INT8, FP16, BF16, and FP8 Format Selection: Inputs and Outputs

Inputs and outputs contract

Inputs and Outputs for INT8, FP16, BF16, and FP8 Format Selection is anchored on Delivered tokens or inferences per joule at fixed quality threshold across representative model slices.. Convert measurements into mechanism-backed decisions with clear owner accountability.

INPUTS
  - workload profile and SLA target
  - model precision and quality thresholds
  - compiler/runtime/firmware metadata
  - hardware operating envelope assumptions

OUTPUTS
  - evidence-backed bottleneck classification
  - owner-signed mitigation proposal
  - validation matrix with rollback triggers
  - release recommendation

Ownership split

OWNERSHIP LAYERS - INT8, FP16, BF16, and FP8 Format Selection

+----------------------+--------------------------------+--------------------------------+
| Team                 | Primary responsibility         | Closure artifact               |
+----------------------+--------------------------------+--------------------------------+
| accelerator architect | mechanism and architecture intent| design rationale + tradeoffs   |
| numeric methods owner | mapping, runtime, and execution   | profile traces + bottleneck map|
| compiler quantization lead | correctness, risk, and signoff    | test report + closure memo     |
+----------------------+--------------------------------+--------------------------------+

AI accelerator deep dive

Precision and DVFS policy must be co-designed with quality guardrails and thermal behavior.

Concept diagram

PRECISION-POWER LOOP

numeric format choice -> throughput and energy
         + thermal state and DVFS policy -> sustained SLA

Metric graph

PERF/W TRADE

INT8 efficiency      █████████
BF16 stability       ██████
thermal clamp risk   ████

Metrics and artifacts to collect

• precision-mode mix

• perf-per-watt trend

• thermal clamp frequency

• quality regression monitor

Mini case study

Switching to lower precision improved nominal throughput, but thermal clamp cycles reduced sustained gains.

Debug branches

• Validate quality guardrails by slice

• Correlate thermal events to latency tails

• Audit precision fallback behavior

Senior review question

Ask: which first-principles bottleneck class explains the symptom, and what artifact proves it reproducibly?

Key takeaways

• Tie every accelerator claim to a reproducible workload slice and one primary metric trend.

• Prefer bounded fixes with clear owner and rollback boundary over broad tuning bundles.

Common pitfalls

• Optimizing synthetic kernels without production-shape validation.

• Reading average latency while ignoring p95 and p99 behavior.

• Declaring sparse or precision wins without fallback and quality evidence.

Handoff explanation

Inputs should include workload profile, model revision, compiler/runtime versions, and platform power mode.

Outputs must include actionable interpretation of Delivered tokens or inferences per joule at fixed quality threshold across representative model slices., required artifacts (Precision decision table mapping operator classes to preferred compute and accumulation formats with quality guardrails.), owner, and validation scope.

The ideal handoff packet is reproducible: fixed seeds, explicit baseline, and rejected alternatives.