How much does FP4 precision on Blackwell (B200/GB300) improve inference throughput, and does it hurt output quality?

Teams eyeing FP4 on Blackwell usually ask two questions at once: how much faster does it make inference, and what does it cost in output quality. Those are the right questions, but they do not have one fixed answer, because the throughput gain and the quality impact both depend on the model and how carefully it is quantized. FP4 precision on Blackwell chips like B200 and GB300 can improve inference throughput substantially over higher precisions such as FP8 or FP16, but the exact gain and any quality loss depend on the model and the quantization method, so FP4 is worth it only when a calibrated deployment holds output quality within your tolerance. This guide explains where the throughput comes from, why quality is not automatically hurt, and how to decide if FP4 fits your workload.

Where the FP4 throughput gain comes from

FP4 represents each number in four bits instead of the eight or sixteen used by FP8 or FP16. Fewer bits per value means more values move through memory and compute per cycle, and Blackwell's hardware is built to accelerate this format directly rather than emulate it.

The gain is real but format-dependent. FP4 roughly halves the memory footprint and bandwidth demand versus FP8 and cuts it further versus FP16, so on memory-bound inference the throughput gain can be large, while on compute-bound steps it tracks the hardware's FP4 acceleration. Because most LLM inference is memory-bound at scale, FP4 tends to help most exactly where throughput matters, which is high-concurrency serving of large models. The size of the gain still depends on your model, batch size, and whether your serving stack has optimized FP4 kernels, so treat any single throughput multiple as directional until you measure it.

Does FP4 hurt output quality

Lower precision can degrade quality, but "does FP4 hurt output" is not a yes-or-no answer. It depends almost entirely on how the model is quantized, not on the format alone.

The determining factor is method, not bit count. FP4 output quality holds up when the model is quantized carefully, using calibration, quantization-aware techniques, or selective mixed precision that keeps sensitive layers at higher precision, and degrades when a model is naively cast to four bits without those steps. A well-calibrated FP4 deployment can stay within a small quality margin of FP8 for many models, while a careless one can visibly hurt accuracy on reasoning or long-context tasks. This is why the quality question is really a quantization-quality question: the format sets the ceiling, but the method decides whether you reach it. Always validate FP4 output against your own quality benchmark before serving it in production.

Reading the FP4 tradeoff for your workload

Use the frame below to judge whether FP4 fits before you commit. At least one column is measurable on your own model.

Factor	Favors FP4	Argues for higher precision
Workload type	Memory-bound, high-concurrency serving	Small models where compute dominates
Model sensitivity	Robust to quantization	Reasoning or long-context accuracy critical
Quantization effort	Calibration and QAT available	No time to calibrate or validate
Quality tolerance	Small margin acceptable	Zero measurable regression allowed

The rule is to treat FP4 as a throughput lever you validate, not a free upgrade. When your workload is memory-bound serving of a quantization-robust model and you can calibrate properly, FP4 often delivers a large throughput gain at negligible quality cost. When accuracy is unforgiving or you cannot validate the quantization, a higher precision is the safer default until you have measured the tradeoff.

Deploying FP4 inference on Blackwell with GMI

Since the FP4 tradeoff is model-specific, the practical step is testing it on real Blackwell hardware and measuring both throughput and quality. We are an AI-native GPU and inference cloud that publishes dedicated NVIDIA GPU list pricing and carries Blackwell-generation hardware, so you can benchmark an FP4 deployment against a higher-precision baseline directly.

We currently list B200 at from $4.00 per GPU-hour under Limited Availability and GB200 NVL72 at from $8.00 per GPU-hour, with GB300 NVL72 shown as pre-order, so you can serve your model in FP4 on Blackwell silicon and measure the throughput gain and quality impact against your own FP8 or FP16 baseline. Verify current rates and availability on our GPU infrastructure (https://www.gmicloud.ai/en/gpus) and pricing (https://www.gmicloud.ai/en/pricing), since Blackwell pricing and stock move quickly. Run FP4 at your production batch size and concurrency, and validate output against a quality benchmark before rollout, because that is what separates a real gain from a hidden regression. When the workload is sustained production serving, our Prime Inference (https://www.gmicloud.ai/en/models/prime-inference) provides reserved GPU capacity with single-tenant isolation and warm serving, so your FP4 throughput reflects steady traffic rather than cold-start overhead. Start a test in our console (https://console.gmicloud.ai).

Use FP4 as a lever you validate, not a default

If you enable FP4 for the throughput and skip the quality validation, you risk shipping a quantization that quietly degrades output on the tasks you care about. Treat it as a measured tradeoff: expect a substantial throughput gain on memory-bound serving of large models, invest in calibration or quantization-aware techniques to protect quality, and validate FP4 output against your own benchmark before production. On Blackwell, FP4 can deliver large gains at small quality cost when done carefully, but the number and the tradeoff are yours to measure, not to assume from the format.