Infrastructure for AI, Machine Learning, and Private LLMs
We design, deploy, and manage computing infrastructure for model training, fine-tuning, and inference. We select the right GPUs, storage, networking, and software stack based on your workloads, security requirements, and budget.
We’ll start by assessing your workloads, data volumes, and deployment requirements.
Who It’s For
Product teams, AI solution developers, and ML and Data Science teams that require a dedicated or isolated computing environment.
Outcome
A ready-to-use infrastructure for model deployment, training, or inference, with documented configurations, monitoring, and agreed access controls.
When AI Infrastructure Is Needed
Insufficient Computing Capacity
Existing CPU and GPU servers cannot train, fine-tune, or run model inference at the required speed.
Costs Are Difficult to Control
Cloud GPU costs continue to rise, while actual resource consumption and the cost of individual workloads are difficult to predict.
Data Must Remain in a Controlled Environment
Security policies or client requirements restrict the transfer of data to external services and public cloud platforms.
Environment Setup Takes Too Long
The team has to install drivers, CUDA, container environments, and ML frameworks manually, while configuration differences make results difficult to reproduce.
Models Require Reliable Production Operations
Production AI services require monitoring, access management, backups, and regular infrastructure maintenance.
What We Provide
Architecture and Sizing
We analyse the expected workloads, model and dataset sizes, and performance requirements to determine the appropriate computing configuration.
Isolated Infrastructure
We configure network segmentation, roles, and access permissions. Data and models remain within agreed boundaries, either in the client’s infrastructure or the selected provider environment.
GPU Compute Nodes
We select servers and GPU accelerators for training, fine-tuning, or inference. Specific models and GPU quantities are determined after assessing the workload.
Preconfigured ML Environment
We install the operating system, drivers, CUDA, container environment, and required frameworks such as PyTorch or TensorFlow. Where necessary, we also prepare the environment for running open-weight models.
High-Performance Storage
We design the storage architecture around dataset volumes, loading speeds, concurrent workloads, and backup requirements.
Monitoring and Support
We monitor GPU, CPU, memory, storage, and network health and configure logging, alerts, and backup policies.
How We Build AI Infrastructure
Requirements and Resource Sizing
We identify the workload type: training from scratch, fine-tuning, inference, or a combination of use cases. We consider model size, data volumes, performance requirements, and the number of users.
Architecture Design
We select the GPU and CPU configuration, calculate memory, storage, and network requirements, determine the deployment model, and prepare an implementation plan.
Environment Deployment
We configure servers, networking, storage, access controls, and the software environment required by the team.
Load Testing
We test infrastructure stability, computing resource utilisation, data throughput, and system behaviour under the expected workload.
Handover and Support
We provide access credentials and technical documentation, enable monitoring, and agree on ongoing infrastructure support.
Deployment Options
Dedicated GPU Infrastructure
Dedicated computing resources for a specific project or team, without contention from unrelated workloads.
Private Cloud for AI
An isolated environment for models and data within an agreed security perimeter managed by the client or provider.
On-Premises Deployment
Infrastructure deployed at the client’s own facility where required by security policies or data processing requirements.
Hybrid Infrastructure
Baseline workloads run on dedicated resources, with additional capacity added for training, testing, or periods of peak demand.
Engagement
Models
Assessment and Architecture Design
We evaluate the team’s current resources and requirements, recommend an appropriate architecture, and prepare an infrastructure development plan.
Project-Based Deployment
We deploy the computing environment and configure storage, networking, access controls, the software stack, and monitoring.
Existing Infrastructure Optimisation
We analyse resource utilisation, eliminate bottlenecks, and help improve the efficiency of GPU and storage usage.
Ongoing Managed Support
We monitor infrastructure health, perform scheduled maintenance, respond to incidents, and provide regular reporting.
Let’s Build Your AI Infrastructure
Tell us about your model, data volumes, and expected workload. We will assess the requirements, recommend a suitable configuration, and prepare an infrastructure deployment plan.
Need a specialised configuration or private cloud for sensitive LLM workloads? Contact our engineers, and we will design and build an optimised server for your datasets.
We will respond within one business day.