Model per-channel quantization size, metadata cost, and savings instantly. Test bit widths carefully right now. Make smarter inference packaging decisions across constrained production systems.
| Scenario | Total Params | Channels | Orig Bits | Quant Bits | Scale Bits | Zero Bits | Sparsity | Approx. Quantized Size | Approx. Ratio |
|---|---|---|---|---|---|---|---|---|---|
| Small 8-bit plan | 125,000,000 | 1,024 | 16 | 8 | 16 | 8 | 0% | 119.21 MB | 2.00x |
| Small 4-bit plan | 125,000,000 | 1,024 | 16 | 4 | 16 | 8 | 0% | 59.61 MB | 4.00x |
| Medium sparse plan | 500,000,000 | 4,096 | 16 | 4 | 16 | 8 | 10% | 218.88 MB | 3.92x |
| Large deployment plan | 7,000,000,000 | 8,192 | 16 | 4 | 16 | 8 | 0% | 3.36 GB | 3.88x |
Effective Parameters = Total Parameters × (1 − Sparsity Percent ÷ 100)
Parameters per Channel = Effective Parameters ÷ Channels
Original Size = Effective Parameters × Original Precision Bits ÷ 8
Quantized Weights Size = Effective Parameters × Quantized Bits ÷ 8
Metadata Size = Channels × (Scale Bits + Zero Point Bits) ÷ 8
Safety Overhead Size = (Quantized Weights Size + Metadata Size) × Safety Overhead Percent ÷ 100
Estimated Quantized Size = Quantized Weights Size + Metadata Size + Safety Overhead Size
Compression Ratio = Original Size ÷ Estimated Quantized Size
Storage Saved Percent = ((Original Size − Estimated Quantized Size) ÷ Original Size) × 100
This formula set is practical for storage planning. It estimates quantized package size, metadata overhead, and compression efficiency for per-channel LLM weight compression.
1. Enter a scenario name so the exported file is easier to identify.
2. Add the total parameter count for the model or tensor group.
3. Enter the channel count used by your quantization pipeline.
4. Set the original precision and target quantized bit width.
5. Enter scale bits and zero point bits for each channel.
6. Add sparsity only when stored weights are truly reduced.
7. Add a safety overhead percent for packaging or container bytes.
8. Click Calculate to view the result above the form.
9. Export the result as CSV or PDF for sharing.
LLM per-channel quantization reduces model storage without changing tensor structure. It assigns a separate scale, and sometimes a zero point, to each output channel. That extra flexibility often preserves accuracy better than coarse tensor-wide quantization. Teams use it when memory is limited, download size matters, or inference hardware has strict capacity limits. This calculator helps estimate memory cost before deployment. It turns core quantization assumptions into storage numbers that are easier to review, compare, and share with engineers, researchers, and operations teams.
The calculator measures effective parameters after sparsity, original model size, quantized weight size, metadata overhead, and estimated compressed size. It also shows compression ratio, bytes per parameter, and estimated storage savings. These metrics matter during model packaging, checkpoint conversion, edge deployment, and cloud inference planning. A low bit width can shrink memory fast. However, metadata still adds cost. Per-channel methods store channel specific scaling information, so the final package is never only the raw quantized weights. This page highlights that overhead clearly.
Start with the model parameter count. Then enter the active channel count for the tensor layout you want to estimate. Choose the original precision, target quantization bits, and metadata precision. Add sparsity only when weights are actually pruned or skipped in storage. Optional overhead can represent alignment, packing, headers, or file container costs. The result block appears above the form after submission. That makes it easy to test several scenarios and export the final estimate as CSV or PDF for documentation.
This estimator is useful for LLM optimization, model compression reviews, hardware fitting, and inference budgeting. It supports quick comparisons between 8 bit, 6 bit, 4 bit, and lower precision plans. It also helps explain why two quantization schemes with the same bit width can still have different package sizes. Per-channel metadata changes the math. Use the example table as a reference, then adjust the inputs to match your checkpoint, tensor group, or deployment target. Clear estimates reduce surprises during release and benchmarking. Better planning also helps compare GPU memory targets, mobile packaging limits, and storage costs across multiple model variants.
Per-channel quantization assigns independent scaling values to each channel. Per-tensor quantization uses one scale for the whole tensor. Per-channel usually preserves accuracy better, especially for LLM weight matrices with uneven channel distributions.
This page estimates storage effects. It does not measure perplexity, latency, or task accuracy directly. Use it for memory planning, then validate quality with benchmark runs on your actual model.
Zero points are optional in some symmetric schemes. If your method uses only scales, set zero-point bits to zero. The calculator will then remove that metadata from the estimate.
Use the number of channels that receive separate quantization parameters. For many weight tensors, this is the output channel count. Match the channel definition used by your quantization pipeline.
Sparsity lowers the effective stored parameter count when pruned weights are not fully retained. If your files still keep dense tensors, leave sparsity at zero for a more realistic size estimate.
Yes. A higher metadata precision increases total storage. That effect is small on huge dense tensors, but it becomes more visible with fewer weights per channel.
The safety overhead field is optional. It can represent packing overhead, tensor headers, index data, alignment, or container level bytes that are not covered by raw quantized weight math.
Use the example rows to see typical trends first. Then enter your own model parameters, channels, and bit widths. Submit the form, review the result block, and export the scenario you want to share.
Important Note: All the Calculators listed in this site are for educational purpose only and we do not guarentee the accuracy of results. Please do consult with other sources as well.