- Service code
- BH-RES-05
- Family
- Research
- Cadence
- Per engagement; re-characterised on material workload, model or framework change
- Response
- Measurement plan agreed within 5 business days; profile delivered to agreed schedule
Overview
Sizing errors trace back to a workload that was never properly measured. Teams plan against peak assumptions, average-case estimates or a single representative job — and then discover that their real distribution has a long tail of small jobs, a straggler problem, or a communication pattern that makes the intended topology wrong. This product measures the workload directly and produces the characteristic profile that sizing, topology and scheduling decisions should be built on.
What is covered
- Job mix analysis: size distribution, duration, arrival pattern, queueing behaviour
- Compute profile: arithmetic intensity, achieved versus peak FLOPs, model-FLOPs and hardware-FLOPs utilisation
- Memory and bandwidth profile: capacity pressure, HBM utilisation, host-device transfer volume
- Communication profile: collective patterns, all-reduce volume, synchronisation stalls, straggler attribution
- Storage and data pipeline profile: ingest rate, checkpoint volume and duration, cache effectiveness
- Sensitivity analysis: which characteristic most constrains throughput at scale
- Topology fit assessment — whether the intended fabric matches the measured communication pattern
Deliverables
- Workload characteristic profile: the measured distribution across compute, memory, communication and storage
- Bottleneck attribution ranking the constraints on achieved throughput
- Sizing model translating the profile into accelerator, interconnect and storage requirements
- Topology fit assessment with recommendation
- Baseline for regression detection as models and frameworks evolve
Who this is for
Teams about to size or re-topologise a cluster, or whose achieved utilisation is well below the hardware they already own.
Specification
- Measurement level
- Job, node, accelerator, interconnect link, storage path
- Utilisation metrics
- Achieved vs peak FLOPs, model-FLOPs and hardware-FLOPs utilisation
- Straggler analysis
- Per-rank timing, synchronisation stall attribution
- Topology assessment
- Rail-optimisation fit, domain size, congestion exposure
- Output
- Profile, bottleneck ranking, sizing model, topology recommendation
Related services
| Service | Scope |
|---|---|
| Accelerator Fleet Lifecycle Maintenance | Node-level servicing for accelerator fleets, measured against the metric that matters: good GPU-hours delivered. |
| Inference Economics Benchmarking | Cost per served token, measured on your model and your traffic — not on a vendor benchmark. |