Generative AI
Compute for model development, fine-tuning and inference for generative applications.
GPU as a ServiceGPU as a Service Accelerated compute.
GPU as a Service Accelerated compute.
Ready when you are.
Access GPU computing for AI, machine learning, analytics, HPC, simulation and graphics-intensive workloads without building dedicated GPU infrastructure.
ACCELERATED COMPUTINGCompute built for
Compute built for
demanding workloads.
Modern AI and computational workloads need considerably more processing power than conventional infrastructure can provide efficiently.
GPU as a Service gives organisations access to accelerated computing resources when required, helping teams develop and run computationally intensive workloads without owning every component of specialised infrastructure.
Accelerated infrastructure for AI, HPC, analytics, simulation and graphics-intensive workloads.
BUILT FOR MODERN COMPUTEOne platform.
One platform.
Multiple possibilities.
Machine Learning
Accelerate training and analysis for data-intensive models.
Scientific Computing
Support parallel processing for research and analytical workloads.
Engineering Simulation
Run computational models and demanding simulation environments.
Visualisation
Provide accelerated processing for rendering and graphics-intensive work.
Data Processing
Use GPU computing for parallel data workloads and analytics.
WHY GPU AS A SERVICEPowerful compute
Powerful compute
without owning
the hardware.
01
On-demand Capacity
Provision accelerated compute as workload requirements change.
02
Reduced Hardware Investment
Access GPU resources without purchasing dedicated hardware for every requirement.
03
Faster Deployment
Reduce the setup time for accelerated computing environments.
04
Flexible Scaling
Adjust capacity as development and production requirements evolve.
05
Access to Specialised Compute
Match GPU resources to AI, HPC, simulation and visualisation workloads.
GPU INFRASTRUCTUREBuilt around
Built around
your workload.
GPU Compute
Accelerated computing resources for demanding workloads.
AI and ML Workloads
Infrastructure for machine learning and intelligent applications.
Model Training
Parallel compute for developing and refining models.
AI Inference
Accelerated processing for running models in applications.
High-Performance Computing
Parallel processing for scientific and engineering workloads.
Simulation
Compute for modelling and scenario-based workloads.
Rendering
Graphics processing for visualisation and media work.
Flexible Provisioning
Deploy GPU resources according to workload requirements.
HOW GPU AS A SERVICE WORKSFrom workload
From workload
to accelerated
compute.
A considered route from the requirement to a right-sized compute environment.
01
Define the Workload
Understand the application, model, dataset and performance requirements.
02
Select GPU Resources
Match computing capacity to training, inference, HPC, rendering or other workload requirements.
03
Provision the Environment
Deploy GPU-enabled compute with the required environment and supporting software.
04
Run the Workload
Train models, execute simulations, process data or operate GPU-intensive applications.
05
Scale as Required
Increase or reduce resources as workload requirements change.
06
Monitor & Optimise
Track utilisation, performance and capacity to improve infrastructure efficiency.
ACCELERATED ECOSYSTEMBuilt for modern AI
Built for modern AI
and compute workflows.
GPU environments can support widely adopted accelerated-computing frameworks and development ecosystems.
These are common technologies in the wider ecosystem. Specific compatibility and support depend on the environment and should be confirmed for each project.
CUDAPyTorchTensorFlowJAXContainersKubernetes
WHAT CAN YOU BUILD?Compute for ideas
Compute for ideas
that demand more.
Generative AI
Large language models, multimodal AI, fine-tuning and inference.
Machine Learning
Training, prediction and advanced analytics.
Scientific Computing
Parallel analysis and research workloads.
Engineering Simulation
Modelling, simulation and computational design.
Visualisation
3D rendering and graphics-intensive applications.
Data Processing
Parallel data analysis and processing pipelines.
CONNECTED CAPABILITIESGPU infrastructure is only
GPU infrastructure is only
part of the equation.
Combine accelerated computing with Taeknizon's AI, cloud, security and infrastructure capabilities to build complete technology environments.
GPU AS A SERVICE / GOOD TO KNOWQuestions,
Questions,
answered.
What is GPU as a Service?
GPU as a Service provides access to GPU-accelerated computing through a cloud or managed infrastructure environment, allowing organisations to run compute-intensive workloads without owning and managing dedicated GPU hardware.
What workloads can GPU as a Service support?
GPU infrastructure can support AI model training and inference, high-performance computing, scientific simulation, rendering, visualisation and other parallel-compute workloads.
Why use GPU as a Service instead of purchasing GPU servers?
GPUaaS can reduce the need for upfront hardware investment and allows organisations to provision accelerated computing according to workload requirements.
Can GPU as a Service be used for generative AI?
Yes. GPU infrastructure can support model training, fine-tuning and inference for generative AI workloads.
ACCELERATE WHAT'S NEXTPut accelerated compute
Put accelerated compute
behind your next big idea.
Whether you are training an AI model, running complex simulations or building GPU-intensive applications, access the compute your workload needs without building everything from the ground up.