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Google Cloud A4X Max Bare Metal Instances Support 50k GPU Clusters, Network Bandwidth Doubles

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ME News Updates, April 19 (UTC+8), Google Cloud announced that its A4X Max bare-metal instance supports clusters of up to 50,000 GPUs, with network bandwidth twice that of the previous generation. This instance belongs to the Google Compute Engine accelerator-optimized machine series, which come pre-installed with NVIDIA GPUs and are designed for AI, machine learning, high-performance computing, and graphics-intensive applications. The documentation details multiple machine series including A4X Max, A4X, A4, A3, A2, G4, and G2, and recommends specific series based on workload types such as pre-training, fine-tuning, inference, graphics, and high-performance computing. Additionally, the documentation explains pricing and consumption options (on-demand, Spot, Flex-start, reserved) based on pre-installed GPUs, vCPUs, memory, and local SSDs, as well as the maintenance experience for different machine types. (Source: InFoQ)
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MossyLedger
· 27m ago
Pretraining, fine-tuning, inference—all included. Google seems to want people to be on its cloud from birth to death.
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GateUser-8da82d63
· 6h ago
Based on workload recommendations for instance types, so I don't have to calculate TFLOPS myself.
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BoredInBlockspace
· 6h ago
Reserved instances are suitable for businesses with strong determinism, but who can really say for sure that their large model training will be certain?
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GateUser-eccf92a1
· 6h ago
Google Cloud's GPU supply has finally caught up; previously, applying for A100 took forever.
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BlocktimeBarista
· 6h ago
Bare metal means no virtualization overhead, this money is well spent.
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ExitLiquidityIntern
· 6h ago
Maintaining a cluster of 50k cards... How many SREs are needed to get a good night's sleep?
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TreatMemesAsBeliefs
· 6h ago
Transparent local SSD pricing is a good thing; previously, some cloud providers' storage costs were like opening blind boxes.
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StakingDaydreamer
· 6h ago
Cloud providers have gone wild— a 50,000‑GPU cluster: are they trying to snatch supercomputing centers’ “bread and butter”?
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