Model Quantization Savings Calculator

JJ Ben-Joseph headshotReviewed by: JJ Ben-Joseph

Model parameters

Provide the model size, original precision, quantized precision, and baseline inference cost assumptions. The calculator returns updated memory use, latency, and estimated cost per thousand tokens.

Enter model details to see quantization impacts on memory, speed, and cost.

How quantization reshapes model footprints

Quantization trades numerical precision for efficiency. Weights stored at lower bit depths consume less memory and move through caches faster, often reducing inference latency. If a model has \(N\) billions of parameters at \(b_o\) bits each, its approximate memory requirement is:

Mo = N × bo 8  GB

Quantizing to \(b_q\) bits changes the memory to \(M_q = N × b_q / 8\). The savings \(S\) and percent reduction follow:

S = Mo − Mq Percent = S Mo × 100

Latency improvements scale roughly with the bit-width ratio, so the quantized latency \(L_q\) becomes \(L_q = L_o × b_q / b_o\). When inference cost is dominated by compute, the cost per thousand tokens is

C1k = K × Lq × 1000 3600

where \(K\) is the hourly hardware cost. These equations simplify reality—calibration data, dequantization, and layer-specific behavior also matter—but they provide a quick north star for sizing hardware or budgeting inference workloads.

Example 7B parameter model (16-bit to 8-bit)
Metric Original Quantized
Memory (GB) 14.00 7.00
Latency (ms/token) 30.0 15.0
Cost per 1k tokens ($) 0.021 0.010

Dive deeper by pairing this calculator with the Cloud GPU Rental Cost Calculator, the Model Ensemble Inference Cost Calculator, and the Batch Inference Throughput & Latency Calculator to evaluate hardware provisioning, ensemble strategies, and workload batching in tandem with quantization.

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