INT8, FP16, BF16, and FP8 Format Selection: Interview Drills

Interview drills

Interview Drills 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.

PROMPT
You observe regression in Delivered tokens or inferences per joule at fixed quality threshold across representative model slices. for INT8, FP16, BF16, and FP8 Format Selection. Explain root cause and release decision.

STRONG ANSWER
1. Defines workload and first failing mechanism.
2. Explains 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.
3. Requests proving artifact: Precision decision table mapping operator classes to preferred compute and accumulation formats with quality guardrails.
4. Proposes bounded fix + owner + rollback-safe validation.

WEAK ANSWER
Gives generic optimization ideas without mechanism proof or ownership.

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.

Interview answer expansion

A strong answer on INT8, FP16, BF16, and FP8 Format Selection names the workload symptom, explains 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.), and proposes one measurable validation plan.

Then it identifies owner and fallback action if the proposed fix under-delivers.

The goal is practical engineering reasoning, not keyword listing.