Comparing Enterprise NVIDIA GPUs: A100, H100, T4, A30, and Jetson - Which One to Pick
Updated: Oct 7, 2025
NVIDIA offers a wide range of GPUs built for enterprise infrastructure, covering AI, data analytics, and high-performance computing (HPC). Each model serves a different workload, from AI training and inference to visualization and edge computing.
NVIDIA GPUs - Save Up To 80%
✔️ 5-Year Warranty – No Risk: Pay Only After Testing
NVIDIA A100 GPU (Ampere, 2020)
Best for: AI Training, Inference, and HPC
Memory: Up to 80 GB HBM2e
Performance:The A100 supports MIG (Multi-Instance GPU), allowing one GPU to be partitioned into multiple isolated instances. It remains a foundation in enterprise AI and HPC deployment.
Use Case:Large model training, scientific computing, data-intensive analytics.
NVIDIA A30 GPU (Ampere, 2021)
Best for: Mixed AI Inference / Training and Analytics
Memory: 24 GB HBM2
Performance:Built for hybrid workloads, the A30 handles inference and training tasks effectively and supports MIG.
Use Case:Organizations needing flexible GPU use across AI and analytics workloads.
NVIDIA A40 GPU (Ampere, 2020)
Best for: Visualization + AI Acceleration
Memory: 48 GB GDDR6
Performance:Optimized for rendering, GPU-accelerated graphics, VDI, and visual workloads, while offering support for AI workloads.
Use Case:Design, architecture, simulation, visualization tasks with AI components.
NVIDIA RTX A6000 GPU (Ampere, 2020)
Best for: High-End Visualization, AI, Simulation
Memory: 48 GB GDDR6
Performance:A professional workstation-class GPU that balances rendering and AI capabilities.
Use Case:Rendering, simulation, design, and AI-augmented visualization in enterprise environments.
NVIDIA H100 GPU (Hopper, 2022)
Best for: Next-Gen AI, LLMs, HPC
Memory: 80 GB HBM3 (94 GB in the NVL configuration)
Performance:Offers significant advances over A100, with Transformer Engines, updated Tensor Cores, and stronger performance for large-scale AI tasks.
Use Case:Training large language models, real-time analytics, high-end scientific workloads.
NVIDIA H200 GPU (Hopper / next iteration, 2024)
Best for: AI & HPC at scale
Memory: ~141 GB HBM3e (per GPU)
Performance:An evolution over H100, the H200 offers increased memory bandwidth and capacity, making it strong for next-generation AI and HPC deployments.
Use Case:Massive AI training clusters, cutting-edge model deployment, exascale computing.
NVIDIA L4 GPU (Ada Lovelace / data center, 2023)
Best for: AI Inference in Cloud & Edge
Memory: 24 GB GDDR6
Performance:Serves as the successor to T4. It is optimized for throughput and power efficiency in inference workloads.
Use Case:Scalable inference, video processing, cloud-native AI services.
NVIDIA L40 / L40S GPU (Ada Lovelace, 2023)
Best for: Visual + AI Workloads
Memory: 48 GB GDDR6
Performance:Replaces A40 in many use cases, combining strong visualization/rendering performance with AI acceleration.
Use Case:Visualization, design, rendering with AI-enabled workflows.
NVIDIA RTX 6000 Ada (Ada Lovelace, 2022)
Best for: Visualization, Simulation, AI Tasks
Memory: 48 GB GDDR6
Performance:A newer workstation GPU that replaces RTX A6000 in many workloads, offering improved efficiency and feature set.
Use Case:Workstations for design, simulation, AI-infused visualization workflows.
NVIDIA B100 / B200 GPUs (Blackwell architecture, 2025)
Best for: Large-Scale AI Training & Inference
Memory: HBM3e (expected capacities up to ~192 GB depending on model)
Performance:These are the upcoming next-generation GPUs succeeding Hopper, intended to push AI training, inference, and HPC further.
Use Case:Future AI/data center deployments, exascale computing, next-tier LLM training.
NVIDIA Jetson Orin / Edge AI Modules (2020)
Best for: Edge AI & Embedded Systems
Memory: Up to 32 GB (LPDDR5 or similar)
Performance:Jetson Orin modules bring updated performance to robotics, industrial edge, and embedded AI systems.
Use Case:Edge inference, robotics, IoT, autonomous systems requiring local AI.
Choosing the Right NVIDIA GPU
For AI Training & HPC: H200, H100, A100, B100/B200
For AI Inference: L4, A30, or H100 NVL config
For Visualization + AI: L40 / L40S, RTX 6000 Ada, A40
For Edge AI: Jetson Orin modules
NVIDIA GPUs - Save Up To 80%
✔️ 5-Year Warranty – No Risk: Pay Only After Testing
Пошук бізнес партнерів у Європі поступово приніс свої результати. Частину питань можна вирішити онлайн, але особисті зустрічі все одно мають велике значення. Так легше обговорити деталі співпраці, познайомитися ближче та знайти спільні напрямки для роботи. Зараз організувати таку поїздку з України вже не так складно. Міжнародні автобусні перевезення, зокрема автобуси Україна Європа Transempo https://transtempo.ua/, допомагають зробити дорогу до країн ЄС доступнішою. Це зручно, коли потрібно відвідати партнерів, поїхати на конференцію або просто підтримати робочі контакти наживо.