GPU Data Center for AI with NVIDIA compute ready for training and inference
Enterprise GPU as a Service with NVIDIA options and API, SSH or Jupyter access. Location, latency, controls, capacity and billing are sized for each workload and documented in the proposal.
Your model is ready. But AWS GPU has latency — and your data can't leave the country.
Composite example: a voicebot may require lower latency and defined data residency. The choice between global cloud and national hosting should be based on a workload benchmark, legal review and total-cost comparison.
The real cost of cloud GPU
Compute billed in USD + egress fees + slow quota approvals + data crossing borders — all charged at a volatile exchange rate.
The cost of buying your own GPUs
A single DGX H100 node exceeds USD 300K + power + cooling + specialized IT staff — CAPEX few companies can justify.
BITS GPU as a Service in Mexico
NVIDIA options, enterprise access, MXN billing and operations by scope. Hardware, data residency and service levels are confirmed by contract.
Validate your workload with a technical trial
Request an evaluation proposal with hardware, duration, success criteria and commercial terms defined in writing.
Global cloud GPU vs. BITS GPU Data Center in Mexico
How your AI operation changes when you move training and inference to GPU infrastructure on Mexican soil.
Latency measured from Mexico
We benchmark from your target locations to define a feasible objective for voicebots and APIs.
Transparent billing and transfer terms
The proposal details MXN rates, transfer, storage, minimums and reserved-capacity conditions.
Defined data residency
Location and applicable controls are documented by contract; the customer validates its regulatory obligations.
Planned provisioning
Hardware, capacity and activation date are confirmed against inventory, access requirements and identity controls.
Enterprise GPU infrastructure — not just compute
NVIDIA hardware, InfiniBand networking, NVMe storage, OpenAI-compatible APIs and 24/7 operation from Mexico.
Latest-generation NVIDIA GPUs
H100, A100 and L40S options sized for the workload and subject to inventory confirmed in the proposal.
- H100 for LLMs
- A100 for fine-tuning
- L40S for inference
400Gb/s InfiniBand networking
Distributed-training interconnect sized for topology, availability and the agreed performance objective.
- Intra-node NVLink
- Inter-node InfiniBand
- Native RDMA
NVMe + object storage
Local NVMe storage for active datasets and an S3-compatible bucket for checkpoints and models.
- Gen4 NVMe
- S3 compatible
- Automatic snapshots
API · SSH · Jupyter access
OpenAI-compatible REST/gRPC API, dedicated instances with SSH, and Jupyter Hub for data teams.
- OpenAI / vLLM compatible
- SSH + Docker + CUDA
- Managed JupyterHub
Sovereignty and security
Mexico residency options, encryption, tenant isolation and enterprise SSO documented for the contracted scope.
- LFPDPPP
- ISO 27001 · SOC 2
- RBAC + auditing
24/7 AI NOC
Utilization monitoring, thermal alerts, autoscaling and on-call SRE specialized in AI.
- Live GPU telemetry
- Autoscaling
- AI engineering support
How GPU-cluster telemetry is visualized
A demonstration of utilization, temperature and workload telemetry. Values do not represent customer capacity, availability or load.
GPU Cluster · Mexico Data Center
DEMOModel a cost scenario for your AI compute
An illustrative comparison between global cloud and GPU as a Service. Replace the assumptions with current quotes before deciding.
Your AI workload
Monthly comparison
One platform for the entire AI lifecycle
Train, evaluate and serve models on the same infrastructure — no data movement, no re-deploys, no extra vendor.
Benefits for training and fine-tuning
- Multi-GPU clusters sized for the model, dataset and available inventory
- PyTorch, TensorFlow, JAX and DeepSpeed preinstalled
- Fine-tuning of Llama 3, Mistral, Whisper and proprietary models
- Automatic checkpointing to S3-compatible storage
- Reserved capacity under conditions defined in the quote
Benefits for serving and APIs
- Latency validated through a benchmark from target locations
- API compatible with OpenAI · vLLM · TGI
- Concurrency-based autoscaling with no aggressive cold starts
- L40S optimized for high-concurrency inference
- Data-transfer and egress terms detailed in the proposal
Guided GPU inference trial
We benchmark your workload and document the environment, latency and throughput so the comparison is reproducible.
Five steps to bring your AI to BITS GPU
From assessment to your first model in production — a phased process with enterprise AI experts.
AI workload assessment
Analysis of models, datasets, required throughput and sovereignty constraints.
GPU sizing
Selection of H100, A100 or L40S, cluster topology and required storage.
API / SSH access
Provisioning of credentials, isolated namespaces, enterprise SSO and RBAC.
On-demand scaling
Load-based autoscaling, reserved capacity for production and spot for experimentation.
Pay-per-use billing
Monthly reports in MXN, utilization telemetry and continuous optimization.
Measurable objectives for cost, latency and data residency
Each objective is set after the benchmark, quote, and technical and regulatory scope review.
Comparable total cost
The proposal breaks down compute, transfer, storage, support and reserved capacity.
Benchmark-based latency
Reproducible measurement from your users and with your model before an objective is committed.
Documented residency
Location, data flows, controls and responsibilities defined by contract.
Your AI platform is stronger with the rest of the BITS stack
Dedicated connectivity, SD-WAN, AI cybersecurity and NOC monitoring for a world-class AI operation.
Dedicated connectivity
Symmetric links and dark fiber to your GPU data center.
SD-WAN / SASE
Smart connectivity between branches, cloud and on-prem GPU.
AI Security & Governance
Prompt controls, model DLP and AI compliance.
Zero Trust / ZTNA
Identity-based secure access to your GPU pipelines and notebooks.
24/7 NOC monitoring
GPU telemetry, alerts and monthly SLA reports.
Managed cloud
Orchestration between your public cloud workloads and BITS GPU.
NVIDIA ecosystem
Hardware, manufacturer support and availability are specified in each proposal.
In-house AI team
Data scientists and MLOps engineers support your adoption end-to-end.
Agreed provisioning date
Activation is scheduled after inventory, access controls and acceptance criteria are confirmed.
Our own 24/7 NOC
Monitoring and on-call SRE from Chihuahua — we don't outsource it.
GPU use cases for your sector
Banking and fintech
Fraud prevention, credit scoring and voicebots with CNBV-compliant data.
Manufacturing
Computer vision for quality control and predictive maintenance.
Healthcare
Imaging, records NLP and clinical models with data in Mexico.
Retail / e-commerce
Recommenders, semantic search and chatbots with local catalog data.
Workloads that can be evaluated on dedicated GPU
Technical training and inference scenarios that must be validated with customer data, capacity and success criteria.
Paseo Central – Chihuahua
Integración tecnológica para un complejo comercial, hotelero y corporativo.
- CCTV Digital IP de Misión Crítica
- Control de Acceso Avanzado
Grupo México
Seguridad y automatización para ambientes mineros exigentes.
- Monitoreo Integral con IA
- Reducción de Costos en Operación
Grupo Bafar
Servicios Administrados de red LAN/WAN y Seguridad Perimetral.
- Disponibilidad histórica documentada del 99.13%
- Optimización histórica de costos WAN
More on GPU Data Center and enterprise AI
Articles on model training, inference, GPU architectures and AI use cases in Mexico.
Example nvidia-smi telemetry.
The console illustrates available metric types. Hardware, location, performance and data residency are confirmed for each environment.
Partners y tecnologías que dominamos
Frequently asked questions about the AI GPU Data Center
Validate the infrastructure for your AI workload
Request a proposal with benchmark, capacity, location, controls, price and access date defined before work starts.