Model Quantization Calculator

Model quantization is the process of shrinking a neural network by storing its weights in a lower-precision number format. A modern large language model (LLM) such as LLaMA-7B keeps every parameter as a 16-bit floating point value (FP16 or BF16). Quantization re-encodes those same parameters as 8-bit, 4-bit, 3-bit, or even 2-bit integers, dramatically reducing the memory footprint and the bandwidth needed to move weights from VRAM into the GPU compute units.

This model quantization calculator answers the question every practitioner asks before deploying a model: how much memory will I actually save, and what will it cost me in quality? By entering the model size in billions of parameters, the original precision, and the target precision, the calculator estimates the original disk/VRAM size, the quantized size, the compression ratio, an estimated inference speedup, and the per-group scale overhead that real quantization formats add.

Quantization matters because LLM inference is overwhelmingly memory-bandwidth bound. Most of the time spent generating a token is not arithmetic; it is the wait to stream billions of weights from GPU memory. Halving the bytes per weight roughly halves that traffic, which is why a model quantized from FP16 to INT4 can run several times faster while fitting on a consumer GPU. The calculator translates these abstract bit-widths into concrete gigabytes so you can decide whether your 7B, 13B, or 70B model will fit on an RTX 4090, an A100 40GB, or an A100 80GB.

Popular post-training quantization methods include GPTQ, AWQ, GGML/GGUF, and BitsAndBytes. They differ in how they choose scaling factors and which weights they protect, but all of them produce roughly the same storage footprint for a given bit-width, which is what this calculator estimates. The group size you choose controls how finely the quantizer adapts its scale, trading a small amount of extra memory for noticeably better accuracy.

The calculator starts from the number of parameters, computed as your model size in billions multiplied by one billion (a 7B model has 7,000,000,000 parameters). It then assigns a bytes-per-parameter value to each precision: FP32 = 4 bytes, FP16 and BF16 = 2 bytes, INT8 = 1 byte, INT4 = 0.5 bytes, INT3 = 0.375 bytes, and INT2 = 0.25 bytes.

The original size in gigabytes is the parameter count times the original bytes-per-parameter, divided by 1024³ (1,073,741,824 bytes per gigabyte). The raw quantized size uses the target bytes-per-parameter the same way. For any integer target, the calculator adds a realistic scale overhead: grouped quantization stores one FP16 scale and one FP16 zero-point per group, which is 4 bytes for every group. The number of groups is the parameter count divided by the group size, rounded up.

From those two sizes the tool derives the memory saved (original minus quantized), the savings percentage, and the compression ratio (original divided by quantized). It also reports effective bits per weight, which is the quantized size expressed back in bits per parameter and is always slightly above the nominal bit-width because of the group overhead. A theoretical speedup equals the ratio of original to target bytes, and the realistic speedup assumes you capture about 60% of that ideal because inference is not perfectly bandwidth bound.

Finally, the calculator checks the quantized size against a small table of common GPUs (RTX 3080 10GB, RTX 3090 24GB, RTX 4090 24GB, A100 40GB, A100 80GB) and lists every card whose memory is at least 10% larger than the quantized model, leaving headroom for activations and the KV cache.

Choosing a target precision is the single biggest lever in quantization. Each step down the precision ladder halves or nearly halves storage, but the quality cost grows non-linearly. The table below shows the bytes and bits each format uses per parameter, plus the rough quality impact this calculator reports for integer targets.

The effective bits per weight the calculator reports will be a little higher than the nominal value. For a 7B model quantized to INT4 with group size 128, the effective bit-width works out to about 4.25 bits, because each 128-weight group carries a 4-byte FP16 scale and zero-point. Shrinking the group size to 64 raises the effective width and storage further but typically improves accuracy, which is why the in-app tip recommends 128 as a balanced default.

Note that BF16 and FP16 both occupy 2 bytes, so converting between them yields no memory savings; the real gains come from moving to integer formats. INT8 is widely regarded as near-lossless for most LLMs, while INT4 is the practical floor for many production deployments where quality must stay high.

Once you have a quantized size, three numbers tell you whether the trade is worth it. The compression ratio shows how many times smaller the model became; an FP16-to-INT4 conversion lands near 3.7x once group overhead is counted, slightly below the naive 4x because of the scale bytes. The memory saved in gigabytes is the most actionable figure for fitting a model onto a specific card.

The realistic speedup reflects that token generation is memory-bandwidth bound but not perfectly so. The calculator takes the theoretical bandwidth ratio (original bytes divided by target bytes) and keeps 60% of the improvement above 1x. So an INT4 target with a theoretical 4x ceiling reports a realistic 2.8x, while INT8 with a theoretical 2x reports 1.6x. These are decode-time estimates; prompt processing (the prefill stage) is more compute bound and benefits less.

The compatible GPUs list applies a 10% headroom factor so you are not warned that a model "fits" when it leaves no room for activations, the KV cache, or CUDA overhead. If your quantized model is 3.46 GB, every listed card from the RTX 3080 upward will accommodate it comfortably; a 36 GB INT4 70B model, by contrast, would require an A100 80GB. Use these results alongside a KV cache calculator to budget the full runtime memory, not just the weights.

Remember that the quality figures are estimates. Real perplexity and accuracy depend on the model architecture, the calibration dataset, and the specific quantization method (GPTQ, AWQ, GGUF, or BitsAndBytes). Treat the perplexity-increase and accuracy-drop ranges as planning guidance, then validate on your own evaluation set before shipping.

Quantization unlocks three practical wins. First, cost: a model that fits in 24 GB instead of 80 GB can run on a single consumer GPU rather than a data-center accelerator, cutting hourly cloud bills and capital expenditure. Running a quantized 13B model locally can replace a paid API tier entirely for many workloads, turning a recurring $ expense into a fixed hardware purchase.

Second, throughput: because fewer bytes cross the memory bus per token, a quantized model serves more tokens per second and supports larger batch sizes within the same VRAM budget. This directly raises the number of concurrent users a single GPU can handle, improving the economics of self-hosted inference.

Third, accessibility: 4-bit quantization is what makes it feasible to run a 7B or 13B assistant on a laptop or a single RTX 4090. The GGUF format popularized by llama.cpp, for example, relies on this exact memory math to let hobbyists run capable models offline. The model quantization calculator gives you the numbers to plan all three of these outcomes before you download a single checkpoint.

The trade-off is always quality. Every bit you remove forces the quantizer to represent a wider range of weight values with fewer distinct levels, which introduces rounding error. Smart methods minimize that error by protecting the most important weights and choosing scales carefully, but there is no free lunch. The art of deployment is finding the precision and group size that keep your task accuracy acceptable while delivering the memory and speed you need.