azure openai hit by gpu crunch: fine-tuning grinds to a halt
redmond, Tuesday, 1 July 2025.
developers are reporting significant delays in fine-tuning models like gpt-4.1 on openai’s azure platform. some users are experiencing fine-tuning processes exceeding 24 hours, even with small datasets. this bottleneck stems from gpu capacity shortages, impacting ai development timelines. the issue highlights the intense demand for gpus and the resource allocation challenges faced by cloud-based ai platforms. recommended regions for gpt-4.1 fine-tuning include West US 3 and Sweden Central. if a fine-tuning job remains unchanged after 24-36 hours, users should cancel and resubmit it.
impact on ai development and project timelines
The gpu shortage on azure could significantly impact developers and companies that rely on the platform for ai model training [1]. Extended fine-tuning times can disrupt project timelines and increase operational costs [1]. This situation underscores the critical need for efficient resource management in cloud-based ai platforms to ensure consistent performance and prevent delays [GPT]. The delays do not affect cost or model quality [1]. Microsoft engineers have confirmed that the delay is expected [1].
microsoft’s response and azure’s queuing system
Azure’s architecture allows only one training job to run per resource at a time, with a maximum of 20 jobs queued [1]. Azure will keep trying until the 720-hour hard timeout [1]. A prolonged ‘running’ status often indicates that the job is waiting in the regional queue due to limited gpu capacity [1]. During peak periods, even small datasets can remain in the queue for 6 to 24 hours, or sometimes longer, before processing begins [1]. This queuing system, while designed to manage resources, contributes to the delays experienced by users [1].
the broader market context: nvidia’s dominance and alternative solutions
Nvidia remains a dominant player in the gpu market, but faces capacity constraints [7]. Morgan Stanley noted that strong demand for alternative architectures is partly driven by inference shortages [7]. OpenAI has begun using Google’s tpu chips to support chatgpt and other products, marking its first large-scale adoption of non-nvidia chips [7]. This move aims to reduce inference computing costs and alleviate dependence on microsoft data centers [7]. Despite not having the most advanced version, openai’s choice to use tpus highlights google’s leadership in the broader asic ecosystem [7].
amazon aws’s competitive positioning
Notably, amazon aws is absent from openai’s list of cloud service providers for ai workloads [7]. Morgan Stanley suggests this absence may reflect amazon’s capacity constraints or the competitiveness of its trainium chips [7]. The decision by openai to use older generation tpus over trainium could negatively impact aws’s trainium custom silicon [7]. Investors will likely pay close attention to aws’s growth and expected acceleration in the second half of the year [7]. Crusoe and redwood recently partnered on a data center powered by used batteries [8].