Optimise for Performance
Upgrade your AI solutions and reduce costs by leveraging fine-tuning inference. Our powerful GPUs accelerate the training process and ensure your models are faster, more accurate, and more reliable.
/ MODEL FINE-TUNING
Model Fine-Tuning
At Tscale, we offer GPU-accelerated compute solutions to manage advanced infrastructure and support amounts of flexible workloads, ensuring your AI models achieve their highest potential and most accurate results.
Accelerate Time to Market
Tscale Cloud simplifies the complexity of managing and scaling fine-tuning processes. Speed up your time-to-market, allowing you to bring products and services to market quickly.
Cost-Effective Scalability
Tscale’s pricing is customisable, robust, and cost-effective. Easily scale your fine-tuning operations, no matter the size. Our flexible solutions allow you to scale faster than your competitors, with the added benefit of avoiding models that grow out of your needs.
/ LEVERAGE ADVANCED GPU CLOUD INFRASTRUCTURE
// 30% FASTER TIME TO VALUE
// 40% EFFICIENCY IMPROVEMENT
Fast, efficient model fine-tuning
Leveraging the latest GPU technology to deliver unparalleled performance efficiency and scalability, our infrastructure supports your needs, ensuring your AI models achieve their highest potential and bring value to the market.
30% Faster Time to Value for Your AI Projects
Accelerate Your AI Success. We use infrastructure to you can focus on the innovation.
40% Efficiency Improvement
Get More Performance for Less. On average, model GPU cloud platform provides 40% cost-saving in comparison to hyperscalers.
Fine-Tuning Stack
Marketplace
- Jupyter Notebooks
- TensorFlow
- PyTorch
User Experience
- Web Console
- API
- CLI
Platform
- Virtual Machines
- Managed Kubernetes
Infrastructure
- GPU Compute
- Storage
- Networking
Hardware
- A40
- H100
- H200
- GB200
- MI300X
Data Centre
- Renewable Energy
- Low-latency Fabric
Performance
Accelerate Time to Insights
Tscale Cloud accelerates time to insights by up to 30%, thanks to its AI-optimised stack.
More performance for less
Tscale delivers an average 80% cost-saving in comparison to hyperscalers.
Improved Resource Utilisation
Up to 40% improvement in efficiency across compute, memory, and networking.
Faster Inference
GPUs with UCMM tuning improve throughput and latency by up to 7.2x.
Key Services
AI Compute Training
A highly scalable, performance-optimised technology that significantly reduces training times and lowers production costs.
Learn MoreAI Marketplace
An ecosystem of services for developing and deploying AI applications built using Tscale tools and other popular AI/ML software.
Learn MoreMore solutions
Tscale accelerates the journey from development to deployment, delivering faster time to productivity for your AI initiatives.
FAQs
Quick answers to the most common questions about Tscale’s Model Fine-Tuning platform, supported frameworks, model customisation, and performance.
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What makes Tscale’s GPU Cloud different from others?
Tscale is purpose-built for AI workloads — not retrofitted from general-purpose cloud. Every layer of the stack is optimised for fine-tuning: bare-metal GPU nodes with NVIDIA Blackwell and Rubin, behind-the-meter power for predictable costs, proprietary software tuning (UCMM) that delivers up to 7.2× faster throughput, and an integrated environment that takes you from base model to production-ready fine-tune without leaving the platform.
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What types of GPUs does Tscale offer?
We deploy the full spectrum of modern AI accelerators: NVIDIA A40, H100, H200, and GB200 (Blackwell) for production fine-tuning, plus AMD MI300X for cost-optimised paths. New hardware lands on the platform within weeks of release — your team always has access to the latest generation, on the same blueprint across regions.
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What industries can benefit from Fine-Tuning?
Fine-tuning delivers value across virtually every sector. Common use cases include healthcare (specialised clinical models), legal (contract and case-law assistants), finance (risk and compliance models), customer support (brand-aligned conversational agents), software development (code generation in proprietary stacks), manufacturing (predictive maintenance), and research (domain-specific scientific models). Any team that needs an LLM to behave consistently with proprietary knowledge benefits from fine-tuning.
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How long does a fine-tuning run take?
For a typical LoRA fine-tune on a 7B–13B parameter model, end-to-end runs take 2–8 hours on Tscale’s H100 clusters. Larger models (70B+) or full-parameter fine-tunes can take 12–48 hours depending on dataset size. We provide real-time monitoring and can resume failed jobs from the last checkpoint automatically.
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Do you support LoRA, QLoRA, and full-parameter fine-tuning?
Yes. We support all three training modes — full-parameter fine-tuning, LoRA, and QLoRA — with pre-tuned containers for the most common open-source base models. Our team can also help you choose the right approach for your dataset size, base model, and deployment constraints. Hyperparameter search, gradient checkpointing, and mixed-precision training are all available out of the box.
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Is my training data secure during fine-tuning?
Yes. All fine-tuning runs on dedicated, isolated infrastructure — your data, weights, and gradients 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, and single-tenant deployment options for regulated industries. Training data is purged after job completion by default, with configurable retention windows.