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Gang Scheduling Still Fails If It Cannot See NVLink
Starting all eight ranks together prevents a deadlock. It does not keep them inside one NVLink domai
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Put the GPU Failure Domain in the Contract, Not the SLA
An uptime percentage does not say who drains a dead GPU, who replaces it, or who may put the node ba
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The GPU Hour Is Cheap. The Queue in Front of It Is Not.
A low GPU rate that starts next week is not cheaper than a higher rate that starts tonight. Price th
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A GPU Power Cap Taxes Training Harder Than Decode
The same watt limit does not cost every GPU job the same throughput. Dense training hits the power g
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Split Prefill and Decode Only When the KV Transfer Is Cheap
Separating prefill and decode removes one kind of interference and adds a KV transfer. Split only wh
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Expert Parallelism Sends All-to-All, Not Another All-Reduce
A fabric that passed a ring all-reduce test can still stall an MoE job. Expert parallelism permutes
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Set the Checkpoint Interval from Failures, Not from Habit
A checkpoint every N minutes is a guess. The interval follows from how long a save takes and how oft
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When Reloading a KV Cache Loses to Just Recomputing It
NVMe KV-cache offload is not a default win. Short prefixes on a fast GPU are cheaper to recompute, a
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The Acceptance Rate That Should Disable Speculative Decoding
Speculative decoding pays only while drafted tokens are accepted. Measure the ratio per route, cut t
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Read the NCCL Timeout Before You Shrink the Batch
An NCCL watchdog line is four different failures. Classify rendezvous, a slow rank, RoCE congestion,
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AI Infrastructure Services vs Raw Cloud GPU Rental Cost
Comprehensive enterprise TCO comparison: full-stack AI infrastructure services vs raw hourly cloud G
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AI Infrastructure for Fraud Detection: Low-Latency Inference
Architecting dedicated AI infrastructure for real-time fraud detection: sub-50ms p99 latency SLAs, T
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AI Workload Migration From Public Cloud to Dedicated Hardware
Step-by-step engineering guide to migrating enterprise AI workloads from public cloud to dedicated b
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Fixing Memory Bandwidth Bottlenecks in Low-Utilization LLMs
Production engineering guide to resolving memory bandwidth bottlenecks in low-utilization LLM infere
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GPU Capacity Planning for AI Teams: Sizing Beyond Peak Load
Systematic GPU capacity planning methodology for AI teams: transformer compute sizing formulas, Mode
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latest articles
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Set the Checkpoint Interval from Failures, Not from Habit
-
Gang Scheduling Still Fails If It Cannot See NVLink
-
Read the NCCL Timeout Before You Shrink the Batch
-
Expert Parallelism Sends All-to-All, Not Another All-Reduce
-
The Acceptance Rate That Should Disable Speculative Decoding
-
Put the GPU Failure Domain in the Contract, Not the SLA
-
A GPU Power Cap Taxes Training Harder Than Decode
-
When Reloading a KV Cache Loses to Just Recomputing It
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The GPU Hour Is Cheap. The Queue in Front of It Is Not.
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Split Prefill and Decode Only When the KV Transfer Is Cheap