INT8, FP16, BF16, and FP8 Format Selection: Debug Playbook

Debug playbook

Debug Playbook 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.

1. Freeze workload seed, model revision, and execution environment.

2. Locate first persistent stage where metrics diverge.

3. Classify dominant mechanism: compute, memory, scheduling, precision, or thermal.

4. Build one focused reproducer and apply one bounded fix.

5. Re-run full correctness, quality, and performance matrix.

Review memo template

ACCELERATOR REVIEW MEMO - Power & Precision Tradeoffs / INT8, FP16, BF16, and FP8 Format Selection

1. Symptom
   - Failing metric: Delivered tokens or inferences per joule at fixed quality threshold across representative model slices.
   - Workload or traffic slice: <name>
   - First failing layer or stage: <operator, schedule, memory, runtime>
   - Build and runtime tags: <compiler/firmware/runtime/hardware>

2. Mechanism hypothesis
   - Primary mechanism: 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.
   - Competing hypotheses: <dataflow mismatch, memory stalls, precision drift, thermal limits>
   - Missing evidence: <counter packet, trace, replay, signoff data>

3. Proposed action
   - Smallest reversible change: <mapping/runtime/policy/config>
   - Expected movement: <throughput, p99 latency, perf-per-watt>
   - Regression risk: correctness, quality, thermal, software compatibility

4. Signoff
   - Required artifact: Precision decision table mapping operator classes to preferred compute and accumulation formats with quality guardrails.
   - Required owners: accelerator architect, numeric methods owner, compiler quantization lead, inference performance owner
   - Final decision: ship, bounded rollout, rollback, or escalate

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.

Principal accelerator review addendum

INT8, FP16, BF16, and FP8 Format Selection should be framed as a full-system behavior, not an isolated kernel trick. Production outcomes are set by model shape mix, compiler choices, runtime queueing policy, memory hierarchy limits, and silicon delivery margins.

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.

Use this addendum to force explicit owner assignment, bounded fixes, and reproducible evidence before declaring closure.