA GPUs), and Hugging Face TGI (integrated with Hugging Face model ecosystem). The choice depends on the target GPU hardware, model type, and performance requirements. Framework selection should be validated through benchmarking on the target deployment configuration.

Can I deploy a large language model on private infrastructure?

Yes. Deploying an LLM on private, dedicated GPU infrastructure gives the organization full control over the deployment environment, security configuration, and data handling. Private deployment is the preferred approach for enterprises processing sensitive data, operating under compliance requirements, or requiring consistent inference performance. OneSource Cloud provides dedicated, non-shared GPU infrastructure designed for enterprise LLM deployment.

How do I optimize LLM deployment cost?

Key cost optimization strategies include deploying the smallest model that meets quality requirements, using quantization (FP8, INT8) to reduce VRAM and increase throughput, implementing continuous batching and KV cache optimization to maximize GPU utilization, and monitoring usage patterns to right-size GPU allocation. Managed infrastructure services can also reduce operational costs by providing specialized expertise without requiring the enterprise to build and maintain an internal GPU operations team.

What is the difference between deploying an LLM and using a public LLM API?

Using a public LLM API (such as OpenAI, Anthropic, or Google) sends prompts to a third-party endpoint and receives responses. Deploying an LLM means running the model on infrastructure the organization controls, with full authority over data handling, model configuration, security, and performance. Private deployment is preferred for sensitive data, compliance-regulated workloads, high-volume usage where API costs become prohibitive, and applications requiring consistent latency and availability guarantees.

Can I deploy open-source LLMs like Llama or Mistral for commercial use?

Yes. Models like Meta's Llama series and Mistral are available under licenses that permit commercial deployment. Organizations can fine-tune these models on their own data and deploy them on private GPU infrastructure. The open-source model ecosystem provides options across a range of sizes and specializations suitable for enterprise use cases.

How does OneSource Cloud support large language model deployment?

summary

Deploying a large language model is a systems engineering task that requires coordinated planning across GPU hardware, inference serving software, storage, networking, and operational management. The decisions made at each stage, from model selection and precision choice to GPU sizing and serving framework configuration, directly affect deployment performance, cost, and reliability.

Enterprise teams that approach LLM deployment with a structured process, validate configurations through load testing, and invest in inference optimization achieve significantly better outcomes than teams that treat deployment as a simple container launch. The most effective deployments match model capability to task requirements, right-size GPU infrastructure to actual demand, and maintain ongoing operational processes for monitoring, updates, and scaling.