You need a released bf16 model at half the footprint. Walk through quantizing it yourself.

The conversion is the easy part and the evaluation is the project. What calibration data does and why yours should come from production, the per-category evaluation that catches what an average hides, and the parts of the model that should not be quantized at all.

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The concepts behind this question

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Foundational

🧮 Open Weights & Serving Engines

Weight Formats: FP8 Blocks, MXFP4 and AWQOpen-weights models now ship pre-quantized, and the format is part of the release rather than something you choose afterwards. Block-scaled FP8 gives one byte per parameter with a scale per tile. MXFP4 gives about 0.53 bytes by pairing four-bit values with a shared exponent every 32 elements. Integer schemes like AWQ reach similar sizes with a different error profile. What decides a deployment is not which is most accurate in the abstract but which one the model was released and evaluated in, and which one your engine and hardware can execute natively.

Foundational

🧮 Open Weights & Serving Engines

Serving Benchmarks That Do Not LieMost published serving numbers are not comparable to each other and not predictive of production, because they differ in the input distribution, the concurrency, whether the cache was warm, and which of several very different metrics is being reported. A benchmark that supports a decision has to fix all four, report a distribution rather than a mean, and be run against the traffic shape you actually serve. The single most useful discipline is to compute the bandwidth bound first, so you know what fraction of the possible you achieved.

Advanced

🚀 Inference & Serving🔒 Premium

Quantization for InferenceQuantization stores weights, and sometimes activations and the KV cache, in fewer bits, which cuts the bytes a decode step has to stream and the memory a model occupies. Weight-only int4 (GPTQ, AWQ) is a capacity and single-stream latency play; fp8 for weights and activations (W8A8) doubles tensor-core throughput and helps prefill and large batch; fp8 KV cache doubles context per GPU. Each has an accuracy cost you measure rather than assume, and knowing which one to reach for from the bottleneck is the interview question.

Core

🧩 GPU & Accelerator ArchitectureSign in

Numerics: FP32, BF16, FP8 and FP4Every number format is a trade between range (exponent bits), precision (mantissa bits) and throughput (fewer bits, more values per cycle through the tensor cores). bf16 won training because it keeps fp32's range; fp8 splits into E4M3 for precision and E5M2 for range and needs scaling factors; fp4 needs block scaling and careful outlier handling. Knowing which format goes where, and why accumulation stays fp32, is what the numerics question is really asking.

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Next in this trackDesign a deployment that serves a trillion-parameter model at a million tokens of context with usable latency.Next in this trackThe open-weights model you are deploying is multimodal. What changes about serving it?Next in this trackYour agent product's tool-call success rate fell from 94 to 61 percent after a deploy. Debug it.

FEDITOR'S NOTE

Scored on calibration data coming from production traffic, on per-category evaluation against a reference, and on the modules that stay in higher precision.

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