Keep your models, data and edge in-house.
On-premises GPU systems for quant research, risk, fraud detection and private language models.
The constraint
Proprietary strategies, client records and market-data licences rarely allow a copy in someone else's cloud. Regulators also expect firms to know where their models run and who can reach them. Meanwhile, Monte Carlo risk runs, backtests and model training all want more GPU than a desk can hold.
Workloads
Quant research & backtesting
Run GPU-accelerated dataframes and backtests over years of tick data, so researchers test more ideas per day.
Risk & pricing
Run Monte Carlo VaR, XVA and derivatives pricing with more paths in the same overnight window.
Fraud & AML detection
Train and serve transaction models, including graph models that follow money across accounts.
Private LLMs for research & compliance
Serve open-weight models over filings, research notes and policy documents, with every prompt kept on your network.
Fine-tuning on proprietary data
Adapt models to your instruments, terminology and house style without sharing the data.
Systems
- GLabs GPU Servers — risk, training and inference nodes.
- GLabs AI Workstations — deskside GPU systems for quant researchers.
- GLabs Vault — tick history, training data and model artefacts.
Deployed with
GLabs AI Stack for fraud models, LLMs and fine-tuning. GLIDS for risk and backtesting compute.
Related solutionsSelf-Hosted LLM InferenceFine-Tuning & Domain Adaptation
FAQs
Can GPUs be used for low-latency trading?
Execution paths that need microsecond latency generally run on FPGAs and tuned CPUs, not GPUs. GPUs earn their place one step back: in signal research, model training, risk and pricing. That is the side we build for.
Do GPUs speed up Monte Carlo risk and pricing?
Monte Carlo methods are highly parallel and map well to GPUs. Pricing and risk libraries with GPU support can run more paths in the same window, which tightens estimates or shortens the batch. Gains depend on the library and the model.
Can we run a private LLM over research and filings?
Yes. Open-weight models can be served on your own GPU servers and connected to your document stores for retrieval. Prompts, documents and answers stay on your network.
How does on-premises AI fit RBI's IT outsourcing rules?
RBI's Master Direction on outsourcing of IT services asks regulated entities to manage the risk of depending on third-party providers. Running models on infrastructure you own reduces that dependence, though your own controls still have to meet the direction.
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