DZ GaaS
GPU-as-a-Service for AI/ML Workload Optimization
More than 80% of the companies invest in AI infrastructure that leads to inefficiencies and capitalization, and GPU-as-a-Service from digi edZe eliminates the upfront investment through on-demand access to high-performance computing resources.
GPU-as-a-Service Overview
Designed to run mission-critical workloads with high-end performance, GPU-as-a-Service complements computing instances the modern cloud environments demand through dynamically, intelligently, and seamlessly. Your access to the most advanced technologies without upfront costs begins here.
Scale computational resources in real-time without compromising on the highest performance
Prioritize your roadmaps for next innovation while we take care of operational responsibilities
Enable business continuity and predictable operational costs without infrastructure overhead
Onboard high GPU computing resources from AMD and NVIDIA for your AI/ML-driven workloads
Gain the maximum business value from the on-demand GPUs where operational efficiency is promised
Transformation at Speed, Powered by dZ GaaS
Enterprise AI demands capital expenditure and opportunity cost. Very few GPU hardware keeps the teams in the queue for access and takes longer runs for workloads, resulting in operational inefficiencies. Embrace dZ Gaas as we bring the same resource pooling and dynamic allocation model that transforms cloud infrastructure into a shared GPU compute service. It encompasses the enterprises with fractional allocation, intelligent scheduling, automatic reclamation, and hybrid orchestration.
AI and ML Prowess
Train LLMs, process imaging data, develop autonomous algorithms, and run real-time recommendation engines. Integrate deep learning, NLP, and predictive analytics with AI and ML offered by GPUs for the modern environments.
Simulation and Analysis
Run complex simulations, digital twins, and high-performance analytics with unmatched speed and precision. From financial risk modeling to scientific research and engineering workloads, leverage GPU acceleration to unlock deeper insights. Enable faster decision-making by compressing compute-intensive processes from days into hours.
Scalable Resources
Dynamically scale GPU capacity up or down based on workload intensity without upfront investments. Eliminate bottlenecks with on-demand provisioning that aligns compute power with business priorities. Ensure seamless performance continuity during peak demands while optimizing cost efficiency across environments.
Accelerate Enterprise Transformation
Drive innovation at pace by removing infrastructure constraints and enabling rapid experimentation. Empower teams to build, test, and deploy AI-driven solutions faster, reducing time-to-market significantly. Transform enterprise agility by integrating GPU-powered intelligence into every layer of your digital ecosystem.
Key Capabilities of dZ GPU-as-Service
From seamless provisioning and multi-tenant resource pooling to policy-driven governance and real-time performance monitoring, the platform is designed to align compute power with evolving business needs. With built-in automation, hybrid cloud interoperability, and secure access controls, dZ GaaS enables organizations to operationalize AI, accelerate innovation, and scale with confidence.
Allocate GPU resources with precision right from fractional units (as low as 0.25 GPU) to large-scale clusters. This ensures full isolation without over-provisioning. dZ GaaS eliminates capacity waste by enabling multiple workloads to securely share a single GPU, maximizing utilization. This drives a significant shift from underutilized infrastructure to high-efficiency compute, delivering exponentially greater value per GPU. With dynamic resizing, workloads adapt in real time, minimizing idle capacity and aligning compute usage with actual demand.
dZ GaaS scheduler understands the difference between a production inference API serving 10,000 users and an experimental training job that a researcher started in an afternoon. Production workloads receive guaranteed priority. Training runs on reserved capacity are protected from preemption at critical checkpoints. Experimental workloads yield to higher-priority work automatically. The entire GPU portfolio delivers maximum business value for the workload that happened to start first.
Monitor GPU utilization across every allocated workload continuously, identifying genuine idle resources and automatically reclaiming them for reallocation, The intelligent reclamation is where a GPU waiting for an imminent scheduled job is retained, while one that has been inactive beyond its threshold is returned to the pool immediately. Reclaimed capacity routes instantly to the highest-priority queued workload, ensuring the cluster is always working as hard as the demand requires.
On-premise and cloud GPU capacity unified into a single pool. Workloads route to on-premise hardware when capacity is available for lowest cost. They burst to cloud automatically when on-premise saturates. Batch jobs chase spot pricing across cloud regions. Regulated workloads enforce data residency requirements automatically. Every routing decision optimises the specific constraints of the individual workload without requiring manual configuration or human intervention.
GPU costs tracked per training run, inference request, model, team, and per AI initiative. Finance teams see the projects that are driving GPU spend. Teams see their consumption in real time and develop cost awareness naturally. Budget caps prevent runaway spending without blocking the critical workloads that genuinely justify burst expenditure. Every GPU dollar is attributable to a specific business decision.
Transformer training benefits from different memory architectures than real-time inference. dZ GaaS understands workload types and routes each to the GPU architecture that optimizes its specific performance and cost profile — A100s for large model training, T4s for cost-efficient inference, H100s for the workloads that justify premium compute. Matching improves both performance and cost efficiency simultaneously.
dZ GaaS learns from historical GPU usage patterns to forecast future demand, helping infrastructure teams make informed decisions about GPU procurement, cloud commitment levels, and capacity planning. Training cycles that recur monthly are detected and predicted. Product launches that historically drive inference demand spikes are flagged. Procurement decisions are made with data rather than intuition, reducing both over-investment and under-provisioning.
ML and AI leaders see GPU economics at the level that supports strategic decisions: cost per model, cost per experiment, efficiency trends across teams, and ROI from specific AI initiatives. Infrastructure teams see utilisation, queue depths, and cluster health. Finance teams see budget attribution and variance. Every audience receives the GPU intelligence that enables their specific decisions rather than a single dashboard view.
Business Impact with GPU-as-a-Service Platform
With the access to industry-leading GPUs designed to scale workloads by offering deep learning and GenAI to computing and model inference, our GaaS platform delivers what enterprise transformation demands the most – AI powerfulness, advanced capabilities, and innovative approaches without any infrastructure complexities.
Up to 40% reduction in Opex
Rapid deployments and faster time-to-cloud
Improved application performance and uptime
Built-in compliance and security controls
Future-ready architecture for AI/GenAI adoption
Continuous support for cloud optimization
Extreme innovation, exceptional business impact, and scalable transformation
4x-5x Efficient AI Compute Capacity increase from the same hardware investment
75%-85% GPU Utilization with intelligent scheduling and fractional allocation
7x AI Compute Capacity Multiplier without spending a dollar on additional hardware
Real Time On-Demand Access with dynamic allocation and hybrid burst capability
30%-40% Cut in AI Costs through greater visibility and efficiency
