schema_version = "1.1"

[task]
name = "mle-bench/nvfp4-quantize"
description = "Producing NVFP4 is harder than consuming it: the per-tensor global scale has to be known before any block scale can be formed, each 16-element block then gets its own E4M3 scale, every element is rounded onto the eight E2M1 magnitudes, and two codes are packed per byte. Dynamic NVFP4 activation quantisation runs this on every activation tensor of a Blackwell-served model, and it is pure memory traffic — the arithmetic is a handful of comparisons per element."
authors = []
keywords = ["mle", "kernel-generation", "nvfp4", "fp4", "quantization", "memory-bound", "low-precision", "blackwell"]

[metadata]
suite = "mle-bench"
group = "kernel-generation"
level = "1.0"
difficulty = "hard"
category = "mle"
tags = [ "mle", "kernel-generation", "kernels", "gpu", "real-world",]

[verifier]
timeout_sec = 1800.0

[agent]
timeout_sec = 14400.0

# GPU request: honored by Modal/GKE/Daytona. On the local docker backend set environment.override_gpus: 0
# in the job config and attach a GPU via configs/gpu_overlay_nvidia.yaml (see RUNNING.md §5).
# Profiling: `ncu` needs GPU performance counters — the runner must add the SYS_ADMIN capability
# (docker `--cap-add SYS_ADMIN`) or the host must set NVreg_RestrictProfilingToAdminUsers=0.
# Without it ncu exits with ERR_NVGPUCTRPERM. `nsys` works without any extra capability.
[environment]
build_timeout_sec = 3600.0
cpus = 8
memory_mb = 65536
storage_mb = 40960
gpus = 1
network_mode = "public"
mcp_servers = []

[verifier.env]

[environment.env]

[solution.env]