LLMs 44. Distributed Training of Large Language Models (LLMs)
Quick Overview
This guide explains distributed training of large language models as a systems problem, covering memory and time constraints, communication costs and synchronization patterns, and parallelism techniques such as data, tensor, and pipeline parallelism with their trade-offs and applicability.
Distributed Training of Large Language Models: A Systems Thinking Guide
Distributed training is not just a way to make Large Language Models (LLMs) train faster. It is a response to a deeper constraint: no single device can hold, compute, and update modern models alone. This post is written to help candidates understand distributed training as a systems problem, not a collection of parallelism tricks.
Rather than enumerating APIs or configurations, we focus on the underlying ideas that explain why these techniques exist, when they work, and where the same logic applies beyond LLMs.
Why LLMs Force Distribution
There are two hard limits that collide in LLM training.
First, memory. Model parameters scale linearly with size, but training memory scales much faster. Optimizers, gradients, and activations easily multiply memory requirements by 6–8×. Even an 80 GB GPU cannot train many modern models without sharding.
Second, time. Even if memory were infinite, training a 100B+ parameter model on a single GPU would take decades. Parallelism is not an optimization—it is a necessity.
These two constraints define everything that follows.
Communication Is the Real Cost
When training is distributed, computation is rarely the bottleneck. Communication is.
Every distributed strategy answers the same question differently:
What must be synchronized, when, and how often?
Point-to-point communication is fast and cheap but limited in scope. Collective communication (AllReduce, ReduceScatter, Broadcast) is powerful but expensive. The entire design space of distributed training is about minimizing how much data moves and how frequently it moves.
This insight generalizes directly to distributed databases, streaming systems, and even large-scale inference.
Data Parallelism: Scaling Compute, Not Memory
Data Parallelism (DP) is the simplest strategy: each worker holds a full copy of the model and processes different data. Gradients are synchronized before updating parameters.
This works well when:
- The model fits in memory
- Communication bandwidth is sufficient
- Compute dominates communication
Its limitation is fundamental: memory does not scale. Every GPU stores the full model, optimizer states, and gradients. DP solves throughput, not capacity.
This mirrors patterns in other systems: replicating services improves throughput but not storage efficiency.
Model Parallelism: Sharding the Model Itself
When the model no longer fits, the model must be split.
Tensor Parallelism divides individual layers across GPUs. Pipeline Parallelism divides layers themselves across GPUs. Both reduce memory per device but introduce new communication paths.
Here, a key trade-off appears:
- Tensor Parallelism increases fine-grained communication inside layers
- Pipeline Parallelism increases latency and pipeline bubbles
Neither is “better” in isolation. The right choice depends on hardware topology, especially NVLink vs Ethernet.
The deeper lesson: architecture follows hardware, not the other way around.
Why 3D Parallelism Exists
3D parallelism combines:
- Data Parallelism (scale throughput)
- Tensor Parallelism (fit layers)
- Pipeline Parallelism (fit depth)
This combination is not elegant, but it is effective. It reflects a reality candidates should internalize:
Large systems are rarely simple; they are layered compromises.
Understanding why each dimension exists is more important than memorizing how to configure them.
ZeRO and FSDP: Rethinking Redundancy
ZeRO-style optimizations ask a different question:
Why should every worker store the same optimizer state, gradients, or parameters?
By sharding these states, ZeRO dramatically reduces memory usage. But this comes at the cost of increased communication. At small scales, this trade-off is favorable. At very large scales, communication dominates and performance degrades.
This is a recurring systems theme: removing redundancy saves space but increases coordination cost.
Training vs Inference: Different Constraints, Different Answers
Many parallel strategies work well for training but poorly for inference.
Inference prioritizes:
- Latency
- Predictability
- Minimal synchronization
Pipeline parallelism and heavy sharding often fail here. This is why inference systems frequently look simpler than training systems, even for the same model.
The takeaway is that system design must be workload-aware.
How This Transfers Beyond LLMs
The ideas in distributed LLM training apply broadly:
- In recommendation systems, embedding tables mirror model sharding problems
- In graph processing, communication dominates computation at scale
- In distributed storage, replication vs sharding reflects DP vs MP trade-offs
Once you see distributed training as a resource-allocation problem, new domains feel familiar.
A Mental Model for Candidates
When reasoning about distributed systems, ask:
- What is replicated?
- What is sharded?
- What must stay consistent?
- What dominates cost: compute, memory, or communication?
Candidates who answer these questions clearly demonstrate true systems understanding—far beyond framework-level knowledge.
Final Thought
Distributed training is not about “using more GPUs.”
It is about aligning algorithms, hardware, and communication patterns under constraint.
If you can explain why a strategy works—and when it stops working—you are no longer just training models.
You are designing systems.
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