INT8, FP16, BF16, and FP8 Format Selection: Reports and Metrics

Reports and metrics

Reports and Metrics 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.

A useful report explains why movement happened, not only that movement happened.

Evidence matrix

EVIDENCE MATRIX - INT8, FP16, BF16, and FP8 Format Selection

+-----------------------------+--------------------------------+--------------------------------+---------------------------+
| Evidence                    | Tells you                      | Does not prove                 | Next action               |
+-----------------------------+--------------------------------+--------------------------------+---------------------------+
| occupancy + timeline traces | where utilization is lost      | precise root cause             | map to memory and schedule|
| cache/SRAM/bandwidth stats  | data movement pressure         | model-level quality impact     | correlate with quality run|
| counter + profile alignment | bottleneck class confidence    | rollout safety                 | run full regression matrix|
| thermal/power telemetry     | sustained operating envelope   | correctness closure            | pair with verification    |
| before/after scenario pack  | mitigation movement            | long-tail stability            | execute guardrail replay  |
+-----------------------------+--------------------------------+--------------------------------+---------------------------+

• Track Delivered tokens or inferences per joule at fixed quality threshold across representative model slices. on representative production workloads.

• Include build/runtime metadata in every report header.

• Correlate throughput, latency, and quality before rollout decisions.

• Call out contradictory evidence explicitly.

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.

Report interpretation

Precision mode sets the balance between arithmetic throughput, memory bandwidth demand, and numerical robustness. INT8 can offer the highest raw efficiency when activation ranges are stable and calibration is strong, while FP16 and BF16 provide wider dynamic behavior for workloads sensitive to quantization noise. FP8 introduces additional throughput and bandwidth gains with careful scaling and mixed-precision accumulation to control error growth. Teams should compare these formats using workload-level quality gates, overflow and clipping telemetry, and operator-level sensitivity maps rather than peak compute claims. A useful explanation always ties observed symptom to a repeatable path where useful work was blocked, delayed, or diluted by overhead.

Use Delivered tokens or inferences per joule at fixed quality threshold across representative model slices. as an alarm, then anchor action using hard evidence such as Precision decision table mapping operator classes to preferred compute and accumulation formats with quality guardrails..

Precision policy is a system-level contract between quality, latency, and thermal limits. Senior reviews expect a chain of proof: workload intent -> mapping -> hardware behavior -> product impact.

For INT8, FP16, BF16, and FP8 Format Selection, reports should explain why Delivered tokens or inferences per joule at fixed quality threshold across representative model slices. moved and which path consumed budget first.