Does FP8 quantization reduce model quality?

FP8 quantization typically causes only a small quality loss compared to FP16 or BF16, and for many models the difference is close to negligible on standard benchmarks when using a properly calibrated FP8 checkpoint or per-tensor scaling, which is why NVIDIA ships FP8 as a native format for models like Llama and DeepSeek on Hopper and Blackwell hardware. The degradation tends to be more noticeable on tasks requiring precise numerical reasoning, long chains of multi-step logic, or edge cases far from the calibration data used during quantization, so it is worth evaluating on your own task rather than assuming benchmark-reported numbers transfer directly. FP8 differs from more aggressive INT4 quantization in that it keeps a floating-point representation with a real exponent, which generally preserves dynamic range better than a fixed 4-bit integer scheme. Hardware-accelerated FP8 tensor cores on H100, H200 and B200 also mean the quality trade-off comes with a real throughput and memory benefit, unlike software-only quantization approaches. Testing FP8 against FP16 on representative prompts before committing to a production deployment remains the safest approach. Nanobase AI validates FP8 accuracy against a customer's own evaluation set before recommending it for production.

Task categories where FP8 degradation is more likely

Rather than a single quality number, FP8's impact varies meaningfully by what the model is being asked to do. The categories below are ordered roughly from least to most sensitive based on how error tends to accumulate through a task.

Key takeaway: FP8 is close to safe by default for chat and summarization, and worth explicit testing for multi-step reasoning and long chains of logic.

Calibration methods and why they matter

FP8 quantization is not one procedure; the calibration method used to determine scale factors affects the resulting accuracy meaningfully. Per-tensor scaling applies a single scale factor across an entire weight tensor, which is fast and simple but can lose precision when a tensor contains a wide range of values. Per-channel scaling computes a separate scale factor for each output channel, generally preserving more accuracy at a small computational cost during quantization. Dynamic scaling recomputes scale factors at inference time based on the actual activation values seen, which adapts better to varying input distributions but adds a small runtime overhead. Models released natively in FP8 by their creators, such as DeepSeek R1 and V3, have already been calibrated and validated by the model provider, which is generally more reliable than quantizing an FP16 checkpoint yourself with default settings.

Key takeaway: how a model was calibrated to FP8 matters as much as the fact that it is FP8, so a natively released FP8 checkpoint is usually a safer starting point than a quick post-hoc conversion.

A methodology for testing FP8 on your own workload

1. Collect 50 to 100 representative prompts from your actual use case, weighted toward the task types your deployment will see most, not a generic benchmark set.
2. Run each prompt through both the FP16 or BF16 baseline and the FP8 candidate, capturing full outputs for both.
3. Score outputs using a mix of automated similarity metrics and human or LLM-based judgment on task-specific criteria, since automated metrics alone often miss subtle quality regressions.
4. Pay particular attention to any prompts involving multi-step reasoning, arithmetic, or long chains of instructions, since this is where FP8 divergence is most likely to appear.
5. Make the FP8-versus-FP16 decision per use case if your deployment serves multiple distinct task types, rather than a single blanket decision for the whole system.

Key takeaway: a structured comparison on your own prompts, weighted toward reasoning-heavy tasks, is the only reliable way to know FP8's real impact on your workload.

Why natively FP8-trained models are a different case

DeepSeek R1 and V3 were trained with FP8 in the loop rather than trained in a higher precision and quantized afterward, which means the model's own weight distributions were shaped around FP8's numeric range from the start rather than approximated after the fact. This generally produces less degradation than post-training quantization of an FP16 or BF16 checkpoint, since post-training methods have to fit a fixed set of already-trained weights into a smaller format, while native FP8 training lets the optimization process adapt to the format's constraints directly. This distinction is worth knowing when comparing a natively FP8 model's public reputation for accuracy against a model you are quantizing to FP8 yourself, since the two are not the same exercise.

Key takeaway: a model trained natively in FP8 tends to hold up better than one you quantize yourself afterward, so treat their quality claims separately.

Frequently asked questions

Is FP8 quality loss the same across all model families?

No, it varies by how a model was trained and whether it was designed with FP8 inference in mind. Models trained or released natively in FP8 tend to show less degradation than FP16 or BF16 models quantized afterward with generic tooling.

Does FP8 KV cache introduce the same risk as FP8 weights?

It introduces a related but separate risk, since KV cache quantization affects the precision of stored attention information rather than the model's parameters. Testing both independently, since serving engines usually allow them to be configured separately, gives a clearer picture of where any quality loss originates.

How much faster is FP8 than FP16 on Hopper hardware?

FP8 tensor cores on H100 and H200 offer roughly double the peak throughput of FP16 or BF16 tensor cores for the same operation, alongside half the memory footprint, which is why FP8 has become the default production choice on this hardware when accuracy validation passes.

Should I re-test FP8 quality after switching serving engines?

It is a reasonable precaution, since different serving engines and kernel implementations can apply quantization or KV cache handling slightly differently, and a configuration validated on one engine is not automatically guaranteed to behave identically on another.

How Nanobase AI helps

Nanobase AI validates FP8 accuracy against a customer's own evaluation set before recommending it for production, using a structured comparison methodology rather than relying on published benchmark claims that may not transfer to your specific tasks. This is part of our standard sizing and deployment process across H100, H200 and B200 hardware. See our solutions for the full engagement.

Ready to discuss your project? Contact Nanobase AI or email hello@bumu.tech.