| name | nvidia-megatron-bridge-perf-parallelism-strategies |
| description | Operational guide for choosing and combining parallelism strategies in Megatron Bridge, including sizing rules, hardware topology mapping, and combined parallelism configuration. |
| license | Apache-2.0 AND CC-BY-4.0 |
| metadata | {"when_to_use":"Choosing or sizing TP/DP/PP/CP/EP degrees, or tracing an OOM or regression to a parallelism config change; 'how to parallelize', 'tensor parallel', 'pipeline parallel', 'parallelism config', 'which parallelism for X GPUs'.","author":"NVIDIA"} |
Parallelism Strategy Selection Skill
For stable background on each parallelism type, see:
- @docs/parallelisms.md
- @skills/nvidia-megatron-bridge-perf-parallelism-strategies/card.yaml
Decision by Model Size
Dense models
| Model size | GPUs | Recommended starting point |
|---|
| < 1B | 1-8 | DP only |
| 1-10B | 8-16 | TP=2-4 + DP |
| 10-70B | 16-64 | TP=4-8 + PP=2-4 + DP |
| 70-175B | 64-256 | TP=8 + PP=4-8 + DP |
| 175-500B | 256-1024 | TP=8 + PP=8-16 + CP=2 + DP |
MoE models
MoE parallelism differs from dense models. Because only a fraction of
parameters are active per token, TP can often stay at 1 or 2 — the active
parameter shard already fits on a single GPU. EP is the primary scaling
dimension, with PP handling cross-node layer distribution.
| Model (total / active) | TP | PP | EP | Notes |
|---|
| OLMoE 7B / 1B | 1 | 1 | 8 | EP only, fits single node |
| Moonlight 16B / 3B | 2 | 1 | 8 | small TP for shared layers |
| DeepSeek-V2 236B / 21B | 1 | 4 | 32 | no TP at all |
| GLM-4.5 Air 106B / 12B | 1 | 4 | 8 | no TP at all |
| Qwen3 30B-A3B | 4 | 2 | 4 | |
| GLM-4.5 355B / 32B | 2 | 8 | 16 | |
| Qwen3 235B-A22B | 4 | 16 | 8 | CP=2 for pretrain |
| DeepSeek-V3 671B / 37B | 2 | 16 | 64 | TP=2, not 8 |
| Kimi-K2 1T | 2 | 16 | 32 | |
Key patterns:
- TP is sized by active params, not total params. A 671B MoE with
37B active needs far less TP than a 70B dense model.
- EP scales with expert count. Common: EP = num_experts or
num_experts / experts_per_gpu.
- PP handles depth. Large MoE models use PP=8-16 across nodes.
- ETP (expert tensor parallelism) is rarely used. Llama 4 is an
exception (ETP=4).
These are starting points, not hard rules. Always profile the first
iteration to verify memory and communication.
Decision by Hardware Topology
Single node with NVLink:
cfg.model.tensor_model_parallel_size = 8
Multiple nodes with InfiniBand:
cfg.model.tensor_model_parallel_size = 8
cfg.model.pipeline_model_parallel_size = N
Limited network (Ethernet):
cfg.model.tensor_model_parallel_size = 4
cfg.model.pipeline_model_parallel_size = M
The stable rule is: keep TP within a single NVLink domain. Use PP or DP
for cross-node scaling. TP across nodes is almost always a performance
loss.
Decision by Sequence Length
| Sequence length | Recommendation |
|---|
| < 2K | standard TP + PP + DP |
| 2K-8K | add SP (sequence_parallel=True) |
| 8K-32K | add CP=2 |
| 32K+ | add CP=4-8, consider a2a+p2p for large CP |
Combined Parallelism Enablement
3D parallelism (TP + PP + DP):
cfg.model.tensor_model_parallel_size = 4
cfg.model.pipeline_model_parallel_size = 4
cfg.model.sequence_parallel = True
4D parallelism (TP + PP + CP + DP):
cfg.model.tensor_model_parallel_size = 8
cfg.model.pipeline_model_parallel_size = 8
cfg.model.context_parallel_size = 2
cfg.model.sequence_parallel = True
MoE with EP + PP (e.g. DeepSeek-V2 236B on 128 GPUs):
cfg.model.tensor_model_parallel_size = 1
cfg.model.pipeline_model_parallel_size = 4
cfg.model.expert_model_parallel_size = 32
cfg.model.sequence_parallel = False
MoE with small TP + PP + EP (e.g. DeepSeek-V3 671B on 256 GPUs):
cfg.model.tensor_model_parallel_size = 2
cfg.model.pipeline_model_parallel_size = 16
cfg.model.expert_model_parallel_size = 64
cfg.model.sequence_parallel = True
DP size is always implicit:
data_parallel_size = world_size / (TP * PP * CP) # dense path
expert_data_parallel_size = world_size / (PP * EP * ETP) # MoE path
Minimum GPU Count
The minimum GPUs needed to run a config (i.e. with DP=1, EDP=1)
is not the product of all parallelism dimensions. The dense path uses
a TP*CP-mesh and the MoE path uses an EP*ETP-mesh, and within each PP
stage these two meshes share the same set of GPUs — they overlap, they
don't multiply. Only PP stages multiply (they're disjoint slices of the
model). So:
min_gpus = PP * max(TP * CP, EP * ETP)
Common simplification (WRONG): PP * TP * CP * EP * ETP. This
over-allocates GPUs and shows up in many READMEs and slurm sizing tables.
Don't propagate it.
The decoupling of attention and MoE parallelism (different mesh shapes
for the dense and expert paths sharing the same PP-stage GPUs) is
detailed in
Pangu Ultra MoE (arXiv:2504.14960).
Examples
| Config | Wrong (PP·TP·CP·EP·ETP) | Correct (PP·max(TP·CP, EP·ETP)) |
|---|
| PP=1, TP=2, CP=1, EP=8, ETP=1 | 16 | 8 (1 node) |
| PP=1, TP=4, CP=1, EP=8, ETP=1 | 32 | 8 (max(4, 8)) |
| PP=1, TP=2, CP=2, EP=8, ETP=1 | 32 | 8 (max(4, 8)) |
| PP=1, TP=2, CP=4, EP=8, ETP=1 | 64 | 8 (max(8, 8)) |
| PP=2, TP=2, CP=1, EP=8, ETP=1 | 32 | 16 (2 · max(2, 8)) |
| PP=1, TP=2, CP=1, EP=4, ETP=2 | 16 | 8 (max(2, 8)) |
Scaling above the minimum
Adding GPUs scales DP and/or EDP (the world_size must satisfy
both equations simultaneously). At min_gpus the larger-mesh side has
DP (or EDP) = 1 and the smaller side absorbs the slack.
Example — TP=2, CP=1, EP=8, ETP=1, PP=1:
- 8 GPUs (
min_gpus): dense DP = 8/2 = 4, MoE EDP = 8/8 = 1
- 16 GPUs: dense
DP = 8, MoE EDP = 2 → 2× global batch
- 32 GPUs: dense
DP = 16, MoE EDP = 4 → 4× global batch
When sizing slurm scripts, compute --nodes from min_gpus (or a
multiple of it for higher throughput via DP/EDP).
Memory Estimation
Without parallelism (70B model, FP16):
parameters: 140 GB
gradients: 140 GB
optimizer states: 280 GB (Adam)
activations: 48 GB (batch=1, seq=4K)
total: 608 GB
With TP=4, PP=4, DP=4 (64 GPUs):
parameters: 8.75 GB per GPU
gradients: 8.75 GB per GPU
optimizer states: 17.50 GB per GPU
activations: 3.00 GB per GPU
total: ~38 GB per GPU
Code Anchors
Parallelism dimensions set in model provider:
model_config = GPTModelProvider(
tensor_model_parallel_size=2,
# ... other model parameters
)
DP size calculation:
data_parallel_size = world_size / (tensor_model_parallel_size × pipeline_model_parallel_size × context_parallel_size)
Bridge initialization wires parallelism into process groups:
parallel_state.initialize_model_parallel(
tensor_model_parallel_size=model_config.tensor_model_parallel_size,
pipeline_model_parallel_size=model_config.pipeline_model_parallel_size,
...
context_parallel_size=model_config.context_parallel_size,
hierarchical_context_parallel_sizes=model_config.hierarchical_context_parallel_sizes,
expert_model_parallel_size=model_config.expert_model_parallel_size,
...
)
Pitfalls
-
TP across nodes destroys throughput. Always keep TP within a single
NVLink domain.
-
PP without interleaving has large pipeline bubbles. Use
virtual_pipeline_model_parallel_size when possible.
-
SP requires tensor_model_parallel_size > 1. Enabling SP alone
without TP is a config error.
-
CP requires seq_length % (2 * context_parallel_size) == 0.
-
EP is only for MoE models. Setting expert_model_parallel_size on a
dense model is a no-op or error.
-
The model-size-to-parallelism table above is a starting heuristic.
Always profile the first iteration to check memory and communication.
-
CUDA_DEVICE_MAX_CONNECTIONS and related env vars interact with
overlap settings. See @skills/nvidia-megatron-bridge-perf-tp-dp-comm-overlap/SKILL.md.
-
The minimum GPU count for an MoE config is PP * max(TP*CP, EP*ETP),
not the product of all dimensions. The dense TP*CP-mesh and MoE
EP*ETP-mesh share the same GPUs in each PP stage. See
"Minimum GPU Count" section above.
Verification
Quick sanity check that combined parallelism initializes correctly using
the smallest available recipe with overridden parallelism:
CUDA_VISIBLE_DEVICES=0,1,2,3 uv run python -m torch.distributed.run --nproc_per_node=4 \
scripts/training/run_recipe.py \
--recipe llama32_1b_pretrain_config \
model.tensor_model_parallel_size=2 \
model.pipeline_model_parallel_size=2 \
model.sequence_parallel=True \
train.train_iters=3 train.global_batch_size=8 train.micro_batch_size=1 \
scheduler.lr_warmup_iters=0 \
validation.eval_iters=0 validation.eval_interval=0 \
checkpoint.save_interval=0 \
logger.log_interval=1
Success criteria:
- exit code 0
- finite loss at iteration 3 (e.g.
lm loss: 1.003808E+01)
- log shows TP=2 PP=2 DP=1 layout with 4 ranks
