Low-precision quantization

FP8 and AWQ 4-bit weight quantization engine for GPU inference.

problem it solves
Prevents VRAM bottlenecks from limiting serving batch sizes and throughput.

What it does

What it does: Converts 16-bit floating point model weights to FP8 or 4-bit AWQ representations.

What it watches: Model weight precision, VRAM footprint, and numerical accuracy benchmarks.

When it triggers: When loading models configured for low-precision inference execution.

The Action: Halves or quarters VRAM consumption, doubling maximum serving batch capacity.

How it recovers: Accuracy validation suites verify model output against unquantized baselines.

Self-hosted model with configurable weightsrequired
Requires control over model deployment artifacts and engine.
TensorRT-LLM or vLLM AWQ/FP8 supportrequired
Hardware must support target low-precision instruction sets.

Mode	Effect on your request	What you can see
off	Not consulted. Models run in standard FP16 or BF16 precision.	No quantization stage recorded.
shadow	Evaluates quantized model outputs in background against FP16 baseline.	Stage with action=would_quantize and perplexity_delta logged.
prod	Serves inference using low-precision quantized model build.	VRAM footprint and batch size capacity logged.

Supported formats	FP8 (E4M3/E5M2), AWQ 4-bit, INT8	Hardware-accelerated quantization.
VRAM saving	50% (FP8) to 75% (INT4)	Frees memory for larger batch sizes.
Throughput gain	1.5x - 2.2x batch throughput	Reduces cost per generated token.

·FP8 requires modern GPU architecture (NVIDIA Ada Lovelace / Hopper / Blackwell).
·Extremely small 4-bit models require activation-aware quantization (AWQ) to preserve reasoning.
·Quantized weight builds must be pre-compiled or loaded from registry.