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One free scan finds every outdated or missing driver and matches the right update for your exact hardware.Free scan · exact hardware matchLSEG said on October 14, 2026, that it is collaborating with NVIDIA to accelerate analysis of its Tick History market-data archive. Using NVIDIA GPUs and the cuDF data-processing library, the companies report reducing a benchmark workload from hours to seconds while allowing customers to work with governed data without making another full copy.
What LSEG and NVIDIA announced
The collaboration combines LSEG Tick History with NVIDIA’s GPU-accelerated cuDF processing technology. The goal is to let financial institutions scan and analyse very large volumes of historical trades and quotes quickly enough for research, testing and operational decisions that traditionally depended on overnight or market-close batch jobs.
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LSEG describes the result as a benchmark improvement from hours to seconds. That figure is an LSEG claim, not a universal performance guarantee: the announcement does not disclose the workload configuration, GPU model, dataset slice, query design or reproducibility details needed to generalise the result to every customer environment.
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Market-data analysis often involves operations such as filtering instruments, grouping records by time or venue, calculating returns and liquidity measures, and replaying quote and trade sequences. CPUs can perform these tasks, but scanning billions of rows requires substantial time and storage bandwidth. GPUs are designed to execute many similar operations concurrently. cuDF provides dataframe-style operations that can run on NVIDIA GPUs, allowing portions of a familiar analytical workflow to be parallelised rather than processed serially on a small number of CPU cores.
In practical terms, a researcher could submit a large historical-data query, transform the resulting records and calculate statistics in a much shorter interactive session. Faster iteration matters when teams are testing many strategy variants, examining unusual market events or recalculating risk scenarios across thousands of instruments.
What is LSEG Tick History?
Tick History is LSEG’s archive of granular historical market data. LSEG says the collection contains:
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- Part number 900-53651-2500-000 and model: P3651
- This is the 2 slot version for when there is no empty slots between 2 slot cards. If you have one or more empty slots between the cards or the cards are 3 slot this NVLink will not work. See the attached images showing the card layout.
- NVLink 3.0 for any brand of RTX Ampere model graphics cards: 3090, A30, A40, A100 / H100 (Requires three NVLinks), A800, A4500, A5000, A5500, A6000
- This is the same as PNY part number: NVLAMP-2SLOT-BSP and RTXA6000NVLINK-KIT
- This is the same as Dell part number: 0RWJ7Y
| Measure | Reported scope |
|---|---|
| Rows | 110 trillion |
| Instruments | 100 million |
| History | More than 30 years |
| Sources | More than 580 exchanges, trading venues and contributors |
The scale is significant because the archive can support both broad statistical studies and highly specific market-event investigations. Analysts can examine individual trades and quotes rather than relying only on pre-aggregated bars, although the useful level of detail depends on the customer’s licensed access and chosen workflow.
Analysing data without creating another copy
A central part of the announcement is data governance. LSEG says customers can analyse detailed Tick History data without creating another complete copy of the underlying dataset. The stated benefit is not simply lower storage use: keeping analysis closer to the governed source can reduce the risk that an unmanaged duplicate outlives its licence, permissions or retention policy.
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The companies say licences, user permissions and controls remain intact while customers run accelerated processing. This approach is different from exporting a full archive into a separate data lake and then maintaining an independent copy. It does not eliminate the need for customer-side controls; firms still have to configure identities, entitlements, audit records and output handling correctly.
Who the capability is intended for
- Quantitative researchers: test signals over long histories and large instrument universes.
- Strategy-testing teams: run more parameter combinations or replay periods in the same research window.
- Execution and market analysts: study quote changes, fills, spreads and venue behaviour around executions.
- Risk teams: examine historical shocks and recalculate exposures across broad portfolios.
- Market-replay users: reconstruct sequences of trades and quotes for investigation or simulation.
- AI and machine-learning researchers: prepare high-volume training or evaluation datasets while retaining the source data’s governance framework.
Why demand for faster processing is rising
LSEG reported that traffic on its real-time network grew 29% in the first half of 2026 compared with the first half of 2025, reached 26 million messages per second in June, and that requests to its Tick History API increased 48%. Those figures indicate growing demand for both current and historical market-data access. As message volumes and analytical workloads rise, reducing the time spent waiting for historical queries can affect how many research or risk cycles a team completes in a day.
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- CUDA Cores: 4608 / NVIDIA Tensor Cores: 576 / NVIDIA RT Cores: 72
- GPU Memory: 24 GB GDDR6 with ECC / Bandwidth: 624 GB/Sec
- System Interface: PCI Express 3.0 x16
- Four DisplayPort 1.4 Connectors
- 3D Stereo Support with Stereo Connector
How to evaluate the approach against conventional analytics
| Decision factor | GPU-accelerated Tick History workflow | Conventional market-close or overnight workflow |
|---|---|---|
| Processing latency | LSEG benchmark: hours reduced to seconds; configuration not stated | Typically designed for scheduled or batch processing; exact time depends on the system |
| Historical depth | Access to Tick History’s reported 30-plus years and 110 trillion rows, subject to licence | Depends on the organisation’s retained and licensed archive |
| Data movement | Designed to avoid another full copy of the source dataset | Often relies on extracted, replicated or separately staged data |
| Governance | LSEG says licences, permissions and controls are preserved | Controls must be reimplemented and monitored across each copy or processing store |
| Best fit | Interactive research, replay, execution analysis, risk and AI workloads | Stable recurring reports and workloads where batch latency is acceptable |
What the announcement does not establish
- It does not specify which NVIDIA GPU model, memory configuration or software version produced the benchmark.
- It does not say whether every Tick History query will achieve an hours-to-seconds improvement.
- It does not publish the benchmark’s row count, filters, joins, transformations or comparison hardware.
- It does not provide public reseller, affiliate or referral terms for the data service, cuDF or GPU infrastructure.
Firms considering the technology should therefore request a workload-specific proof of concept. The meaningful test is not a headline speedup but the time required for the organisation’s own queries, data-entitlement rules, output formats and audit requirements.
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The collaboration points toward a model in which high-resolution historical data remains in a governed environment while specialised processors bring computation closer to the data. If the reported performance holds for a customer’s workloads, researchers could investigate deeper histories and more scenarios before a trading, risk or model-development decision. The governance design is equally important: faster analysis is useful only if the resulting workflow continues to respect market-data licences and internal access controls.
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