hyperloom-remote-mn-qwen3-30b

name	hyperloom-remote-mn-qwen3-30b
description	Run a 4-hour multi-node Hyperloom Qwen3-30B-A3B optimization (Infera PD-disaggregated or RayJob aggregated) with --nodes 2 and sglang MoE tuning on MI325X. Hand this skill to the agent to launch and monitor the run.

Hyperloom Remote Multi-Node — Qwen3-30B-A3B

Pinned workload for a remote multi-node optimize run. Pick one backend below and pass its FLAGS + Environment block to inference_optimizer optimize; the agent launches it in the background and monitors state.json until a terminal stop_reason — success (target_reached, global_converged, time_exhausted, max_ticks) or early failure (baseline_failed, baseline_accuracy_failed) that ends the run before optimizing. Concept and variable reference: @../../../reference/multi-node.md. Optimizer skill: @${HYPERLOOM_SKILL_PATH} (fallback @../../../../src/hyperloom/inference_optimizer/SKILL.md).

Prerequisites

• NFS_SHARED_ROOT: cluster-wide shared mount at the same absolute path on the sandbox and every GPU pod (model, tool checkouts, session artifacts). Required; local-only paths break multi-node restart and benchmarks.
• A provisioned cluster: the platform creates the GPU pods and hands them over via HYPERLOOM_MN_EXT_* (platform-injected; never passed on the CLI). optimize adopts that cluster and exits 2 without it. See @../../../reference/multi-node.md.

One FLAGS block, two readers

The blocks below are written for Primus-Claw and optimize: Claw parses them to size and build the cluster, then the agent runs optimize with the same block. Four flags configure the cluster Claw provisions and can be dropped when composing the optimize command. optimize accepts them so a real platform prompt parses, but nothing reads them once the cluster exists:

Claw-only flag	What Claw does with it
--mn-image	Container image for the GPU pods (infera needs the sshd layer)
--cpus-per-node / --mem-per-node	Per-pod CPU / memory request
--extra-env K=V	Baked into pod env at create time (repeatable)

They are listed last in each block so the optimize command is the block with its tail cut off.

Everything else (--nodes, --mn-backend, --gpus-per-node, --model, --framework, --pd-*) is read by both and stays on the optimize command.

Layout under NFS_SHARED_ROOT (replace ${NFS_SHARED_ROOT} with the real mount):

${NFS_SHARED_ROOT}/models/Qwen3-30B-A3B/   # --model
${NFS_SHARED_ROOT}/InferenceX/             # INFERENCEX_PATH
${NFS_SHARED_ROOT}/Magpie/                 # MAGPIE_PATH
${NFS_SHARED_ROOT}/TraceLens/              # TRACELENS_ROOT

If your deployment has the TraceLens-internal checkout, set TRACELENS_INTERNAL_ROOT separately. Leave it unset for the public open-source path.

---

Workload A — Infera + PD disaggregation

--mn-backend infera with --pd-mode disaggregated.

FLAGS

--gpu-type mi325x \
--model ${NFS_SHARED_ROOT}/models/Qwen3-30B-A3B \
--target-gain 30 \
--max-hours 4 \
--mn-backend infera \
--framework=sglang \
--nodes 2 \
--gpus-per-node 8 \
--tp 8 --ep 8 \
--isl 1024 --osl 1024 --conc 128 \
--precision bf16 \
--pd-mode disaggregated \
--pd-prefill-nodes 1 --pd-decode-nodes 1 \
--pd-prefill-tp 8 --pd-decode-tp 8 \
--pd-prefill-ep 8 --pd-decode-ep 8 \
--pd-transfer-backend mooncake \
--pd-ib-device rdma0,rdma1,rdma2,rdma3,rdma4,rdma5,rdma6,rdma7 \
--pd-prefill-extra-args "--attention-backend aiter --mem-fraction-static 0.78 --disable-radix-cache --load-balance-method round_robin --watchdog-timeout 3600 --enable-dp-attention --moe-dense-tp-size 1 --enable-dp-lm-head --chunked-prefill-size 8192" \
--pd-decode-extra-args "--attention-backend aiter --mem-fraction-static 0.82 --enable-dp-attention --load-balance-method round_robin --watchdog-timeout 3600 --moe-dense-tp-size 1 --enable-dp-lm-head --chunked-prefill-size 8192 --max-running-requests 1024" \
--mn-image <INFERA_SSHD_IMAGE> \
--cpus-per-node 90 \
--mem-per-node 1024 \
--extra-env MC_GID_INDEX=3 \
--extra-env NCCL_IB_GID_INDEX=3 \
--extra-env SGLANG_USE_AITER_AR=0

Environment

RANDOM_RANGE_RATIO=0.8
SGLANG_DISAGGREGATION_BOOTSTRAP_TIMEOUT=1200
SGLANG_DISAGGREGATION_WAITING_TIMEOUT=1200
INFERENCEX_PATH=${NFS_SHARED_ROOT}/InferenceX
TRACELENS_ROOT=${NFS_SHARED_ROOT}/TraceLens
MAGPIE_PATH=${NFS_SHARED_ROOT}/Magpie

Large-model MoE cold start may need a longer poll budget: export HYPERLOOM_MN_POLL_TIMEOUT_S=1800 HYPERLOOM_MN_HEALTH_WAIT_S=1800.

---

Workload B — RayJob aggregated

--mn-backend rayjob (no PD flags). Same model / Environment as Workload A; the differences are:

• Drop all --pd-* flags.
• Replace --pd-*-extra-args with a single --server-args (applied each restart).
• --mn-image is a standard Ray image (no sshd layer needed).
• Drop the SGLANG_DISAGGREGATION_* env vars (PD-only).
• --tp covers the whole cluster, not one node. One server spans every pod here, so --tp must be nodes * gpus-per-node (16) — sglang receives --tp <n> --nnodes 2 and splits the ranks, so --tp 8 would leave half of each pod's GPUs idle. Workload A differs because each PD role owns one node, where --pd-*-tp 8 fills all 8 of its GPUs.
• Pod-side env reaches the GPU pods only through --extra-env, which Claw bakes in at create time (Claw-only, see the table above — drop it from the optimize command). Cross-node tensor parallel needs the RoCE GID there. The Environment block below is sandbox-side only and never reaches the pods.

FLAGS

--model ${NFS_SHARED_ROOT}/models/Qwen3-30B-A3B \
--target-gain 30 \
--max-hours 4 \
--mn-backend rayjob \
--framework=sglang \
--nodes 2 \
--gpus-per-node 8 \
--tp 16 --ep 16 \
--isl 1024 --osl 1024 --conc 128 \
--gpu-type mi325x \
--precision bf16 \
--server-args "--attention-backend aiter --mem-fraction-static 0.8 --enable-dp-attention --deepep-mode normal --ep-dispatch-algorithm fake --load-balance-method round_robin --watchdog-timeout 3600 --moe-dense-tp-size 1 --enable-dp-lm-head --chunked-prefill-size 8192 --max-running-requests 1024" \
--mn-image <RAYJOB_IMAGE> \
--cpus-per-node 90 \
--mem-per-node 1024 \
--extra-env NCCL_IB_GID_INDEX=3

Optional model pins (omit to use the deployment defaults): --claude-model <model> / --codex-model <model>.

Environment

RANDOM_RANGE_RATIO=0.8
INFERENCEX_PATH=${NFS_SHARED_ROOT}/InferenceX
TRACELENS_ROOT=${NFS_SHARED_ROOT}/TraceLens
MAGPIE_PATH=${NFS_SHARED_ROOT}/Magpie

The Kernel-Forge kernel backend needs no checkout and no path variable: KernelForge ships inside Hyperloom. Select it with KERNEL_OPT_BACKEND_ORDER=forge (Workload B only). Behind a TLS-terminating proxy add NODE_TLS_REJECT_UNAUTHORIZED=0 and ANTHROPIC_SKIP_TLS_VERIFY=true.

---

Run notes

• Session lands in $USER_DATA_PATH/<model_basename>/<UTC_ts>/; USER_DATA_PATH is platform-injected and kept unchanged.
• Crash recovery: optimize --resume-from "$SESSION_DIR" on the same session dir (never a second optimize; resume past a terminal stop_reason needs --force-resume).
• Releasing the cluster is the platform's job, not the optimizer's — it happens when the session ends.