GPU Farm Operations
A GPU farm is a production system coupling power, thermal, networking, scheduling, and incident response—not an inventory of devices.
Unify capacity planning, telemetry, workload isolation, maintenance, and recovery drills in one operating model.
01
Problem definition
GPU utilization alone misses goodput loss from memory errors, network contention, and power caps.
Unclear workload ownership and failure domains let one-node incidents spread into cluster-wide queues.
- Training and inference workloads share hardware generations across teams
- Capacity, cost, and recovery levels must be forecast continuously
- A small number of managed instances meets the requirement
- Power, cooling, and network telemetry is unavailable
GPU Farm Operations: system plate
System 1
Observe GPU, memory, links, hosts, power, and job queues on one timeline.
System 2
Calibrate baselines by hardware generation and workload shape to isolate anomalies.
System 3
Connect reservation, priority, preemption, and isolation policy to user SLOs and maintenance windows.
System 4
Regularly drill node isolation, checkpoint recovery, and reserve-capacity failover.
- N1 N2context
- N2 N3decision
- N3 N4evidence
- The workflow begins with Observe GPU, memory, links, hosts, power, and job queues on one timeline..
- It reaches an acceptance decision through Workload goodput and queue delay.
03
Design method
A GPU farm is a production system coupling power, thermal, networking, scheduling, and incident response—not an inventory of devices.
- 01
Stage 1
Observe GPU, memory, links, hosts, power, and job queues on one timeline.
- 02
Stage 2
Calibrate baselines by hardware generation and workload shape to isolate anomalies.
- 03
Stage 3
Connect reservation, priority, preemption, and isolation policy to user SLOs and maintenance windows.
- 04
Stage 4
Regularly drill node isolation, checkpoint recovery, and reserve-capacity failover.
04
Application scenarios
Training and inference workloads share hardware generations across teams
GPU utilization alone misses goodput loss from memory errors, network contention, and power caps.
- APPROACH
- Observe GPU, memory, links, hosts, power, and job queues on one timeline.
- BOUNDARY
- Nominal FLOPS is not used as actual workload throughput.
Capacity, cost, and recovery levels must be forecast continuously
Unclear workload ownership and failure domains let one-node incidents spread into cluster-wide queues.
- APPROACH
- Calibrate baselines by hardware generation and workload shape to isolate anomalies.
- BOUNDARY
- Adding hardware is not a substitute for operating design.
05
Design choices
| Decision | Gain | Cost | Watch |
|---|---|---|---|
| Training and inference workloads share hardware generations across teams | Observe GPU, memory, links, hosts, power, and job queues on one timeline. | Nominal FLOPS is not used as actual workload throughput. | Workload goodput and queue delay |
| Capacity, cost, and recovery levels must be forecast continuously | Calibrate baselines by hardware generation and workload shape to isolate anomalies. | Adding hardware is not a substitute for operating design. | Hardware and link anomaly detection time |
GPU Farm Operations: system plate
- A small number of managed instances meets the requirement
Nominal FLOPS is not used as actual workload throughput.
- SIGNAL
- Workload goodput and queue delay
- MITIGATION
- Calibrate baselines by hardware generation and workload shape to isolate anomalies.
- Power, cooling, and network telemetry is unavailable
Adding hardware is not a substitute for operating design.
- SIGNAL
- Hardware and link anomaly detection time
- MITIGATION
- Connect reservation, priority, preemption, and isolation policy to user SLOs and maintenance windows.
- The workflow begins with Observe GPU, memory, links, hosts, power, and job queues on one timeline..
- It reaches an acceptance decision through Workload goodput and queue delay.
07
Validation plan
| Measure | Method | Pass condition | Caveat |
|---|---|---|---|
| Workload goodput and queue delay | Observe GPU, memory, links, hosts, power, and job queues on one timeline. | Repeated runs satisfy the acceptance threshold agreed during discovery | Nominal FLOPS is not used as actual workload throughput. |
| Hardware and link anomaly detection time | Calibrate baselines by hardware generation and workload shape to isolate anomalies. | Repeated runs satisfy the acceptance threshold agreed during discovery | — |
| Recovery time and capacity forecast error | Connect reservation, priority, preemption, and isolation policy to user SLOs and maintenance windows. | Repeated runs satisfy the acceptance threshold agreed during discovery | — |
08
Constraints and failure conditions
A small number of managed instances meets the requirement
Nominal FLOPS is not used as actual workload throughput.
Power, cooling, and network telemetry is unavailable
Adding hardware is not a substitute for operating design.
09
Engagement model
- 01
Diagnosis
PattyAnalyze the current system and its failure signals.
ClientProvide representative work, data boundaries, and operating constraints.
Workload goodput and queue delay - 02
Design
PattyCalibrate baselines by hardware generation and workload shape to isolate anomalies.
ClientConfirm owners and acceptance criteria.
Hardware and link anomaly detection time - 03
Validation
PattyConnect reservation, priority, preemption, and isolation policy to user SLOs and maintenance windows.
ClientMake the production-transition or stop decision.
Recovery time and capacity forecast error
10
Durable deliverables
- GPU Farm Operations decision record
- A GPU farm is a production system coupling power, thermal, networking, scheduling, and incident response—not an inventory of devices.Client-owned · Patty-reviewed
- Validation harness and acceptance criteria
- Workload goodput and queue delay · Hardware and link anomaly detection time · Recovery time and capacity forecast errorJointly maintained
- Operations and recovery runbook
- Nominal FLOPS is not used as actual workload throughput. · Adding hardware is not a substitute for operating design.Operating-team owned
11
Terminology
- GPU Farm Operations
- Unify capacity planning, telemetry, workload isolation, maintenance, and recovery drills in one operating model.
- Acceptance criterion
- Workload goodput and queue delay
- Operating boundary
- Nominal FLOPS is not used as actual workload throughput.
REFERENCES
References and primary material
- NVIDIA DCGM
Primary material for the method and terminology.
- Kubernetes Device Plugins
Primary material for the method and terminology.