Quantization Is More Than Just Model Compression: Quality Boundaries of FP8, MXFP4, and KV Cache

Quantization is often reduced to a single sentence: compressing models from higher to lower precision to save VRAM and increase speed. However, in real-world inference systems, quantization is not a single toggle but a set of engineering trade-offs: whether to quantize weights, whether to quantize activations, whether to quantize the KV Cache, which layers must retain high precision, and which tasks will experience quality degradation first.

This is why formats like FP8 and MXFP4 have garnered attention. They are not simply about "making numbers smaller"; rather, they redraw the lines between hardware throughput, VRAM usage, and model stability.

Why INT4 Is Not a Universal Solution

The most intuitive benefit of low-bit quantization is reduced VRAM consumption. The same GPU can accommodate larger models, or support longer contexts and higher concurrency for the same model. However, the lower the bit width, the narrower the representation range, and the more likely errors are to concentrate in a few critical layers. While the model may still appear to respond, detailed reasoning, code generation, mathematical steps, and consistency in long outputs tend to degrade first.

The true engineering challenge is not "whether it can run," but "which type of request fails first after it starts running." Some models appear normal in chat tasks but begin dropping fields in structured outputs; others handle short Q&A without issue but exhibit format drift when entering long-chain reasoning.

The Value of FP8

FP8 retains the dynamic range of floating-point formats, making it more suitable than pure integer formats for handling activations and outliers. For inference services, its appeal lies in the fact that if hardware natively supports FP8, higher throughput can be achieved with minimal quality loss.

However, FP8 is not automatically safe. Different layers have varying sensitivities to precision; errors in attention layers, normalization layers, and output heads can be amplified by subsequent computations. Mature deployments typically employ mixed precision: most matrix multiplications use low precision, while a few sensitive paths retain BF16 or FP16.

The Boundaries of MXFP4

More aggressive formats like MXFP4 push the problem in another direction: whether finer-grained scaling factors can allow 4-bit representations to retain sufficient effective information. This approach suits scenarios pursuing extreme throughput but demands higher standards for calibration data, tiling strategies, and hardware support.

If calibration data only covers routine chat, the model may encounter a "quality cliff" after deployment when faced with code, mathematics, legal texts, or table extraction. A quality cliff does not mean a smooth decline in scores; rather, it signifies the sudden failure of certain capabilities.

KV Cache Quantization Is More Easily Underestimated

During long-context inference, the KV Cache often fills up VRAM before the weights do. Compressing the KV Cache can significantly increase context length and concurrency, but it directly impacts the precision of attention mechanisms when reading historical information. This may not be obvious in short texts, but errors are amplified in long documents, multi-turn conversations, and cross-segment references.

In practical deployments, KV Cache quantization can be treated as a tiered strategy: enabled for general Q&A, conservatively enabled for long-document analysis, and kept at high precision for high-value tasks. Simultaneously, monitor output consistency, citation accuracy, and format error rates.

Practical Conclusions

Quantization should be viewed as a service-level strategy rather than a one-time model conversion. First, determine the task type, then decide on the precision for weights, activations, and KV Cache respectively. Conduct small-traffic shadow evaluations before scaling up to production. A truly reliable quantization solution is not about compressing to the lowest possible precision, but clearly knowing where the quality boundaries lie as costs decrease.