AI infrastructure for financial services

From signal generation to compliance review, run trading, risk, and compliance workloads on dedicated NVIDIA AI infrastructure. Backed by independently audited performance and the ML engineers who produced it. Trading-desk speed. Procurement-grade proof.

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Numbers that survive procurement review

Lambda published the first audited STAC LANG6 result on NVIDIA HGX B200. STAC Research verified the full stack under test using a benchmark designed by technologists from financial institutions, so you can compare it against any other audited system from any vendor.
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91% lower latency at production load

Llama 3.1 8B at 165 req/s: 1.39s median on NVIDIA HGX B200 vs. 15.5s on NVIDIA HGX H200.

3.6× batch throughput on a 70B model

12,040 words per second on Llama 3.1 70B vs. 3,351 on the H200 baseline.

0.095s time to first token on a 70B model

At 20 req/s. Reasoning-grade quality at a speed analysts will use during market hours.

NVIDIA HGX B200 systems at scale for Hudson River Trading

The compute backbone for HRT's quantitative research roadmap.

Why choose Lambda

Procurement wants proof: audited numbers, working code, and single-tenant infrastructure.

Audited performance you can verify

The STAC audit covers hardware, software versions, and configuration, and verifies that results reproduce under the stated conditions. When Lambda publishes a STAC number, your risk team can hold it to the same standard as any other audited system.

NVIDIA NIM microservices, self-hosted

Deploy NVIDIA NIM microservices on your Lambda cluster in three commands. OpenAI-compatible endpoints mean existing code changes one URL. Model weights and trading data stay inside your environment.

ML engineers who build the pipeline with you

Lambda's ML engineers build working financial services pipelines, from limit order book anomaly detection to compliance alert generation, then hand over the notebooks, the GPU configuration, and the benchmark results. Bring a bespoke workload, and your engineers get code they can run.

Capacity sized for quant workloads

Launch on-demand NVIDIA HGX B200 and NVIDIA HGX H100 instances in minutes for bursty backtests. Reserve 1-Click Clusters™ from 16 to 2,000+ GPUs on terms from two weeks to multiple years. Scale to Superclusters when the research roadmap demands it.

Built for the workloads that move markets

Each use case below runs on Lambda today. The numbers come from audited benchmarks and ML engineering demos.

Quantitative research and high-frequency trading

Signal generation, backtesting, and alpha research scale with compute. When Hudson River Trading's on-premises infrastructure hit its ceiling, HRT moved its research roadmap onto NVIDIA HGX B200 systems on Lambda, with the networking, storage, and orchestration to match.

 

"Lambda stood out for its technical depth and operational clarity. We're confident we've found the right partner to help power our workloads."  — Gerard Bernabeu Altayo, Compute Systems Lead, Hudson River Trading

 

Read the announcement

Risk and compliance

Spoofing and layering hide in the limit order book, and rules alone miss them. Lambda's ML engineers ran a self-hosted pipeline using NVIDIA NIM microservices against 1,053,322 real NASDAQ order events. Supervised detection reached a 0.972 F1 score on NVIDIA HGX B200, and NVIDIA NV-Tesseract surfaced 4,506 anomalies in a 100,000-event window that rule-based detection missed. A large language model, served as an NVIDIA NIM microservice, then wrote each alert in language a compliance officer can act on.

Fraud detection

Fraud models only help if they score events faster than the events arrive. The same GPU-native stack (NVIDIA RAPIDS cuDF and cuML) loaded one million order events into GPU memory and ran supervised classification at 3.2 million events per second on NVIDIA HGX B200. Keep inference on dedicated single-tenant GPUs so transaction data never leaves your environment.

Model training

Credit scoring, risk models, and portfolio optimization need training runs that finish before the assumptions change. Train on NVIDIA HGX B200 clusters with NVIDIA Quantum-2 InfiniBand networking, managed Kubernetes or Slurm, and ML engineers who tune throughput with you. Banks such as TD Bank and RBC train and iterate on models on Lambda today.

Resources for financial services teams

Lambda's STAC LANG6 results on NVIDIA HGX B200

What the numbers mean for trading desks, compliance teams, and wealth management.

Hudson River Trading and Lambda

Why HRT chose Lambda when on-premises infrastructure hit its ceiling.

STAC’s audited report

The full third-party report: configuration, methodology, and every data point.

Hands-on workshop

A half- or full-day lab with Lambda ML engineers and NVIDIA, on your workloads.

Secure by design. Mission‑critical by default.

Trading data and client records stay inside a single-tenant, shared-nothing architecture. Deployments are performed by Lambda employees and vetted partners, and you can revoke Lambda's credentials at any time.

  • SOC 2 Type II attestation
  • ISO 27001, 27017, 27701, and 22301 certified
  • Single-tenant clusters: no shared compute, network, or storage
  • Customer-governed access with MFA and continuous monitoring
  • No charges for data ingress or egress
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