[QUESTION] Megatron-LM DistributedOptimizer or NeMo MegatronDistributedFusedAdam Optimizer?

[TJ-Solergibert]

Hi,

After going across both Megatron-LM & NeMo I've found that NeMo configs set by default the MegatronDistributedFusedAdam optimizer from the NeMo framework. But Megatron also contains a DistributedOptimizer.

The NeMo one is based on the Apex's DistributedFusedAdam which incorporates ZeRO-2. I would like to know which one is better, both in terms of throughput and performance or which config do you recommend to use for large scale training.

Thanks!

[Advait251206]

This is a good question because these two optimizers look similar but sit at different levels of the stack and make very different trade-offs.

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1. High-level summary

• Megatron-LM DistributedOptimizer

  • Original Megatron implementation
  • Focus: model-parallel + optimizer-state sharding (ZeRO-1 style)
  • Lightweight, very stable, minimal dependencies

• NeMo MegatronDistributedFusedAdam

  • Built on top of Apex-style fused Adam
  • Focus: performance + communication reduction + better kernel fusion
  • More optimized, more features, slightly more complex stack

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2. Key conceptual difference

🔹 Megatron-LM DistributedOptimizer

This is:

a wrapper around standard Adam with model-parallel-aware state partitioning

It provides:

• optimizer state sharding across TP/PP ranks
• gradient reduction handling
• simple, explicit communication logic

Pros:

• very stable reference implementation
• easy to debug
• minimal external dependencies
• predictable behavior

Cons:

• not heavily kernel-fused
• slower than modern fused implementations
• more communication overhead

---

🔹 NeMo MegatronDistributedFusedAdam

This is:

a highly optimized fused optimizer built on Apex-style fused kernels + distributed sharding

It includes:

• fused Adam kernel (faster GPU updates)
• ZeRO-2-like sharding behavior
• reduced memory traffic
• tighter integration with NCCL communication

Pros:

• significantly higher throughput
• lower optimizer overhead
• better scaling for large models
• reduced GPU memory bandwidth pressure

Cons:

• more complex code path
• depends on fused kernels (Apex / custom CUDA)
• harder to debug numerical issues
• slightly less transparent than Megatron-LM reference optimizer

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3. Performance comparison (practical view)

Compute-heavy large-scale training (7B+ models)

✔ NeMo fused optimizer wins

Because:

• optimizer step becomes memory-bound at scale
• fused kernels reduce GPU memory traffic
• communication overlaps better

👉 Typically better tokens/sec and MFU

---

Small / medium scale / research setups

✔ Megatron-LM DistributedOptimizer is often better

Because:

• simpler execution graph
• fewer compilation dependencies
• easier to reproduce results
• fewer fused-kernel edge cases

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4. Communication behavior difference

Feature	Megatron DistributedOptimizer	NeMo FusedAdam
Gradient sync	explicit	overlapped/fused
Optimizer state sharding	ZeRO-1 style	closer to ZeRO-2
NCCL usage	more explicit calls	more hidden / fused
Memory efficiency	good	better

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5. Stability vs performance trade-off

Megatron-LM optimizer:

“research-grade correctness first”

NeMo optimizer:

“production-scale throughput first”

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6. Which one should you use?

✔ Use NeMo MegatronDistributedFusedAdam if:

• training large models (≥7B, especially 13B+)
• running multi-node setups
• you want max throughput
• using NeMo training stack already
• GPU memory bandwidth is bottleneck

---

✔ Use Megatron-LM DistributedOptimizer if:

• you are doing research or debugging
• you want simpler code path
• you modify training logic frequently
• you care about reproducibility and transparency
• you are not bottlenecked by optimizer step

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7. Important nuance

Both optimizers are still:

• data-parallel + tensor-parallel aware
• not full ZeRO-3 implementations
• dependent on Megatron’s parallel state system

So differences are mostly:

kernel fusion + communication efficiency, not training semantics

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8. Rule of thumb

If you're scaling → use NeMo fused optimizer
If you're experimenting → use Megatron reference optimizer

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9. Bottom line

• ✔ NeMo MegatronDistributedFusedAdam → faster, more optimized, production-grade
• ✔ Megatron DistributedOptimizer → simpler, more transparent, research-friendly
• ❌ Neither changes model correctness fundamentally
• ⚠ Choice mainly affects throughput + scalability, not convergence behavior

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GitHub: https://github.com/Advait251206