nvidia-megatron-bridge-perf-moe-vlm-training

name	nvidia-megatron-bridge-perf-moe-vlm-training
description	Practical guidance for training MoE VLMs in Megatron Bridge. Compares FSDP and 3D-parallel approaches, using rounded lessons from Qwen3-VL, Qwen3-Next, and other multimodal experiments.
license	Apache-2.0 AND CC-BY-4.0
metadata	{"when_to_use":"Training MoE VLMs, or investigating a commit that caused MoE VLM training failure or OOM; 'MoE VLM', 'multimodal MoE', 'Qwen3-VL training', 'FSDP vs 3D-parallel for VLM', 'MoE vision language model'.","author":"NVIDIA"}

MoE VLM Training

Stable docs: @docs/training/moe-optimization.md Card: @skills/nvidia-megatron-bridge-perf-moe-vlm-training/card.yaml

FSDP vs 3D Parallel

Approach	Strength	Best fit
FSDP	Simplest path to a working multimodal run	first bring-up, memory-first tuning, awkward PP boundaries
3D parallel	Higher ceiling after tuning	stable models with a clean PP layout and time for deeper sweeps

For MoE VLMs, the practical workflow is usually:

1. get the first reliable run with FSDP
2. stabilize real-data input, recompute, and memory behavior
3. move to 3D parallel only if the throughput headroom is worth the extra work

Rounded Findings From Recent VLM Runs

Qwen3-VL class models

The main patterns were consistent across the tracker:

• FSDP on GB200-class systems can already reach healthy high-teens utilization with a comparatively simple setup
• B200 FSDP runs are viable, but more sensitive to recompute choice and frozen vision settings
• 3D parallel can recover to a similar or better operating point, but only after tuning MBS, recompute, and the real vision path together

Real data vs mock data

Mock-data VLM runs are not trustworthy performance proxies. In the experiments, image-free mock runs looked closer to "roughly twice as fast" than "slightly optimistic" when compared with real multimodal input.

Use real or realistic image payloads before drawing any conclusion about VLM throughput.

Smaller multimodal MoE runs

The smaller Qwen3.5-style multimodal experiments reinforce the same lessons:

• HybridEP is a solid default on GB200
• TE-scoped CUDA graphs help once the training loop is stable
• larger MBS can pay off, but only if the vision encoder does not become the next bottleneck

Decision Guide

Choose FSDP when

• you are bringing up a new VLM for the first time
• the model has awkward stage boundaries across embedding, vision, and decoder
• memory fit matters more than absolute throughput
• you may freeze the vision stack during decoder-focused tuning

Choose 3D parallel when

• the model is already stable under FSDP
• the PP layout is clear and repeatable
• you can sweep MBS, recompute, and CUDA-graph scope together
• the goal is best steady-state throughput, not easiest bring-up

Key Tuning Knobs

1. Freeze the vision stack when appropriate: if the work is decoder-focused, freezing the vision side often gives a small but real throughput gain and reduces memory pressure.

2. Sweep MBS aggressively: VLMs are more MBS-sensitive than text-only MoE runs because the vision path changes the compute-to-overhead balance.

3. Prefer selective recompute once the model fits: full recompute is a useful bring-up tool, but selective recompute is usually the better steady state.

4. Match CUDA-graph scope to the workload: attn moe_router moe_preprocess is the safer MoE default, while narrower scopes can still be useful for controlled experiments.

5. Use ETP only when EP alone is insufficient: it can unlock a layout, but it also introduces more communication and more tuning surface.

Representative Config Families

FSDP-first GB200 path

TP=1  CP=1  PP=1
EP sized to the expert topology, often large
Dispatcher: HybridEP on GB200-class systems
Recompute: start with full, then relax toward selective recompute

3D-parallel GB200 path

TP=1  CP=1  PP=1 or modest PP
EP and ETP sized to the expert topology
Dispatcher: HybridEP
CUDA Graph: start narrow, then widen only after the real-data path is stable

Compatibility

Feature	FSDP	3D parallel
HybridEP on GB200	strong default	strong default once topology is stable
CUDA graphs	useful after bring-up	useful, but more scope-sensitive
Freeze vision	natural fit	possible, but less often used as the headline perf path
Selective recompute	recommended	recommended

Pitfalls

1. Mock multimodal data is misleading: it can make the decoder look much healthier than the real end-to-end VLM path.

2. The vision encoder can dominate unexpectedly: profile encoder, projector, and decoder separately before attributing everything to the dispatcher.

3. Do not compare FSDP and 3D-parallel runs with different effective work: normalize by useful tokens and workload shape, not only by step time.

4. ETP is not free: use it as a fit or topology tool, not as the default.

5. Recompute and CUDA-graph choices are coupled: the setting that gets the model to fit is often not the setting that gives the best steady-state speed.