NVIDIA GPUs for Enterprises & Data Centres

NVIDIA GPUs provide scalable acceleration for AI, HPC, and data-centre workloads. Built for virtualised and hybrid-cloud environments, they combine massive parallel compute performance with advanced telemetry, efficient power design, and secure, multi-instance architectures. The result is a flexible GPU platform that supports everything from large-scale model training to high-density inference and visualisation across modern enterprise infrastructures.

NVIDIA GPUs: key features

Advanced Telemetry and Automation: NVIDIA enterprise GPUs integrate real-time monitoring, management APIs, and orchestration tools for visibility and automation across AI, data analytics, and virtualised enterprise workloads.
Exceptional Compute and Memory Performance: With high-bandwidth memory, NVLink interconnects, and multi-GPU scalability, these accelerators deliver exceptional throughput for deep learning, simulation, and visualisation.
Flexible Deployment Modes: Supporting virtual GPU (vGPU), bare-metal, and containerised deployments, NVIDIA GPUs integrate seamlessly with on-premises, hybrid, and cloud IT infrastructures for maximum operational flexibility.
Secure and Energy-Efficient Design: Built with hardware-level isolation, workload partitioning, and power-optimised designs, NVIDIA enterprise GPUs deliver secure, sustainable performance in dense data-centre environments.

Why Choose Steel City Consulting?

With decades of industry expertise, our team has an extensive technical understanding of NVIDIA’s product portfolio – enabling us to assess the right solutions for complex or high-demand environments.

Our recommendations can be mapped directly to your performance targets, integration requirements, and future roadmap. This ensures your chosen GPU is fully compatible, cost-efficient, and ready to scale with your environment.

Customer FAQ: NVIDIA GPUs

Need help choosing the right NVIDIA GPU? Our FAQ answers 4 common questions.

Open NVIDIA GPU FAQ

Q1: How do the architecture and tensor-core capabilities differ across A100, H100 and the newer Ada/Amphera-class L-series for training and inference?

NVIDIA’s data-centre families diverge on tensor-core microarchitecture and supported precisions:

The Ampere-class NVIDIA A100 supports TF32/FP16/BF16 and provides MIG partitioning.

Hopper-class H100 introduces FP8 and substantial TFLOPS uplift on tensor cores plus architectural changes that accelerate transformer kernels

Ada Lovelace-derived L-series NVIDIA GPUs (L4, L40, L40S) focus on mixed precision, AV1/AVC accelerated media paths and high TF32/FP32 graphics performance for converged AI+graphics workloads.

NVIDIA H100 is ideal for large-model training throughput and FP8 sparsity benefits.

A100/A800 remain strong for FP32/BF16 training and MIG-based multi-tenant consolidation. L-series and RTX/Quadro GPUs optimise mixed AI/graphics pipelines and inference per watt.

Q2: Which NVIDIA GPUs support hardware partitioning (MIG), and what does that mean for performance consistency and security?

Multi-Instance GPU (MIG) is supported on NVIDIA A100 and A800 accelerators, with up to seven hardware-isolated GPU instances per device. For environments requiring deterministic resource separation across tenants or mixed AI workloads, MIG-capable GPUs (A100, A800, H100) are the recommended choice. Our specialists can advise on optimal MIG configuration and workload sizing for your infrastructure.

Other data-centre GPUs (including the A40, L40, L40S, L4, A16, V100, and T4) do not feature MIG, but can still support NVIDIA vGPU or container-level isolation through software.

Open GPU Model Comparison: Memory, Interconnect, Power & MIG

GPU (representative SKU)	GPU Memory (GB)	Memory Type	Sustained Memory Bandwidth (GB/s)	NVLink / NVSwitch	Max TDP (W)	MIG
NVIDIA H100 (SXM / PCIe variants noted)	80 / 94	HBM3	~3,350 – 3,900 GB/s (SXM variants)	NVLink / NVSwitch (high aggregate)	up to ~700 W (SXM peak) / 350 W (PCIe)	H100 supports advanced partitioning (architecture dependent)
NVIDIA A100 (SXM / PCIe)	40 / 80	HBM2e	~1,935 – 2,039 GB/s	NVLink (SXM)	300–400 W (varies by form factor)	MIG up to 7 partitions (A100)
NVIDIA A800 (PCIe variants)	40	HBM2/HBM2e	~1,555 GB/s (A800 PCIe 40GB)	NVLink (supported)	~240 W (A800 PCIe)	MIG profiles (A800 supports MIG instances)
NVIDIA V100 (Volta)	16 / 32	HBM2	~900–1,134 GB/s	NVLink (300 GB/s)	250–300 W	No MIG (Volta does not implement MIG)
NVIDIA A40 / Quadro RTX 8000	48 (A40) / 48 (Quadro RTX 8000)	GDDR6 / GDDR6	A40 ≈696 GB/s / Quadro RTX 8000 ≈624 GB/s	NVLink (A40 2-way)	A40 ≈300 W / RTX 8000 ≈250 W	A40 supports vGPU; MIG not available on GDDR cards.
NVIDIA L4, L40, L40S	24 (L4) / 48 (L40/L40S)	GDDR6	L4 ≈300 GB/s / L40/L40S ≈864 GB/s	PCIe Gen4 (no NVLink)	L4 ≈72 W / L40 ≈300 W	No MIG (PCIe GDDR cards)
NVIDIA T4	16	GDDR6	~300 GB/s	PCIe Gen3	70 W	No MIG (T4 is inference accelerator)
NVIDIA A16 (quad-GPU board)	4×16 = 64 (aggregate)	GDDR6	4×200 GB/s (aggregate)	PCIe Gen4	250 W (board)	vGPU target (high-density VDI), hardware partitioning per board design.

Q3: Which GPUs are optimised for high-density VDI / multi-session inference vs maximum single-instance training throughput?

High-density multi-session / VDI / inference per watt: A16, L4, T4 and A800 (MIG-capable partitions) deliver high consolidation ratios with lower per-card TDP and high encoder/decoder density (A16 specifically built as a quad-GPU board for VDI).

Maximum single-instance training throughput / large model training: H100 (SXM) and A100 (SXM) families—SXM variants with NVLink/NVSwitch—provide the largest single-instance memory footprint and interconnect bandwidth for model parallelism.

Converged graphics + AI workloads: L40 / L40S and Quadro/RTX 8000 series provide large GDDR memory, RT/ Tensor core balance and are fit for graphics-heavy AI inference and simulation.

Q4. Which model is the ideal NVIDIA GPU for our deployment?

Open Deployment Comparison: Key Model Strengths & Form Factor

GPU	Primary deployment fit	Key strengths	Typical infrastructure form factor
H100	Large-model distributed training, FP8 accelerated inference	Highest tensor throughput, HBM3 bandwidth, optimal for transformer scale	SXM for max perf; PCIe variants for server fitment.
A100 / A800	Mixed FP32/BF16 training, MIG multi-tenant inference	Strong MIG support (A100/A800), HBM2e bandwidth, validated AI stacks	SXM and PCIe server blades; A800 PCIe for lower TDP.
V100	Legacy HPC & DL workloads	Proven FP64 performance for HPC, HBM2	PCIe / SXM2 (older platforms) — suitable for validated HPC clusters.
A40 / Quadro RTX 8000	Visualisation, rendering + AI inference	Large GDDR framebuffer, NVLink (A40), strong graphics/Tensor balance	PCIe server/workstation cards with passive thermal solutions.
L40 / L40S	Converged AI + high-fidelity graphics, inference/visualisation	High GDDR bandwidth for graphics, accelerated media, AV1 support (L40 family)	PCIe Gen4 single/dual slot cards for rack servers.
L4 / T4	High-density inference, transcoding and edge-type racks	Low TDP, high encoder/decoder density, economical inference throughput	Low-profile PCIe (L4 low-profile / T4 single slot) — ideal for dense inference nodes.
A16	VDI / multi-session workloads	Quad-GPU board for very high session density and vGPU profiles	Full-height quad GPU board, passive cooling for data-centre racks.
RTX / Quadro RTX 8000	Workstation/visualisation clusters	Very large GDDR frame buffer for massive scenes, RT + Tensor cores	PCIe workstation / passive server cards; NVLink for multi-GPU visualisation