quantized-export – DevOps & Infrastructure skill for AI agents

Quantized Export

The last stop after checkpoint-promotion

hands off a PROMOTE verdict: a checkpoint

that cleared the four-stage gate still isn't

deployed until it's exported in the right

format for its target runtime and proven to

still work post-export. A REJECT verdict

never reaches this skill — export starts only

from a promoted checkpoint.

Input: a promoted checkpoint (or LoRA

adapter) plus the target deployment surface —

GPU class, serving stack, and whether

long-context/code/math workloads are in

scope.

Output format: an exported artifact in

the chosen format plus a smoke-test diff

report comparing 3–5 golden outputs

pre-export and post-export.

Format Map

Pick format by hardware and deployment shape,

not by habit — the wrong pick either wastes

throughput headroom or breaks silently on

specific workloads (see Workload Overrides).

• **FP8 is the default on Hopper-class GPUs

and newer.** It preserves near-bf16 quality

at roughly half the memory, and it's the

safe first choice whenever the target GPU

supports it and no edge-device constraint

applies.

• AWQ INT4 targets older GPUs that predate

FP8 hardware support. **GPTQ is superseded

for new deployments** — don't reach for it

on a fresh export; AWQ has better accuracy

retention at the same bit width and wider

current tooling support.

• **GGUF with Q4_K_M quantization, built from

an imatrix, is the edge/llama.cpp format.**

Use it for local or CPU-adjacent

deployment, not for GPU-serving

throughput — it optimizes for footprint,

not tokens/sec on a datacenter GPU.

• **NVFP4 is for Blackwell-at-scale

deployments only — and explicitly NOT on

GB10. NVFP4 on SM121 (GB10) runs ~32%

slower than FP8** because the hardware

lacks a native cvt.e2m1x2 path unless the

kernel is compiled sm_121a. Choosing

NVFP4 on a GB10 target is a regression, not

an upgrade — pick FP8 there instead.

• **Merged vs. LoRA-only is a separate axis

from quant format.** A merged export folds

the adapter into the base weights: larger

artifact, no base-model dependency at serve

time. LoRA-only keeps the adapter separate:

much smaller artifact, but the serving stack

must load the exact same base model

alongside it — a mismatched or

wrong-revision base silently changes

outputs. Pick merged when artifact

portability matters more than storage; pick

LoRA-only when disk footprint or multi-adapter

serving matters more.

Worked Picks

The core format-selection tradeoff, read as a

lookup table for common scenarios:

| Target | Workload | Format |

|---|---|---|

| Datacenter GPU | generic chat | FP8 |

| Datacenter GPU | long-context/code/math | FP8 or W8A8 — never INT4 |

| Older GPU generation | generic | AWQ INT4 |

| Edge device / laptop | llama.cpp serving | GGUF Q4_K_M + imatrix |

| GB10 | any workload | FP8 via vLLM nightly, or GGUF via llama.cpp locally — skip NVFP4 |

# quick decision snippet — see the table above for the full map
hopper_or_newer: fp8
older_gpu: awq-int4
edge_llama_cpp: gguf-q4_k_m+imatrix
gb10_any_workload: fp8-vllm-nightly   # never nvfp4 on GB10

Workload Overrides

The Format Map above is a default, not a rule

that survives every workload. **Long-context,

code, and math workloads break at INT4** —

quantization error compounds across long

sequences and precise token-level reasoning in

ways that don't show up on short, generic

prompts. For any of these three workload

classes, stay on FP8 or W8A8 even if the

target hardware would otherwise justify INT4

on cost grounds.

• Don't validate this override with MMLU or

similar broad-knowledge benchmarks — they

don't stress the failure mode. **Measure

with the actual task evals** — the goldens

and graders from eval-harness-first, run

through the exported artifact — because

INT4 degradation on long-context, code, or

math shows up as task-specific failures

(dropped context, broken syntax, arithmetic

errors) well before it moves a knowledge

benchmark.

• If a task eval regresses after an INT4

export on one of these three workload

classes, the fix is switching format, not

re-tuning the quantization recipe — AWQ

and GPTQ variants at the same bit width

share the same compounding-error failure

mode on these workloads.

The Smoke Test

Export bugs are silent at the file level — a

malformed export still produces a

loadable artifact, so file-existence checks

prove nothing. **The smoke test is

mandatory for every export, with no

exception for a format that "should just

work":**

1. **Load the exported artifact in its actual

target runtime** — vLLM for FP8/AWQ,

llama.cpp for GGUF, not a quick

sanity load in a different framework than

the one that will serve it in production.

2. Run 3–5 golden prompts through it —

pull these from the same eval/goldens.jsonl

eval-harness-first maintains, not a fresh

ad hoc set.

3. **Compare each output against the

pre-export generation** for the same

prompt, same deterministic sampling

settings — greedy decoding (temperature 0)

and a fixed seed, persisted and reused

between the pre- and post-export runs, not

just nominally identical config. **For a

lossless export, byte match is the gate —

any diff is a bug. For a lossy**

(quantized) export, byte match is expected

to fail; the gate is task-grader verdict

agreement instead — see

references/export-commands.md's

Smoke-Test Script Skeleton.

Run this as a gate, not a manual check:

python smoke_test.py "$EXPORT_PATH" \
    eval/goldens.jsonl pre-export-outputs.jsonl
# non-zero exit on any pre/post mismatch

Failure Signatures

What export bugs actually look like, not a

clean pass/fail flag:

• Template mismatch presents as garbled or

run-on output — the chat template baked

into the export doesn't match the one the

checkpoint was trained and evaluated

against, so turn boundaries or special

tokens land in the wrong place.

• Wrong quantization applied to lm_head

presents as off-template or semantically

nonsensical output that still looks

fluent — the output head lost precision it

needed even though the rest of the network

quantized cleanly.

Never ship an export that skipped this step —

a checkpoint's PROMOTE verdict says the

un-exported checkpoint is good; it says

nothing about the export pipeline. Re-run on

any quant-method or runtime version bump, not

only after the first export. Runnable command

sequences for every format plus the

smoke-test script skeleton:

references/export-commands.md.

Related Skills

• checkpoint-promotion — the only valid

upstream source for this skill. A checkpoint

without a PROMOTE verdict doesn't reach

export.

• eval-harness-first — owns the

eval/goldens.jsonl this skill's smoke test

draws its 3–5 prompts from, and the task

evals the Workload Overrides section

requires for long-context/code/math

validation.

• finetuning-method-selection — its

references/model-catalog.md is the place

to check hardware-class assumptions (which

GPU generations a base model targets) before

picking a format off the Format Map above.

Spark users: on GB10, GGUF via llama.cpp

works well for local serving, and FP8 serving

via vLLM nightly builds is the other proven

path — NVFP4 is the one format to avoid there

(see the Format Map exception above). Once the

dgx-spark-ops plugin is installed, defer

Spark-specific serving and thermal questions to

its skills rather than re-deriving them here.