Cisco UCSC-GPU-L4 | NVIDIA L4 GPU, 24GB GDDR6, 70W, PCIe Gen4 x16

Q: What workloads is the NVIDIA L4 GPU optimized for?
A: The NVIDIA L4 is optimized for AI inference at scale, video transcoding and processing, virtual desktop infrastructure (VDI), generative AI applications including stable diffusion and large language models, recommendation engines, computer vision, and real-time graphics rendering. Its energy-efficient design makes it ideal for edge computing and mainstream enterprise deployments.

Q: What servers are compatible with the UCSC-GPU-L4?
A: The UCSC-GPU-L4 is compatible with Cisco UCS C-Series rack servers including C220 M5/M6/M7 and C240 M5/M6/M7/M8 models, as well as Cisco UCS X-Series modular systems with appropriate PCIe nodes. The low-profile single-slot form factor allows installation in standard PCIe Gen4 x16 slots without requiring additional power connectors or cooling upgrades.

Q: How does the L4 compare to the previous generation T4 GPU?
A: The NVIDIA L4 delivers approximately 2.5X better generative AI performance and up to 4X better graphics performance compared to the T4, while maintaining a similar power envelope and form factor. Built on the newer Ada Lovelace architecture with fourth-generation Tensor Cores, the L4 adds FP8 precision support, hardware AV1 encode/decode, and 24GB GDDR6 memory compared to the T4's 16GB.

Q: Does the L4 require external power connectors?
A: No, the UCSC-GPU-L4 is entirely slot-powered through the PCIe interface and draws only 70W maximum. No auxiliary PCIe power cables are required, simplifying installation and making it suitable for servers with limited power delivery infrastructure.

Q: What AI precision formats does the L4 support?
A: The L4 supports multiple precision formats for both training and inference: FP32, TF32, FP16, and BFLOAT16 for training workloads, plus FP8 and INT8 for optimized inference performance. The fourth-generation Tensor Cores with FP8 support enable up to 485 TFLOPS of AI compute performance, making it highly efficient for production inference deployments.