A data science team wants to train a large language model but lacks on-premises GPU infrastructure. Which development has most directly lowered the barrier for teams in this position?
- AThe introduction of DPUs that offload networking and storage processing from CPUs
- BThe release of open-source model weights and cloud-based GPU compute services Correct
- CWider adoption of NVLink interconnects inside GPU servers
- DStandardisation of InfiniBand networking for distributed training clusters
Why A is wrong: DPUs (data processing units) improve data-centre efficiency by offloading networking and security tasks, but they do not give a team without GPUs access to training-scale parallel compute.
Why B is correct: Open model releases (such as Llama and Mistral) combined with cloud GPU services (on-demand A100 or H100 instances) let teams fine-tune or build on existing models without capital investment in hardware.
Why C is wrong: NVLink is a high-bandwidth GPU-to-GPU interconnect that improves multi-GPU training throughput within a node. It is a hardware feature inside a server, not something that reduces entry barriers for teams without infrastructure.
Why D is wrong: InfiniBand reduces latency in large GPU clusters and benefits teams that already have infrastructure. It does not help a team that has no on-premises GPUs at all.