GPU-accelerated AI inference — on your infrastructure
NVIDIA NIM microservices for optimized LLM, vision and speech inference. AI Enterprise for production AI on-premise, cloud or edge — with the performance that GPU infrastructure enables.
GPU infrastructure is expensive. The cost of underutilizing it — or configuring it incorrectly — is equally expensive. NVIDIA NIM microservices deliver containerized, optimized inference endpoints that extract maximum performance from your GPU hardware without custom CUDA engineering. TensorRT-LLM optimization, automatic batching and continuous batching are built-in — you get production-grade throughput from the first deployment.
The strategic case for NVIDIA AI Stack is data sovereignty. Not every workload can or should go to a cloud API. Healthcare records, financial models, legal documents — these require infrastructure you control entirely. NIM microservices bring the same model quality as cloud APIs to your on-premise servers, air-gapped environments and edge nodes.
What is NVIDIA NIM?
NVIDIA NIM (NVIDIA Inference Microservices) are optimized, containerized inference packages that make it straightforward to deploy state-of-the-art AI models — LLMs, vision models, speech models — on any NVIDIA GPU infrastructure. Each NIM includes a TensorRT-LLM optimized engine, an OpenAI-compatible API, automatic GPU utilization management and performance profiling. NIM is part of NVIDIA AI Enterprise, the end-to-end platform for AI development and deployment that includes NeMo for model customization and lifecycle management. In Adoredev we deploy NIM for clients who need GPU-accelerated inference with data residency requirements or latency targets that cloud APIs cannot meet.
Our capabilities
GPU-accelerated AI infrastructure deployments
NIM Microservices
LLM · Vision · SpeechDeployment of NVIDIA NIM containers for LLM inference (Llama, Mistral, Gemma), vision models and speech-to-text on your GPU fleet.
Learn moreTensorRT-LLM Optimization
Performance EngineeringModel quantization, attention optimization and continuous batching configuration for maximum throughput on your specific GPU hardware.
Learn moreAI Enterprise
On-Premise · Cloud · EdgeNVIDIA AI Enterprise deployment and management — from single-node on-premise to multi-region cloud to edge inference nodes.
Learn moreNeMo Customization
Fine-Tuning · LoRAModel customization with NeMo: supervised fine-tuning, LoRA adapters and RLHF for domain-specific model optimization.
Learn moreEdge Deployment
Jetson · Edge AIAI inference at the edge with NVIDIA Jetson — for computer vision, speech processing and autonomous systems with low-latency requirements.
Learn moreInfrastructure & Monitoring
GPU OpsGPU cluster management, utilization monitoring, cost-per-inference dashboards and auto-scaling configuration for AI workloads.
Learn moreCloud API vs. On-Premise GPU
Cloud AI APIs have an unbeatable advantage for variable workloads: zero infrastructure overhead, instant global scale and pay-per-token billing. But the calculation changes at scale and in regulated industries. At 10M inferences per month, on-premise GPU infrastructure is typically 60–80% cheaper than cloud API billing. And for healthcare, finance or defense workloads — where data cannot leave your perimeter — on-premise is not a cost optimization, it is a compliance requirement.
Good fit
- Organizations with existing NVIDIA GPU hardware to utilize
- Industries with strict data residency requirements (Healthcare, Finance, Defense)
- High-volume inference workloads where cloud API costs are unsustainable
- Teams needing consistent sub-100ms latency that cloud APIs cannot guarantee
Not a good fit
- Startups without GPU infrastructure or budget to acquire it
- Variable-demand workloads where managed cloud services are clearly cheaper
- Teams without DevOps capacity to manage containerized GPU infrastructure
- Projects where data sovereignty is not a requirement
Our NVIDIA AI deployment methodology
From GPU audit to production NIM deployment
Infrastructure Audit
Assessment of your existing GPU hardware, networking and storage — or specification of hardware requirements if you are starting from scratch.
NIM Configuration
Container deployment and TensorRT-LLM optimization for your target models and GPU architecture. Benchmarked against your latency and throughput targets.
Integration Build
OpenAI-compatible API layer, load balancing across GPU nodes and integration with your existing application stack.
Operations & Monitoring
GPU utilization dashboards, cost-per-inference tracking, alert configuration and capacity planning documentation.
Frequently asked questions
Common questions about NVIDIA NIM and AI Enterprise deployments
NIM microservices run on any NVIDIA data center GPU: A10G, A100, H100, or H200 for production workloads; RTX 4090 or A6000 for smaller or dev environments. The minimum depends on the model: Llama 3 8B fits on a single A10G (24GB VRAM); Llama 3 70B requires 4× A100 or 2× H100. We spec the hardware requirements as part of the architecture review.
NIM is not a generic container — it is a model-specific optimized package. It includes TensorRT-LLM compilation for your GPU architecture (INT8/INT4 quantization with accuracy validation), continuous batching for maximum throughput, automatic tensor parallelism for multi-GPU configurations, and a production-tested API layer. Running a model naively in Docker gives you a working endpoint; NIM gives you a production-grade inference server.
Yes, via NeMo. NVIDIA NeMo supports supervised fine-tuning, LoRA adapter training and RLHF for domain-specific model adaptation. The resulting model can be packaged as a custom NIM for deployment. We manage the full fine-tuning pipeline — data preparation, training runs, evaluation and NIM packaging.
NIM microservices are available in two tiers: a free developer tier from build.nvidia.com, and the enterprise tier included in NVIDIA AI Enterprise (which adds SLA guarantees, security support and NeMo access). For production deployments we recommend AI Enterprise. For evaluation or low-volume internal use, the developer tier is viable.
Yes. NIM exposes an OpenAI-compatible API, so any application that calls cloud AI APIs can be pointed at your NIM endpoint with minimal code changes. You can run NIM on-premise for sensitive workloads and AWS Bedrock for variable or public-facing workloads — routing between them based on data classification. We have deployed hybrid architectures of this type.
GPU infrastructure assessment — free.
We evaluate your hardware and workload requirements and recommend the right NIM deployment architecture.
We assess your GPU infrastructure and inference requirements and design a NIM deployment with throughput benchmarks and cost projections.
Talk to an architect