Neural Network Tensor Memory & Quantization Compression (FP32/BF16/INT8/INT4) Calculator

Compare FP32, FP16/BF16, INT8, and grouped INT4 weight memory and HBM latency.

Quantized Tensor Footprint (GiB)
15.37 GiB
Effective Bits per Weight (With Scales) 4.13 bits/w
FP16 / BF16 Unquantized Footprint 59.6 GiB
Single Forward-Pass Memory Read Time 15.37 ms
Inputs
B weights
Total tensor weight elements in billions.
bits
Target quantization bit-width (4, 8, 16, or 32 bits).
weights/group
Block/group size sharing an FP16 scale factor (e.g., 128 for GPTQ/AWQ).
GB/s
GPU HBM/GDDR memory bandwidth in GB/s.
Results & Graph
Quantized Tensor Footprint (GiB)
15.37 GiB
Effective Bits per Weight (With Scales)
4.13 bits/w
FP16 / BF16 Unquantized Footprint
59.6 GiB
Single Forward-Pass Memory Read Time
15.37 ms

Quantized Tensor Footprint (GiB)

Graph 1 • Tensor Elements Billions Sensitivity Recalculates the domain equation across 50% to 150% of the primary input while holding other parameters constant.
0 GiB 12.4 GiB 24.9 GiB 50%: 7.68 GiB 7.7 50% 75%: 11.53 GiB 11.5 75% 100%: 15.37 GiB 15.4 100% 125%: 19.21 GiB 19.2 125% 150%: 23.05 GiB 23.1 150%
Graph 2 • Primary & Supporting Metric Comparison Compares the primary output alongside supporting domain benchmarks.
Quantized Tensor Footprint (GiB) 15.37 GiB Effective Bits per Weight (With Scales) 4.13 bits/w FP16 / BF16 Unquantized Footprint 59.6 GiB Single Forward-Pass Memory Read Time 15.37 ms
Graph 3 • Exact Output Table & Units Displays each calculated output in its exact domain unit.
Quantized Tensor Footprint (GiB)15.37 GiBEffective Bits per Weight (With Scales)4.13 bits/wFP16 / BF16 Unquantized Footprint59.6 GiBSingle Forward-Pass Memory Read Time15.37 ms
Summary: 32B weights at 4-bit (group size 128, 4.125 eff bits/weight) occupy 15.37 GiB vs. 59.6 GiB in FP16.
Summary: 32B weights at 4-bit (group size 128, 4.125 eff bits/weight) occupy 15.37 GiB vs. 59.6 GiB in FP16.
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Formula & Mathematical Methodology: Tensor Quantization RAM Systems Engineering Formula

EffectiveBits = QuantBits + (16 / GroupSize)
SizeGiB = (Elements × EffectiveBits / 8) / 2³⁰
EffectiveBits = QuantBits + (16 / GroupSize); Size_GiB = (Elements × EffectiveBits / 8) / 2³⁰
  • tensorElementsBillions: Tensor Elements Billions (B weights)
  • targetQuantBits: Target Quant Bits (bits)
  • quantGroupSize: Quant Group Size (weights/group)
  • gpuMemoryBandwidthGbs: Gpu Memory Bandwidth Gbs (GB/s)

Rounding: Binary/decimal capacities and engineering metrics retain full floating-point precision and round to 2 decimal places.

Worked Example: Architecture Sizing Example: Tensor Quantization RAM

Compare FP32, FP16/BF16, INT8, and grouped INT4 weight memory and HBM latency.

  1. Enter the workload parameters, model dimensions, or network link specifications.
  2. Compute the sizing equation: EffectiveBits = QuantBits + (16 / GroupSize); Size_GiB = (Elements × EffectiveBits / 8) / 2³⁰.
  3. Inspect the primary capacity metric, headroom buffers, and 3 SVG visualizations.
Result: Calculates Tensor Quantization RAM via EffectiveBits = QuantBits + (16 / GroupSize); Size_GiB = (Elements × EffectiveBits / 8) / 2³⁰.

Frequently Asked Questions

How does the Neural Network Tensor Memory & Quantization Compression (FP32/BF16/INT8/INT4) Calculator compute capacity?

It evaluates EffectiveBits = QuantBits + (16 / GroupSize); Size_GiB = (Elements × EffectiveBits / 8) / 2³⁰ directly in your browser and plots three custom SVG diagrams.

Can I compare different cluster or model configurations side by side?

Yes. Use the "Compare (0/3)" button in the results panel to pin up to three configurations.

Does this tool upload any of my architecture parameters?

No. All calculations run 100% client-side in your browser.

Developer Tools · AI & LLM Infrastructure

Neural Network Tensor Memory & Quantization Compression (FP32/BF16/INT8/INT4) Calculator: inputs, method, and how to read the result

Compare FP32, FP16/BF16, INT8, and grouped INT4 weight memory and HBM latency.

Choose inputs for the case you are checking

The defaults are illustrative. Replace them with values for your case and keep the units shown beside each field.

  • Tensor Elements Billions: Total tensor weight elements in billions.
  • Target Quant Bits: Target quantization bit-width (4, 8, 16, or 32 bits).
  • Quant Group Size: Block/group size sharing an FP16 scale factor (e.g., 128 for GPTQ/AWQ).
  • Gpu Memory Bandwidth Gbs: GPU HBM/GDDR memory bandwidth in GB/s.

What the calculation includes

With the default values shown, Quantized Tensor Footprint (GiB) is 15.37 GiB. The calculation follows Tensor Quantization RAM Systems Engineering Formula; the formula and variable definitions above show how the inputs combine.

One-input check: With the other defaults held constant, changing Tensor Elements Billions from 32 B weights to 35 B weights changes Quantized Tensor Footprint (GiB) from 15.37 GiB to 16.81 GiB. This is a comparison inside the calculator's stated model, not a prediction.

Where to use caution

  • Uses industry-standard IEEE, IETF RFC, and GPU/cloud hardware sizing conventions.
  • Capacity planning estimate; benchmark under production traffic patterns before finalizing cluster or hardware procurement.

Scope: IEEE / IETF / Cloud Systems Engineering Standard Check the assumptions and limitations against your situation before relying on the estimate.

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