CUDA 13.4
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- PyTorch 2.15 ships new distributed backend and low-precision inference updates
PyTorch Blog announced PyTorch 2.15, making the rewritten NCCL backend the default for every NCCL process group while retaining legacy rollback options. The release also adds one-sided torch.distributed transports, FSDP2 and pipelining controls, faster NVFP4 decoding on NVIDIA Blackwell, and FP8 and decode optimizations for Apple Silicon. Platform support expands to Python 3.11–3.15, CUDA 13.2 and 13.4, ROCm 10.0, AMD, and Intel GPUs.
PyTorch Blog · 🔥 0 - CUDA 13.4 Brings Memory-Enforced GPU Sharing to Kubernetes
NVIDIA Technical Blog says CUDA 13.4 adds transparent, application-aware memory enforcement for GPU time-sharing while retaining the SM-sharing compute mode available through Multi-Process Service. Workloads can request an exact memory allocation or a guaranteed minimum with optional burst capacity, and the new open-source GPU Fractioning Operator applies those limits and compute modes in Kubernetes through pod annotations. NVIDIA also says KAI Scheduler v0.18.0 can select GPU fractions and write the required annotations, though no independent test results were supplied.
NVIDIA Technical Blog · 🔥 0
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