By VRLA Tech  ·  AI Computing  ·  April 2026

LLM inference precision — FP16, FP8, or FP4 — determines three things simultaneously: how much VRAM the model requires, how fast tokens are generated, and the quality of the model’s outputs. These are not independent levers. Lower precision reduces VRAM usage and increases throughput while introducing quantization error that may degrade output quality. Choosing the right precision for your deployment is a practical engineering decision that affects hardware selection, serving capacity, and application suitability.

What each precision level means

Floating point numbers are stored with a sign bit, exponent bits, and mantissa bits. More bits means more precision in representing weight values. FP16 uses 16 bits per value. FP8 uses 8 bits. FP4 uses 4 bits. Model weights are stored as millions of these floating point values, so halving the bit width approximately halves the VRAM required to store the model.

The accuracy tradeoff arises because lower-bit representations cannot express the same range of values as higher-bit representations. Small weight differences that matter for model output quality may be lost when quantizing from FP16 to FP8 or FP4. Modern quantization methods — GPTQ, AWQ, and NVIDIA’s TensorRT-LLM FP8 quantization — use calibration datasets to minimize this error, preserving quality in most applications while capturing the VRAM and throughput benefits.

FP16, FP8, FP4 compared directly

FP8: the production standard in 2026

FP8 is the default production inference precision for deployment on NVIDIA Ada Lovelace and Blackwell hardware in 2026. The quality degradation from FP16 to calibrated FP8 is minimal — typically 0.5–2% on standard benchmarks — and the VRAM reduction from approximately 140GB to approximately 70GB for a 70B model is the difference between requiring two GPUs and fitting on the RTX PRO 6000 Blackwell’s single 96GB card.

vLLM, TensorRT-LLM, and text-generation-inference all support FP8 inference with calibrated quantization as a first-class feature. Switching from FP16 to FP8 is a configuration flag in most serving frameworks.

FP4: Blackwell’s new throughput tier

FP4 is new in NVIDIA Blackwell. It is not widely deployed in production in early 2026 because calibration tooling is still maturing and quality degradation is more pronounced than FP8. However, for applications where throughput is the dominant concern — high-volume inference serving where quality is secondary to tokens-per-second-per-dollar — FP4 on Blackwell hardware offers a compelling efficiency improvement. Expect FP4 production deployments to become more common as calibration techniques improve through 2026.

Q4 GGUF quantization vs FP4/FP8

INT4/Q4 quantization in GGUF format (used by llama.cpp and Ollama) is different from hardware FP4. GGUF Q4 runs on both CPU and GPU, making it accessible without high-end GPU hardware. It produces more quality degradation than properly calibrated FP8 but makes 70B models runnable on consumer hardware with 40GB or less of VRAM through CPU offloading. Q4 GGUF is the right choice for local development on consumer hardware. FP8 is the right choice for production serving where quality and speed both matter.

Precision selection by use case

VRLA Tech configures LLM workstations for your target precision and serving framework. Browse configurations on the VRLA Tech LLM Server and Workstation page.

VRLA Tech has been building custom AI workstations since 2016. All systems ship with a 3-year parts warranty and lifetime US-based engineer support.