CUDA
Accelerated computeWe plan to use NVIDIA CUDA for GPU-accelerated computation supporting machine-learning and AI workloads.
COMPUTE FOUNDATIONTHE PLANNED TECHNOLOGY STACK
AI at the application layer.
Acceleration underneath.
We plan to use NVIDIA CUDA, NVIDIA NIM, TensorRT-LLM and Triton Inference Server to support model inference, embeddings, RAG workloads and real-time automation.
NIM
Model deploymentNVIDIA NIM is part of our planned approach to packaging and deploying accelerated inference microservices.
INFERENCE MICROSERVICESTensorRT-LLM
Efficient LLM inferenceWe plan to evaluate TensorRT-LLM for optimizing supported language models and making inference more efficient.
LLM OPTIMIZATIONTriton
Inference orchestrationTriton Inference Server is planned for serving models and coordinating inference workloads across the platform.
MODEL SERVINGPlanned technology use—not a statement of deployed infrastructure, NVIDIA partnership, endorsement or program membership. NVIDIA product names belong to their respective owners.
WORKLOADS WITH PURPOSE
Infrastructure follows
the work.
The architecture will be evaluated against actual product workloads, model requirements and operational constraints. No unverified latency or throughput claims.
01
Model inference
Running language models to support contextual responses and reasoning.
02
Embedding generation
Representing relevant company information for retrieval.
03
RAG workloads
Retrieving and using source context in generated responses.
04
AI automation
Supporting agent-assisted process steps and operational coordination.
BUILD WITH INTENTION
Your operations.
A more intelligent next chapter.
Tell us where work gets stuck. Help shape what comes next.