schema_version = "1.1"

[task]
name = "mle-bench/megakernel-batch4-decode"
description = "The batched form of the whole-model decode megakernel: four independent sequences decode in lockstep through one persistent kernel. The weight stream is shared across all four, so the per-token roofline is a quarter of the batch-1 task and the inner multiplies become 4-row GEMMs -- which is exactly where an unfused implementation wastes the reuse."
authors = []
keywords = ["mle", "kernel-generation", "megakernel", "persistent-kernel", "decode", "batching", "low-latency", "memory-bound"]

[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 = 2700.0

[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]