AI Inference Sizing (FLOPs / TFLOPs / TOPS)

Estimate required compute for Vision, Encoder (BERT-style), or LLM workloads — with utilization & headroom.

1) Workload & Target

Workload type

Preset (optional)

2) Overheads & Assumptions

Headroom multiplier (x)

Covers kernels, framework overhead, scheduling, etc. (1.5–3× typical).

Sustained utilization (%)

Real workloads often hit 30–70% of peak due to memory/bandwidth limits.

Precision (for “TOPS” display)

We’ll show core results in TFLOPs; the TOPS figure is a rough INT8 equivalence.

Concurrent streams (optional)

Multiply requirements if you need multiple simultaneous inferences.

Results

Required Sustained Compute

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TFLOPs sustained (after utilization & headroom)

Peak Budget (INT8 “TOPS” approx)

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Peak device capability target (rule-of-thumb)

Workload Breakdown

Heads-up: Memory bandwidth and kernels often cap real performance before raw TOPS/TFLOPs. Use this as a sizing guide, then validate with your actual model on target hardware.