Quantization compresses a neural network's weights from a high-precision format, such as 16-bit floating point, into a low-precision format such as 8-bit or 4-bit integersCITE:E1. The technique lowers both the memory a model occupies and the amount of computation needed to run itCITE:E1.
Memory usage scales roughly with the number of bits used per parameter, so reducing bit-width directly reduces file sizeCITE:E2. At FP16 precision, each parameter takes up 2 bytes; after 4-bit quantization, each parameter takes up about 0.5 bytes — roughly a quarter of the original sizeCITE:E2. That reduction is what turns a model too large to load into one that runs on hardware that previously could not hold itCITE:E2.
Quantized file size directly determines which devices a model can run on, according to EffectStory's Mac local-LLM dataCITE:E3. Qwen3 235B, quantized with Q4, comes to about 142GB, while Phi-4 14B comes to about 8.9GB after quantizationCITE:E3. Quantization is described in that data as a precondition for running large models locallyCITE:E3.
| Item | Precision | Size |
|---|---|---|
| Generic weight | FP16 | 2 bytes/parameter |
| Generic weight | 4-bit | ~0.5 bytes/parameter |
| Qwen3 235B | Q4 quantized | ~142GB |
| Phi-4 14B | Quantized | ~8.9GB |
Quantization introduces precision loss, but modern 4-bit methods such as Q4_K_M limit that loss through grouped weights and calibrationCITE:E4. This is why 4-bit quantization has become a primary choice for local deployment: it trades a small amount of quality for several-fold gains in memory and speedCITE:E4.
Quantization and MoE architectures reinforce each other, according to EffectStory's frontier-LLM comparison dataCITE:E5. MoE activates only part of a model's parameters at a time to cut computation, while quantization compresses the memory footprint on top of that; combined, the two make it possible for large open-source models to run on consumer-grade hardwareCITE:E5.
Quantization cannot make every model fit: models at the 1-trillion-parameter scale can still exceed a single machine's memory capacity even after 4-bit quantizationCITE:E6. Quantization shrinks a model's file size, but it does not change how large the model fundamentally isCITE:E6.
The same arithmetic — FP16's 2 bytes per parameter dropping to roughly 0.5 bytes at 4-bit, about a quarter the original size — explains why Qwen3 235B lands at roughly 142GB while the much smaller Phi-4 14B lands at roughly 8.9GB: quantization scales file size down proportionally, it does not erase parameter countCITE:E2CITE:E3. Pairing quantization with MoE extends how far that scaling can go by cutting active computation as well as memoryCITE:E5, but the 1-trillion-parameter tier shows where the technique stops working: even at 4-bit, total parameter count there still outruns a single machine's memoryCITE:E6.
Quantization compresses a neural network's weights from a high-precision format, such as 16-bit floating point, into a low-precision format such as 8-bit or 4-b…
Memory usage scales roughly with the number of bits used per parameter, so reducing bit-width directly reduces file sizeCITE:E2.
Quantized file size directly determines which devices a model can run on, according to EffectStory's Mac local-LLM dataCITE:E3.
Quantization introduces precision loss, but modern 4-bit methods such as Q4_K_M limit that loss through grouped weights and calibrationCITE:E4.
The numbers here describe a hard boundary, not a preference. Cutting each weight from FP16's 2 bytes to roughly 0.5 bytes at 4-bit is the sole reason a 235-billion-parameter model like Qwen3 becomes a ~142GB file instead of something several times larger — and the same reason a 14-billion-parameter model like Phi-4 shrinks to about 8.9GB. The gap between those two figures also shows quantization doesn't erase parameter count, it just scales it down proportionally. Pairing quantization with MoE extends how far that scaling reaches, but the 1-trillion-parameter tier marks where the technique stops working: even at 4-bit, models at that scale still exceed a single machine's memory. The metric worth watching next is whether any trillion-parameter-class open model, quantized to 4-bit, is ever reported as fitting inside a single machine's memory — that would mark the actual edge of this technique shifting.
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