Services / NVIDIA® Solutions
NVIDIA expertise that ships
Deep experience across the NVIDIA stack, from AI and ML workloads to full AI Factory deployments. We design, optimize, and run production systems on NVIDIA infrastructure.
NVIDIA AI Factory Solutions
We implement and support NVIDIA AI Factory Solutions end to end, turning clusters of compute, networking, and storage into a coherent, high-throughput AI production environment. From reference architecture to operational runbooks, we help you move from rack delivery to running workloads.
- AI Factory design, rack-scale planning, and cluster orchestration
- NVIDIA AI Enterprise, NIM, and NeMo integration and tuning
- Networking, storage, and scheduler configuration for sustained throughput
- Observability, cost controls, and operational handoff
AI and ML workloads at scale
We build and optimize the workloads that actually run on NVIDIA hardware, training, fine-tuning, inference, and agentic pipelines. The focus is always on throughput, reliability, and getting the most out of every GPU hour.
- Distributed training and large-model fine-tuning
- High-throughput inference, batching, and quantization
- CUDA optimization, kernel profiling, and memory planning
- Agentic AI, RAG, and multi-modal pipelines on GPU
Certified NVIDIA expertise
Four NVIDIA certifications, earned independently and self-studied, plus extensive training across the NVIDIA AI stack. We bring platform-level fluency to every engagement, backed by hands-on experience deploying NVIDIA infrastructure in production.
- NVIDIA certifications in AI Infrastructure, Agentic AI, AI Operations, and AI Advisor/Technical Sales
- Deep Learning Institute and AI Factory-aligned training
- Production experience with DGX, HGX, GH200, and H100/B200-class systems
Hardware and platform coverage
We work across the NVIDIA data center portfolio and the software layers that make it useful. Whether you are standing up a new cluster or tuning an existing one, we know where the bottlenecks hide.
- DGX, HGX, GH200, H100, B200, B300, and compatible GPU platforms
- InfiniBand, Spectrum-X, and NVLink-based fabrics
- Kubernetes, Slurm, and GPU-aware workload schedulers
Compare hardware differences
| Spec | B300 | B200 | H200 | H100 | RTX PRO 6000 | R100 (2026)* |
|---|---|---|---|---|---|---|
| Architecture | Blackwell Ultra | Blackwell | Hopper | Hopper | Blackwell | Rubin* |
| VRAM | 288 GB HBM3e | 192 GB HBM3e | 141 GB HBM3e | 80 GB HBM3 | 96 GB GDDR7 | 288 GB HBM4* |
| Memory Bandwidth | 8 TB/s | 8 TB/s | 4.8 TB/s | 3.35 TB/s | ~1.8 TB/s | Up to 22 TB/s* |
| FP4 Compute | 15 PFLOPS | 9 PFLOPS | N/A | N/A | ~2 PFLOPS (dense) | 50 PFLOPS* |
| FP8 Throughput | 7,000 TFLOPS | 4,500 TFLOPS | ~4,000 TFLOPS | ~2,000 TFLOPS | ~1,000 TFLOPS (dense) | ~16,000 TFLOPS* |
| Interconnect | NVLink 5 (1.8 TB/s) | NVLink 5 (1.8 TB/s) | NVLink 4 (900 GB/s) | NVLink 4 (900 GB/s) | PCIe 5.0 | NVLink 6 (3.6 TB/s)* |
| Transistors | 208 billion | 208 billion | 80 billion | 80 billion | 92 billion | 336 billion* |
| Rent it now | Available now | Available now | Available now | Available now | Available now | Not yet (H2 2026)* |
B300, B200, H200, H100, and RTX PRO 6000 match the GPUs available for rent at /gpus/. RTX PRO 6000 compute figures are dense (non-sparse) values derived from NVIDIA's published 4,000 AI TOPS (FP4, sparse) rating. *R100 is pre-launch: figures are projected and unconfirmed, and it is not yet rentable. All figures are representative of public NVIDIA specifications and may vary by OEM configuration.
Industry experience
We have applied NVIDIA AI and ML infrastructure in regulated, high-stakes environments where reliability, compliance, and security matter as much as performance.
- Banking & Insurance: Risk modeling, fraud detection, document intelligence, and customer-facing AI on governed GPU infrastructure.
- Financial Services: Trading analytics, portfolio optimization, and real-time inference pipelines with strict latency and audit requirements.
- Healthcare: Medical imaging, clinical NLP, and research compute while respecting data privacy, HIPAA, and institutional review workflows.
- Government: Secure AI factories, classified and unclassified workloads, compliance boundaries, and sovereign infrastructure.
- Academia: Research clusters, HPC/AI convergence, student access, and grant-funded infrastructure from proposal to production.
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