Kala Data

Artificial intelligence

Workloads built for the full AI lifecycle.

High-performance GPU infrastructure built for the workloads that need it most. From frontier-model training to production inference, geospatial analytics to agent development — if it needs GPUs, it runs on Kala.

Launch compute → View GPU pricing

Workload category

Language & generative AI

Train, fine-tune and serve large language models on infrastructure built for sustained, high-intensity compute. Kala's B200 and H200 GPUs provide the high-bandwidth memory and interconnect capacity that frontier-scale training demands, while our on-demand tier lets smaller teams run LoRA and QLoRA fine-tunes at transparent hourly rates without upfront commitment.

For production inference, our metro GPU nodes place capacity close to your end users, reducing token latency and keeping throughput high even under bursty load. One API, one bill — whether you're running a small open-weight model or routing traffic across a multi-GPU serving cluster.

TYPICAL WORKLOADS
  • LLM pre-training (multi-node, large-scale)
  • Fine-tuning with LoRA, QLoRA, and full fine-tune
  • Domain adaptation and instruction tuning
  • Text-to-image and video generation pipelines
  • Conversational AI and LLM API serving
  • Embedding models and semantic retrieval services
  • Speech recognition, synthesis, and translation
  • Multimodal models (vision-language, audio-language)

Workload category

Vision & geospatial

Computer vision workloads — object detection, segmentation, change detection, and 3D reconstruction — are routinely bottlenecked by the cost and availability of compute, not by algorithm quality. Processing satellite, aerial, and drone imagery at meaningful scale requires handling terabyte-sized datasets efficiently, which makes abundant low-cost GPU time far more useful than expensive, rationed cloud capacity.

Kala's energy-site infrastructure is purpose-built for exactly this trade-off: sustained high-throughput jobs that don't need to be close to users, running on hardware that doesn't meter ingress and egress at punishing rates. Industries running on Kala include agriculture, mining, infrastructure monitoring, environmental mapping, and autonomous systems development.

TYPICAL WORKLOADS
  • Satellite and aerial imagery processing at scale
  • Object detection, segmentation, and change detection
  • Agriculture analytics (crop health, yield estimation)
  • Mining and resource exploration mapping
  • Infrastructure monitoring and defect detection
  • 3D reconstruction and point cloud processing
  • Autonomous vehicle and robotics perception pipelines
  • Video analytics and real-time scene understanding

Workload category

Science & simulation

Scientific and engineering simulation workloads are among the most demanding GPU consumers in existence — molecular dynamics runs, climate models, and computational fluid dynamics simulations regularly occupy hundreds of GPUs for days at a time. These workloads were historically limited to large institutional clusters with long allocation queues, putting modern GPU infrastructure out of reach for most research teams.

Kala provides research-grade B200 and H200 capacity at hourly rates with no allocation process, so teams can spin up the compute they need on their own schedule. The absence of egress fees is particularly meaningful for HPC workloads, which routinely move large intermediate result sets between jobs.

TYPICAL WORKLOADS
  • Molecular dynamics and protein folding
  • Drug discovery and computational chemistry
  • Climate and weather simulation
  • Computational fluid dynamics (CFD)
  • Finite element analysis and structural simulation
  • Particle physics event generation
  • Quantum chemistry and materials science
  • Genomics pipelines and bioinformatics

Workload category

Media & rendering

VFX and 3D rendering have always been GPU-intensive, but AI-accelerated rendering, denoising, neural upscaling, and video generation have dramatically raised the compute floor. Studios with unpredictable project schedules — deadline crunches followed by quiet periods — are poorly served by fixed hardware ownership and poorly priced by premium cloud rendering.

Kala's on-demand capacity lets studios scale to hundreds of GPUs for a crunch and release them the moment the job is done. The same platform that handles bulk offline rendering can serve real-time ray tracing and AI-generated video, without separate accounts or separate billing.

TYPICAL WORKLOADS
  • VFX compositing and 3D scene rendering
  • AI-assisted video production and editing
  • Neural denoising and upscaling (DLSS-class)
  • Real-time ray tracing and path tracing
  • Style transfer and video-to-video generation
  • Architectural and product visualisation
  • Game asset generation and texture synthesis
  • Animation pipeline processing at scale

Workload category

Financial modelling

Quantitative finance workloads are doubly sensitive: they require both consistent computational performance and predictable costs. Monte Carlo simulations for risk, derivatives pricing models, and backtesting suites can require thousands of GPU-hours per run, and cloud billing variability compounds the forecasting problem for teams that already spend their days managing exposure.

Kala's reserved capacity tier is well-suited to quant teams: guaranteed availability, consistent performance without shared-tenancy contention, and a known cost structure. Fraud detection and credit scoring models that need regular retraining benefit from the same predictability — plus egress-free data movement for the large datasets these pipelines consume.

TYPICAL WORKLOADS
  • Monte Carlo risk simulation and stress testing
  • Derivatives pricing and portfolio optimisation
  • Strategy backtesting and parameter search
  • Fraud detection and anomaly model training
  • Credit scoring and underwriting models
  • High-frequency data feature engineering
  • Regulatory capital calculation (XVA, CVA)
  • Alternative data processing at scale

Workload category

Agents & automation

AI agent workflows differ from single-model inference in a crucial way: they make many concurrent model calls in sequence or in parallel — planning, tool use, retrieval, evaluation, and reflection loops — each of which requires a fast, available inference backend. Latency and throughput compound across multi-step pipelines, and an agent system is only as fast as its slowest model call.

Kala's mesh infrastructure provides the throughput and low inter-GPU latency that multi-agent systems need, from development and red-teaming environments through to production deployment. The same platform scales from single-developer experimentation to the high-concurrency serving layer behind an enterprise automation product.

TYPICAL WORKLOADS
  • LLM agent orchestration and planning systems
  • Multi-agent simulation and evaluation environments
  • Retrieval-augmented generation (RAG) pipelines
  • Tool-use and function-calling inference serving
  • Agent red-teaming and safety evaluation
  • Robotic process automation with AI decision layers
  • AI-driven data processing and transformation pipelines
  • Synthetic data generation for training and evaluation