Component reliability

There are many relevant terms and concepts. Here is a brain dump of them:

• FIT rate and AFR - Failure In Time rate and Annual Failure Rate
• Weibull distribution - A statistical distribution that can fit the rate at which components or systems fail in time.

In practice

Meta published reliability information from their 16K GPU training run of Llama-3.1 405b. 466 interrupts occurred over the 54-day training run, resulting in a 90% job uptime.

Of those 466, 47 were planned activities (hardware/firmware updates or changes to training). 419 were unplanned and broken down as follows:¹

Component	Category	Interruption Count	% of Interruptions
Faulty GPU	GPU	148	30.1%
GPU HBM3 Memory	GPU	72	17.2%
Software Bug	Dependency	54	12.9%
Network Switch/Cable	Network	35	8.4%
Host Maintenance	Unplanned Maintenance	32	7.6%
GPU SRAM Memory	GPU	19	4.5%
GPU System Processor	GPU	17	4.1%
NIC	Host	7	1.7%
NCCL Watchdog Timeouts	Unknown	7	1.7%
Silent Data Corruption	GPU	6	1.4%
GPU Thermal Interface + Sensor	GPU	6	1.4%
SSD	Host	3	0.7%
Power Supply	Host	3	0.7%
Server Chassis	Host	2	0.5%
IO Expansion Board	Host	2	0.5%
Dependency	Dependency	2	0.5%
CPU	Host	2	0.5%
System Memory	Host	2	0.5%

The specific issue of silent data corruption is also discussed in LLM training at scale.

Footnotes

1. The Llama-3 Herd of Models (arxiv.org) ↩