NVIDIA RTX PRO 6000 96GB GDDR7 AI Accelerator Card 900-5G144-2200-000
- RTX PRO 6000
- PCIe 5.0 x16
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AI accelerator card with 48 GB GDDR6 ECC memory, 18,176 CUDA cores and Tensor cores delivering 733 teraFLOPS FP16 performance in a dual-slot PCIe 4.0 x16 form factor
The NVIDIA L40S is a professional AI accelerator card built on the Ada architecture. It delivers 18176 CUDA cores and 48GB of GDDR6 memory with ECC for data integrity. The dual-slot design fits standard server chassis and targets machine-learning training, inference and high-performance visualisation workloads.
The card connects through a PCI Express 4.0 x16 slot providing up to 864 GB/s of memory bandwidth. Host systems must supply a compatible 4.0 x16 link to avoid throughput bottlenecks. The 350W maximum power draw requires adequate auxiliary connectors and chassis cooling capacity. Dual-slot width restricts adjacent slot availability in dense configurations.
This model replaces accelerators with less VRAM when 48GB is needed for larger models or batch sizes. The jump is worth it when workloads exceed the frame-buffer capacity of smaller cards and demand the 91.6 teraFLOPS FP32 or 1466 teraFLOPS FP8 throughput. Buyers who need more than 48GB or require multi-GPU scaling beyond a single dual-slot form factor should evaluate alternative platforms.
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| Product | |
|---|---|
| Brand name | NVIDIA |
| Model name | L40S |
| Part number | 900-2G133-0080-000 |
| Memory | |
| Type of memory | GDDR6 w/ECC |
| Memory clock | 864 GB/s |
| Graphics | |
| Graphics processor | L40S |
| Memory capacity | 48 GB |
| CUDA core count | 18176 |
| Chipset maker | NVIDIA |
| DirectX support | 12.07 |
| Ports & slots | |
| Interface type | PCI Express 4.0 x16 |
| DisplayPort connectors | 4 |
| Size & build | |
| Dimensions (l x h) | Height: 4.4 in Length:10.5 in |
| Slots occupied | Dual |
| Shipping weight | 1.00 kg |
| Package size | 279 × 102 × 51 mm |
The 48GB frame buffer with 864 GB/s bandwidth lets you hold larger batches and bigger layers on a single card, reducing the need for multi-GPU splitting.
That peak throughput translates to high single-precision compute density for inference pipelines, letting you serve more requests per second within the 350W power envelope.
The x16 link provides up to 32 GB/s bidirectional bandwidth, which caps host-to-device transfer rates for dataset loading and model checkpointing.
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