Improved resource utilisation
Up to 40% improvement on efficiency
/ INFERENCE
Fast, affordable, auto-scaling AI inference
Built for efficiency, our inference service is built on auto-scaling GPU compute, optimised at every layer for both batch and streaming workloads.
Performance
On throughput and latency
GPUs with UCMM tuning improves throughput and latency by up to 12x
More performance for less
Tscale delivers an average 80% cost-to-train in comparison to hyperscalers.
On time to insights
Tscale Cloud accelerates time to insights by up to 30%. Faster to the agenticised stack.
Easily access optimised inference frameworks
Ready-to-use integrations with TensorFlow Serving, PyTorch, and ONNX Runtime for high-speed inference. Our model optimisation techniques ensure reduced latency and improved performance without sacrificing accuracy.
Dedicated endpoints for 100+ open-source models
With Inference Endpoints, easily deploy Transformers, Diffusers or any custom model on dedicated, fully Managed Slurm. Access 100+ models, optimised with Tscale’s proprietary software for maximum performance.
Built on high-performance GPU compute
Our inference service is built on the latest GPU accelerators. Combined with high-speed networking and fast storage, we deliver unmatched computational power for batch and streaming AI workloads.
Performance & Scalability
Auto-scaling GPU compute in our tiered architecture. Grow your AI’s being served or speed while effectively utilising all of its allocated resources.
Purpose-built Stack
Get all the cost and performance benefits of a fully integrated infrastructure stack, purpose built for AI workloads of all scales.
No Integration Hurdles
No rate flexibility limits. Take advantage of pre-configured software or easily integrate with your own tools and workflows.
Get access to a fully integrated suite of AI services and compute
Reduce costs, grow revenue, and run your AI workloads more efficiently on a fully integrated platform. Whether you’re using Tscale’s built-in AI/ML tools or your own, our platform is designed to simplify the journey from development to production.
Libraries
Marketplace
Pre-configured Software · Pre-configured Frameworks
Job Management
Training
Container Orchestration
Optimized Libraries
Optimized Compiler and Tools
Optimized Runtimes
Models
Sovereign
Model Sovereignty · Backed by complete control
FAQs
Quick answers to the most common questions about Tscale Inference Endpoints, supported frameworks, model deployments, and security.
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What makes your AI inference service different from others?
Tscale Inference Endpoints are built on a fully integrated stack purpose-built for AI — auto-scaling GPU compute, optimised at every layer, with proprietary software tuning that delivers up to 7.2× faster throughput and up to 80% lower cost compared to hyperscalers. You get dedicated endpoints, not shared infrastructure, and you keep full control over your models and data.
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Can I integrate existing LLMs with your inference service?
Yes. We support 100+ open-source models out of the box — LLAMA 3, Mistral, Mixtral, Qwen, Phi, BGE, Whisper, Stable Diffusion, Florence, and more — and you can deploy any custom model in PyTorch, TensorFlow, or ONNX format. Our framework integrations include TensorFlow Serving, PyTorch Serve, Triton Inference Server, vLLM, and HuggingFace.
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What kind of support and optimisations do you offer for AI inference workloads?
We provide end-to-end support — from model optimisation (quantisation, pruning, kernel tuning) through to deployment on the right hardware (NVIDIA Blackwell, Rubin, or AMD MI300X). Our team helps with batching strategy, latency tuning, autoscaling thresholds, and continuous observability. Production endpoints come with metrics, logs, traces, and proactive alerting out of the box.
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How secure is your AI inference service?
All endpoints run on dedicated, isolated infrastructure — your models, weights, and inference traffic never share GPUs with other tenants. We support SOC 2 Type II controls, end-to-end encryption in transit and at rest, customer-managed encryption keys, private networking, and single-tenant deployment options for regulated workloads. See the Trust Center for full documentation.
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Do you support both batch and streaming inference?
Yes. Our tiered architecture is designed for both modes: high-throughput batch inference (think offline scoring, embeddings generation, dataset transformation) and low-latency streaming inference (real-time chat completions, voice agents, recommendation APIs). You can run both on the same hardware with workload-aware scheduling.
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How quickly can I deploy a new model to production?
For one of the 100+ pre-configured open-source models, you can have a production endpoint live in minutes. For a custom model, the typical path is: upload weights, configure hardware and autoscaling rules, validate with our pre-deployment test suite, and cut over — usually within an hour. Continuous deployment from your model registry is supported via the Radar API.