Today we’re launching TorchPass — Workload Fault Tolerance. GPU failures, network disruptions, driver bugs — every fault forces a full job restart. Hours of compute, gone. TorchPass makes faults invisible to the workload. Training continues. No restarts. No lost progress.

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400 interruptions in 54 days. That was Meta's Llama 3 training run. What's your number — and do you even know? Meta published theirs. Most teams don't track it at that resolution, which means they're absorbing the same cost without the visibility to act on it. Every interruption is a rollback, a checkpoint reload, a synchronization restart — GPU-hours consumed by recovery, not by model-advancing work. At 16,000 GPUs, the math compounds fast. A cluster that fails every few hours doesn't just lose the time to recover. It loses the training progress since the last checkpoint, plus the overhead of getting all workers back to a synchronized state, plus the ripple effects on everything scheduled after it. Meta had the engineering depth to instrument, publish, and learn from that number. Most organizations running serious training workloads are flying with less visibility than that — which means the cost is there, it's just not visible yet. Knowing your interruption rate is step one. Having a fabric architecture that reduces it — and recovers faster when it happens — is the actual lever. 🔗 Live panel on the networking decisions behind AI cluster reliability: na2.hubs.ly/H05FrYQ0 #AIInfrastructure #GPUClusters #DistributedTraining #AIFabric #DataCenterNetworking

Your fabric is three networks pretending to be one. Are you designing for that — or hoping it holds? Scale-up connects GPUs within a node. Scale-out connects nodes within a pod. Scale-across carries traffic between buildings. Each has different physics, different failure modes, and a different blast radius when something goes wrong. Most clusters treat them as a unified fabric. They aren't. The assumptions that hold at 256 GPUs quietly break at 4,096 — not because the hardware changed, but because the interactions between the three layers compound at scale. A congestion event in the scale-out fabric shows up as a training stall that looks like a compute problem. A thermal event in the scale-up layer propagates in ways the scale-across layer can't see. The failure modes multiply across the seams. The teams getting this right are asking different questions before the cluster is built: What's our congestion control strategy at each layer? Where does the blast radius of a failure stop? How do we instrument across all three without three separate forensics teams? NVLink, UALink, or ESUN isn't a procurement question. It's an architectural commitment with consequences that compound. 🔗Live panel on the fabric decisions reshaping AI infrastructure economics: na2.hubs.ly/H05FrYQ0

𝟗𝟐% 𝐨𝐟 𝐭𝐡𝐞 𝐜𝐨𝐬𝐭 𝐝𝐢𝐟𝐟𝐞𝐫𝐞𝐧𝐜𝐞 𝐛𝐞𝐭𝐰𝐞𝐞𝐧 𝐆𝐏𝐔 𝐜𝐥𝐨𝐮𝐝 𝐩𝐫𝐨𝐯𝐢𝐝𝐞𝐫𝐬 𝐡𝐚𝐬 𝐧𝐨𝐭𝐡𝐢𝐧𝐠 𝐭𝐨 𝐝𝐨 𝐰𝐢𝐭𝐡 𝐆𝐏𝐔 𝐩𝐫𝐢𝐜𝐢𝐧𝐠. It’s goodput: how much of your cluster is doing useful work vs recovering from failures. SemiAnalysis modeled a 5,184 GB300 NVL72 cluster: • TorchPass: 6% goodput expense • Checkpointless: 10.53% • Checkpoint restart: 20.91% At Llama 3 scale, checkpoint restart can leave ~80% of GPUs idle after repeated failures. As @SemiAnalysis_ put it, TorchPass is “the closest we've seen to what Frontier Labs are using at this scale.” Infrastructure efficiency is becoming the real AI scaling advantage. #AIInfrastructure #GPUClusters #Goodput #FaultTolerance #semianalysislinuxwebinar