The Ultra-Scale Playbook: Training LLMs on GPU Clusters
Fueled by the scaling laws
In this book we invite you to follow us in the wonderful world of scaling training of Large Language Models to tens, hundreds, thousands of GPUs. It assumes you know the basics on LLM architecture and training, but are new to distributed training. This writing can be seen as a second part of a trilogy following our first blog on processing data for pre-training, the so-called “FineWeb blog post”. Having read both blog posts, you should have almost all the core knowledge needed to deeply understand how LLMs are being built nowadays, just missing a bit the final spices like data mixing or architecture choices to complete the recipe (stay tuned…).
Pre-training LLMs from scratch now requires amounts of compute which exceed in almost every case the use of a single GPU or machine. The clusters used to train these models range from hundreds to thousands of nodes each usually equipped with 4 to 8 GPUs. To make the best use of such an expensive hardware as well as to train in a reasonable time, a range of distributed training methods have been developed with the goal of ensuring that GPUs are highly utilized at all times. Efficiently scaling LLM training is also not confined to pretraining anymore, as fine-tuning larger models on more domain specific data is becoming the standard practice to achieve the best results.
In this post we’ll cover these scaling methods exhaustively while keeping a single story-line to understand where each technique comes from. We’ll cover data, tensor, pipeline and context parallelism as well as ZeRO and kernel fusion. The post is built on the following three foundations:
Quick intros on theory and concepts: before diving into code and experiments, we want to understand how each method works at a high level and what it’s advantages and limits are. You’ll learn about which parts of a language model eat away your memory and when during training it happens. You’ll learn how we can solve memory constraints by parallelizing the models and increase the throughput by scaling up GPUs. As a result you'll understand how the following widget to compute the memory breakdown of a transformer model works:
While this widget gives a theoretical breakdown the following tool can be used to predict the memory usage:
Clear code implementations: theory is one thing, but we discover all kinds of edge cases and important details when we implement something. That’s why we link to implementation references where possible. Depending on the case, we’ll use two code references: the picotron repository is built for education, thus it implements concepts usually in single, self-contained short files. On the other hand, to look at production ready code, we’ll refer to the nanotron implementations which is a production training codebase used at Hugging Face.
Real training efficiency benchmarks: Finally, how to actually scale your LLM training depends on your infrastructure, such as the kind of chips, interconnect etc., and we can’t give a single unified recipe. What we will give though is a way to benchmark several setups and it is what we have done on our cluster! We ran over 4100 distributed experiments with up to 512 GPUs to scan many possible distributed training layouts and model sizes. TODO: link to dataset too
As you can see, there’s a lot of ground to be covered. Before getting into the trenches of distributed training let’s take a quick high level look on we’ll cover in the post.
This book is very extensive so we decide to start with a very general overview of how you can think about distributed training. At a high level, the key challenge in scaling LLM training is to make a training step (forward/backward/optimizer step) with a large batch size the fastest possible.
When scaling up models and input batches, we quickly end up in situations where either our target batch size won't fit in memory, or/and the model itself is too large to fit in a single GPU's memory.
To solve this scaling issue we’ll need to carefully evaluate different parallelization strategies and find the optimal balance between three main factors:
But let’s not get too much ahead of our self and scale progressively. To guide you along the journey and as a practical reference we summarized the key concepts in a cheatsheet:
[TODO: ADD CHEATSHEET]
Now that we nailed a few key concept and terms let’s get started by revisiting the basic training steps of an LLM!
Let’s start by quickly reviewing the very basics of model training before we start to scale to many GPUs. When a model is trained on a single GPU, the training typically consists of three steps:
It looks generally like this:
In this figure, the boxes on the top line can be seen as successive layers inside a model (same for the last line). The red boxes are the associated gradients for each of these layers, computed during the backward pass.
The batch size (
If the batch size is too small, gradients will tend to be noisy and the model may not be able to converge to the most optimal performance, on the contrary it can be useful in early training to navigate quickly in the training landscape. On the other hand, a batch size too large will make less use of each training token rendering convergence slower and wasting compute. You can find a nice discussion of this topic in OpenAI’s paper on large batch training
Batch size also affects the time it takes to train on a given text dataset: a small batch size will require more optimizer steps to train on the same amount of samples. Optimizer steps are costly (in compute time) and the total time to train will thus increase compared to a larger batch size. This being said, note that the batch size can often be adjusted quite largely around the optimal batch size without major impact to the performance of the model, i.e. the sensitivity of final model performances to the exact batch size value is usually rather low around the optimal batch size.
In the LLM pretraining community, batch sizes are commonly reported in terms of tokens rather than in number of samples (
In the simplest case, training on a single machine, the
A sweet spot for recent LLM training is typically on the order of 4-60 million tokens per batch. However, a typical issue when scaling the training of our model to these large batch sizes is out-of-memory issues, ie. our GPU doesn’t have enough memory.
It’s time to tackle our first scaling problem: what if our model starts exploding GPU memory before we’ve reached our target batch size (maybe in some case even when using the lowest possible batch size, bs=1)?
Let’s start by quickly understanding what led to our out-of-memory issue in the first place. This will help us gain some useful intuitions for later.
When training a neural network model, one store several items in memory:
These items are stored as tensors which come in different shapes and precisions. The shapes are determined by hyper-parameters such as batch size, sequence length, model hidden dimensions, attention heads, vocabulary size, and potential model sharding as we’ll see later. Precision refers to formats like FP32, BF16, or FP8, which respectively require 4, 2, or 1 byte to store each single value in the tensor.
So how can I quickly determine memory usage from these variable? One simple way is to do this empirically and just measure it.
Using this snippet [TODO: link to appendix A5], we can understand how memory is allocated throughout training. We can see that memory utilization is not a static thing but varies a lot during training and during a training step:
Clearly the first step looks very different from the subsequent ones, but let’s first have a look at the general anatomy of a step: first the activations increase quickly as we do the forward pass, then during the backward pass the gradients build up and as the backward pass propagates, the stored activations used to compute the gradients are progressively cleared. Finally, we perform the optimization step during which we need all the gradients and then update the optimizer states before we start the next forward pass.
Why does the first step looks different: the activations increase quickly and then plateau for a while. In this first step the torch cache allocator does a lot of preparation preparing memory allocations to speed up the subsequent steps so that they don’t require searching for free memory blocks afterwards (see Zach’s blog). After the first step we also see the optimizer states appearing which generally offset the memory usage for further training steps.
Now that we’ve a first view of memory, let’s see how scaling up training is often a question of maximizing compute efficiency while keeping the memory requirements of these various items (activations, parameters, gradients, optimizer states) within the memory constraints of the GPUs.
We can actually pretty easily estimate the memory needed for the model’s weights, gradients and optimizer states.
For a simple transformer LLM the number of parameters is given by the following formula:
In that equation,
Memory requirements for the parameters and gradients are simply the number of parameters multiplied by the number of bytes per parameter. In good old-fashioned full precision (FP32) training both parameters and gradients require 4 bytes while the optimizer, if we use Adam, requires the momentum and variance to be stored, which adds another two 4 bytes per parameter. In summary:
Now let’s have look how things change if we train with mixed precision
Here’s the summary:
Interestingly, mixed precision itself doesn’t save overall memory as it just distributes the memory differently across the three components, and in fact adds another 4 bytes over full precision training if we accumulate gradients in FP32. It’s still advantageous as having the model which does the forward/backward in half precision it allows us to (1) use optimized lower precision operations on the GPU which are faster and (2) reduces the activation memory requirements during the forward pass.
Let’s get a sense of how much general memory we need for a model (full and mixed precision giving the same overall value):
| Model parameters | FP32 or BF16 w/o FP32 grad acc | BF16 w/ FP32 grad acc |
|---|---|---|
| 1B | 16 GB | 20 GB |
| 7B | 112 GB | 140 GB |
| 70B | 1120 GB | 1400 GB |
| 405B | 6480 GB | 8100 GB |
As we can see, as soon as we reach 7B (!), weights and optimizer requirements already starts to add up significantly and exceed the size of a typical GPU memory, e.g. 80GB for a H100 GPU.
But for now, let’s start with models which still fits in a single GPU, take a look at the other big contributor to our memory budget: the activation memory.
Activation memory is a bit more complex to compute than the weights, gradients and optimizer states, in part because it depends on the inputs of the model. If you’re unsure why we even need to store activations for the backward pass, this reference is a good quick refresh. After a careful inspection of how backward pass is computed we can estimate the total memory required for the activations in mixed precision and we arrive at the following equation:
Here
For the exact derivation of the numbers, you can follow this original NVIDIA paper on recomputation
An interesting observation here is how the memory is not static for a given model but it scales linearly with both the sequence length and batch size. This means the activation memory is the part which will blow up when we increase our batch size or train with longer sequences. We can use this equation to look at how memory usage changes for various sequence lengths for example for Llama models (bs=1):
This graph tells a striking story: for short sequences (or similar for small batch-sizes), activations are almost negligible, but starting at around 2-4k tokens they come to take a significant amount of memory while parameter, gradient and optimizer states usage (that we’ll discuss later) stays roughly independent of the sequence length and batch size.
For large input tokens (a.k.a large batch-sizes/sequences), activations become by far the largest memory burden.
Is there a way to tame this “activation explosion”? Good question, reader!
It’s time to explain our first technique – called activation recomputation– **which will help us cap activation memory footprint. An essential tool in today’s large model training toolbox.
The general idea behind activation recomputation – also called gradient checkpointing or rematerialization – is to discard some activations during the forward pass to save memory and spend some extra compute to recompute these on the fly during the backward pass. Without recomputation, we store every hidden state between two learnable operations (e.g. FF, LayerNorm etc.), such that we can use them during the backward pass to compute gradients. When we use recomputation we typically will only store activations at a few key points along the model architecture, discard the rest of activations and recompute them on the fly during the backward pass from the nearest saved activations, basically performing again a sub-part of the forward pass to trade of memory for compute. It generally looks like this:
There are several strategies to select key activations to store:
full strategy since it requires a forward pass through each layer essentially adding a full forward pass during the backward pass. This strategy saves the most memory but is the most expensive one in terms of compute. It generally increases the compute cost and time by up to 30-40% which is very noticeable.Let’s see how drastically recomputation strategies can in practice reduce the memory footprint and how selective recomputation strikes a nice balance between memory saving and recomputation cost:
Most training frameworks these days use FlashAttention (which we’ll cover a bit later) which integrate natively activation recomputation in its optimization strategy by recomputing attention scores and matrices in the backward pass instead of storing them. Thus most people using Flash Attention are already making use of selective recomputation.
As you’ve now understood, activation recomputation increases the number of FLOPs slightly due to recomputation, while it significantly reduces memory access overhead.
This trade-off is particularly advantageous on hardware with small high-speed memory, like GPUs, as accessing memory is typically slower than performing computations. Despite the additional operations involves, the overall effect is thus often faster computation as well, in addition to the much lower memory footprint.
Now that we’ve learned about recomputation, we can tame the activations memory usage as we saw in the above graphs!
However, activations still bears a linear dependance on the batch size and all our profiles in the barplots above were using bs=1 so as we move to larger batch sizes it might become an issue again. Do not despair as we have a second tool in our box - gradient accumulation to the rescue!
Now that we’ve used activation recomputation to fit our model with a small batch size on a single GPU, we still need to reach our target batch size, let’s say 1M tokens (see our earlier discussion on optimal batch size). Gradient accumulation is a very straightforward method to avoid memory explosion when doing this.
With gradient accumulation we split our batch into micro-batches, do forward and backward passes repeatedly on each micro-batch, compute the gradients, and, as the name suggests, sum the gradients for each micro-batch before doing a final optimizer step. In practice, we perform the optimization step not on the sum but on the average of the gradients, so the result is independent of the number of gradient accumulation steps.
Let’s call the batch size for each forward pass the micro batch size (mbs). We’ll refer to the overall batch size between each optimizer step as the global batch size (gbs). If we do one optimizer step for each 8 forward/backward passes, the global batch size will be 8 times the micro batch size.
What we now call global batch size thus corresponds to what we’ve called up to now just batch size for simplicity (we now make our terms more precise to avoid ambiguity).
With gradient accumulation the global batch size can be simply computed as follows:
Gradient accumulation allows us to effectively increase our batch size up to infinity (and beyond!) while the memory footprint stays constant. Gradient accumulation is also compatible with activation recomputation for further memory reduction. One drawback however, is that gradient accumulation requires multiple consecutive forward/backward passes per optimization step thereby increasing the compute overhead and slowing down training. No free lunch!
Gradient accumulation allows us to reduce memory of activations which grow linearly with batch size by computing only only partial, micro-batches. There is a small overhead caused by the additional forward and backward passes.
But if you’ve carefully followed, you probably noticed that the forward/backward passes for each micro-batch can actually be run in parallel. Forward/backward passes are independent from each other, with independent input samples being the only difference. Seems like it’s time to start extending our training to more than one GPU!
Let’s get a larger workstation 🖥️ with a couple of GPUs and start investigating our first scaling technique called data parallelism which is just a parallel version of gradient accumulation.
TODO: add profiling here or not?
The idea behind data parallelism (DP) is to replicate the model on several GPUs (we call the replica's “model instances”) and run forward and backward passes on different micro batches of data in parallel for each GPU, hence the name Data Parallelism.
Using a different micro batch for each GPU means we’ll have different gradients in each GPU, so to keep the model instances in sync across different GPUs, the gradients from the model instances are averaged using an operation called “all-reduce”, which happens during the backward pass, before the optimizer step.
This involves our first “distributed communication” primitive: all-reduce which handles the synchronization and communication between GPU instances and nodes.
TODO: embed naive DP: https://github.com/huggingface/picotron/blob/0035cce0e04afd6192763b11efe50010d8ad0f71/picotron/data_parallel/data_parallel.py#L10-L60
TODO: embed bucket DP: https://github.com/huggingface/picotron/blob/0035cce0e04afd6192763b11efe50010d8ad0f71/picotron/data_parallel/data_parallel.py#L62-L171
A naive DP implementation would just wait for the backward pass the finish so that we have all gradients, then it triggers an all-reduce over all DP ranks, to sync these gradients. But such an sequential steps of computation followed by communication is A BIG NO! Because we don’t want our GPUs to stay idle while communication is happening.
Instead we should try to overlap communication and computation whenever possible so that they happen at the same time as much as possible.
Let’s see three optimizations that are done in practice for this!
The main drawback of the naive DDP approach we’ve just described is that after the backward pass (computation), we have to wait for gradient synchronization (communication) before updating the parameters. Could we overlap this communication with our computation? The answer is yes!
As shown in the figure above, the gradients (red boxes) for a layer can be gathered and summed even before the gradients from earlier layers (red boxes to the left) have been computed. For example, as soon as the backward pass of the last layer is complete (last box on the right), those gradients can already be gathered and summed while the backward computations continue for earlier layers, moving toward the left.
This can be achieved in pytorch by attaching an all-reduce hook function to each parameter. An all-reduce operation is triggered as soon as the gradient for that parameter is ready, while gradients for other parameters are still being computed. This approach overlaps most of the all-reduce operations with gradient calculations, thereby improving efficiency. Here's a simple function to attach a hook:
Overlapping computation and communication reduces the time spent waiting for gradient synchronization across the entire model. Gradient synchronization can occur (at least partially) in parallel with backward pass, significantly speeding up data parallelism.
This is our first example of “overlapping computation and communication” which we will discuss several times in this blog post and is an essential technique to maximal scaling efficiency. Let's have a look how we can further improve the DP efficiency!
We can even go further with optimizing DP. For a given number of parameters to synchronize, GPU operations like collective communications are often more efficient when performing few calls on large tensors rather than many calls on smaller tensors. Therefore, instead of performing independent all-reduce for each gradient, we can group gradients into buckets and launch a single all-reduce for all the gradients within the same bucket. Think of it like packing items into boxes before shipping—it's more efficient to send a few big boxes than many small ones. By performing a single all-reduce operation for each bucket, we can significantly reduce communication overhead and speed up the communication operation.
As we’ve seen before, gradient accumulation works by performing multiple forward and backward passes before updating the parameters with optimizer.step(). When combining gradient accumulation with data parallelism, we should be careful when we want to synchronize gradients.
In a naive version, an all-reduce operation is automatically triggered after each backward pass during the accumulation, which is sub-optimal as a single reduce after the final step would have the same effect while reducing overhead.
In PyTorch, this is typically solved by adding a model.no_sync() decorator, which disables gradient synchronization, on the backward passes which don’t need reduction.
Let’s update our batch size equation with our newly learned Data Parallelism and Gradient Accumulation parameters:
Where
Given a targeted global batch size, we can thus trade gradient accumulation steps for data-parallel processes to speed up training. In practice, people tend to maximize the number of data-parallel nodes (DP) over gradient accumulation as much as possible since it's inherently parallel, unlike the sequential nature of gradient accumulation. Gradient accumulation is then added on top of data parallelism to achieve the target global batch size when scaling data parallelism alone is not sufficient before you run out of GPUs.
Being able to distribute the training over different samples gives us a first dimension of parallelization, thus making this 1D parallelism (we’ll progressively cover 4 more dimensions).
Let’s quickly summarize what we’ve seen up to now and how to setup our first 1D parallel training with a draft recipe for an optimal data-parallel setup:
GBST) either by consulting literature or running experiments measuring model convergence.If the gradient accumulation ratio is lower than one, i.e. we have too many GPUs a.k.a GPU-rich 🤑 (!), we can either choose to not use all our GPUs, explore a larger global batch size or test if a lower MBS will speed up training. In the latter case we’ll end up prioritizing throughput over individual GPU compute efficiency, using a smaller MBS than possible in order to speed up training.
Time to take a concrete example: Let’s say we want to train a recent model with a GBS of 4M tokens and a sequence length of 4k. This means our batch size will be 1024 samples (we pick powers of two). We observe that a single GPU can only fit MBS=2 in memory and we have 128 GPUs available for training. This means with 4 gradient accumulation steps we’ll achieve our goal of 1024 samples or 4M tokens per training step. Now what if we suddenly have 512 GPUs available? We can achieve the same GBS and thus identical training by keeping MBS=2 and setting gradient accumulation steps to 1 and achieve faster training!
While data parallelism cleverly overlaps the all-reduce gradient synchronization with backward computation to save time, this benefit starts to break down at large scales. As we add more and more GPUs (hundreds or thousands), the overhead of coordinating between them grows significantly. The end result? We get less and less efficient returns from each additional GPU we add to the system:
As expected, we can also see that the memory usage per GPU is not affected by adding more DP ranks for training.
We’ve explored data parallelism, our first (simple) strategy to scale training across more GPUs. It works like gradient accumulation but parallelizes the forward and backward passes on micro batches, thus increasing throughput!
The keen reader has already probably noted however that this assumes that we can fit at least one input sample forward pass (mbs=1) into our GPU memory. This is not always the case! As we can see, larger models don’t fit into a single GPU, even with activation recomputation activated:
Do we have other options for these larger models? We do have some solutions thankfully. They will involve either move some of these tensors to the CPU or split the weights/gradients/optimizer-states tensors across GPUs devices!
There are two main approaches to splitting: parallelism (tensor, context, or pipeline parallelism) and sharing (DeepSpeed Zero or PyTorch FSDP). Both approaches are somewhat orthogonal and can actually be combined! The sharing paradigm is closely related to DP so we’ll have a look at it first by investigating the ZeRO method!
So we have sharded the model’s parameters, gradients and optimizers states with ZeRO but we hit a limit once activation memory overtakes our memory budget. Welcome Tensor Parallelism (TP), a method which shards weights, gradients, and optimizers states as well as activations and without the need to gather them all prior to the computation. Seems like a dream! Let’s first have a look at how Tensor Parallel works with simple matrix multiplications.
Tensor Parallelism leverages the mathematical properties of matrix multiplication
This means that we can compute matrix product by either 1) multiplying each column of
nn.LinearIn practice a small example of the operation looks like this:
Let’s see how we can parallelise this operation! In tensor parallelism, tensors will be split into N shards along a particular dimension and distributed across N GPUs. Matrices can be split either on the column part or row part leading to row and column parallelism. One thing we’ll see in the following is that choosing row or column sharding will require different communications primitives.
Our first option is to use column-wise sharding (also called column-linear): We'll copy the complete input matrices to each worker, requiring an operation called broadcast, and split the weight matrix into columns. The inputs are then multiplied with the partial weight matrices, and the results are finally combined using an all-gather operation.
The second option is called row-wise sharding (also called row-linear): As the attentive reader might guess, row-linear means that we split the weight matrix into chunks of rows. However, this also requires us to split the inputs, which needs a scatter operation rather than a broadcast as used in column-linear sharding. The results on each worker are already in the right shape but need to be summed for the final result, thus requiring an all-reduce operation in this scenario.
We see here our fourth distributed primitive: scatter!
To come up with a strategy to follow, let’s move from a toy example to a real model building block. A Transformer model is made of two main building blocks : Feedforward layers (MLP) and Multi-Head Attention (MHA). We can apply tensor parallelism to both.
The Feedforward part can be parallelized by having a “Column linear” followed by a “Row Linear” which amounts to a broadcast to copy the input and an all-reduce in forward. Note that the broadcast isn’t needed in actual training where we can make sure inputs are already synced across TP ranks.
Now that we’ve found the most efficient schema for the Feedforward part of the transformer, let’s take a look at the multi-head attention block (MHA).
We can generally follow a similar approach where Q, K, and V matrices are split in a column-parallel fashion, and the output projection is split along the row dimension. With multi-head attention, the column-parallel approach has a very natural interpretation: each worker computes the attention for an individual or a subset of heads. The same approach works as well for multi-query (MQA) or grouped query attention (GQA) where key and values are shared between queries.
It's also worth noting that the tensor parallelism degree should not exceed the number of Q/K/V heads because we need intact heads per TP rank. And in case we’re using GQA, TP degree should be below number of K/V heads, otherwise it requires additional comms to keep them in sync. For instance, LLaMA-3 8B has 8 Key/Value heads, so the tensor parallelism degree should be less than or equal to 8, otherwise if TP=16 for example, we need to duplicate each K/V head and make sure they stay in sync.
Finally note that there is a tradeoff in terms of communication as we’ve added several distributed communication primitive directly in the computation path of our model. At the difference of ZeRO where we could prefetch, it can be harder to make these communication fully overlap with computations.
Looking at the timeline of operations in tensor-parallel MLP (same applies for Attention), we can better understand the tradeoffs involved. In the forward of each decoder layer, we hit a synchronization point with the AllReduce operation that cannot be overlapped with computation. This exposed communication overhead is necessary to combine partial results across tensor-parallel ranks before the final LayerNorm can be applied.
Tensor parallelism does help reduce activation memory for the matrix multiplications since the intermediate activations are sharded across GPUs. However, we still need to gather the full activations for operations like LayerNorm, which means we're not getting the full memory benefits we could. Additionally, it introduces significant communication requirements that heavily depend on the network infrastructure. The inability to hide this particular AllReduce behind computation means it directly adds to the critical path of forward propagation.
Impact of Tensor Parallelism on model performance and batch size capacity: while increasing TP leads to reduced per-GPU throughput (left), it enables processing of larger batch sizes (right), illustrating the trade-off between computational efficiency and memory availability in distributed training.
In practice, the communication overhead of tensor parallelism becomes particularly noticeable as we scale beyond 8 GPUs. While tensor parallelism within a single node can leverage fast NVLink interconnects, going across nodes requires slower network connections. As shown in the throughput plot above, we observe significant drops when moving from TP=8 to TP=16, and an even steeper decline from TP=16 to TP=32. This illustrates how communication costs can dominate at higher degrees of parallelism.
However, tensor parallelism provides important benefits for memory usage by distributing model parameters, gradients, optimizer states and activations (to some extent) across GPUs. Let's examine this effect on a 70B parameter model:
As we can see, increasing tensor parallelism reduces the memory needed for model parameters, gradients and optimizer states on each GPU. While tensor parallelism does help reduce activation memory in attention and feedforward layers by sharding the matrix multiplications across GPUs, we don't get the full memory benefits we could. This is because operations like layer normalization and dropout still require gathering the full activations on each GPU, partially negating the memory savings. We can do better by finding ways to parallelize these remaining operations as well.
This raises an interesting question - could we extend tensor parallelism to these remaining operations as well? Indeed, it's possible to parallelize layer norm, dropout and other operations too, which we'll explore next.
In regions where we apply tensor parallelism (TP), like attention and feedforward layers, each GPU only needs to operate on a portion of the hidden dimension since the weights are sharded. However, operations like layer norm or dropout (which is not used a lot anymore in LLM) require access to the full hidden dimension to compute correctly.
Rather than gathering the full hidden dimension on each GPU (which would defeat the memory benefits of TP), we can instead shard these operations along the sequence length dimension. This approach is called sequence parallelism (SP).
Sequence parallelism (SP) involves splitting the activations and computations for the parts of the model not handled by tensor parallelism (TP) such as Dropout and LayerNorm, but along the input sequence dimension rather than across hidden dimension. This is needed because these operations require access to the full hidden dimension to compute correctly. For example, LayerNorm needs the full hidden dimension to compute mean and variance:
where
So even though these operations are computationally cheap, they still require significant activation memory since they need the complete hidden dimension. SP allows us to shard this memory burden across GPUs by splitting along the sequence dimension instead.
In practice we’ll go from the left diagram to the right:
in forward: f = no-op ; f = all-reduce ; g = all-gather ; g = reduce-scatter in backward: f = all-reduce ; f = no-op ; g = reduce-scatter ; g = all-gather SP region needs full hidden_dim
The diagram shows how we transition between tensor-parallel and sequence-parallel regions using different collective operations (labeled "f" and "g"). The key challenge is managing these transitions efficiently while keeping memory usage low and maintaining correctness.
In the forward pass:
In the backward pass:
These operations "f" and "f" are called conjugate* pairs because they complement each other - when one is a no-op in forward, the other is an all-reduce in backward, and vice versa.
For sequence parallelism (SP), we use different operations labeled "g" and "g*". Specifically, we avoid using all-reduce in the SP region since that would require gathering the full activations and increase our peak memory usage, defeating the purpose of SP.
So what is actually happening here? As a famous LLM would say, let’s take it step-by-step:
Initial LayerNorm (SP Region)
First Transition (SP → TP)
First Linear Layer (TP Region)
Second Linear Layer (TP Region)
Final Transition (TP → SP)
A key advantage of sequence parallelism is that it reduces the maximum activation size we need to store. In tensor parallelism alone, we had to store activations of shape (b,s,h) at various points. However, with sequence parallelism, the maximum activation size is reduced to
It’s a bit difficult to keep track of all the parts that are sharded differently in TP and TP/SP - believe us, we find it hard to map as well so we made this small table to summarize how the activations (aka hidden_states ) shape change across hidden dimension h and sequence dimension s during a forward pass:
| Region | TP only | TP with SP |
|---|---|---|
| Enter TP (Column Linear) | h: sharded (weight_out is sharded) s: full |
h: sharded (weight_out is sharded) s: all-gather to full |
| TP Region | h: sharded s: full |
h: sharded s: full |
| Exit TP (Row Linear) | h: full (weight_out is full + all-reduce for correctness) s: full |
h: full (weight_out is full + reduce-scatter for correctness) s: reduce-scatter to sharded |
| SP Region | h: full s: full |
h: full s: sharded |
And for the embedding layer:
| Region | Vanilla TP | TP with SP |
|---|---|---|
| Embedding Layer (Row Linear sharded on vocab) | h: full (weight_out is full + all-reduce for correctness) s: unchanged |
h: full (weight_out is full + reduce-scatter for correctness) s: reduce-scatter to sharded |
You can find an example of implementation of both column and row linear TP in picotron: https://github.com/huggingface/picotron/blob/main/picotron/tensor_parallel/tensor_parallel.py
By using sequence parallelism, we can achieve even greater activation memory savings, allowing us to push our batch size and sequence length further than what would be possible with tensor parallelism alone. Let's see what that means for our previous 70B model example:
Does that mean that SP incurs more communication than TP? Well, yes and no. In the forward of a vanilla TP we had two all-reduce per transformer block, and in SP we have two all-gather and two reduce-scatter per transformer block. So SP does twice the number of communication operations as TP. But since an all-reduce operation can be broken down into to an all-gather + reduce-scatter (see in [TODO: Appendix link]) they’re actually equivalent in terms of communication. Same reasoning for backward as we just use the conjugate of each operation (no-op ↔ allreduce and allgather ↔ reducescatter).
If you’ve been paying close attention, you’ll notice that we’re talking about 4 comms ops in each layer (2 for Attention and 2 for MLP). This is how the MLP profiling looks like when using Tensor + Sequence Parallelism:
Besides the fact that TP requires communications in each layer, it also can’t easily be overlapped with compute, which makes throughput heavily dependent on the communication bandwidth. This is why TP is usually done only within a node (TP≤8).
As you might expect, this communication overhead becomes increasingly problematic as we scale up tensor parallelism. To illustrate this, let’s check throughput as we scale TP with SP for a 3B model:
Impact of combined Tensor and Sequence Parallelism (TP/SP) on a 3B model’s performance and memory utilization with 4096 seqlen: when scaling both TP and SP together, there's a trade-off between computational efficiency (left) and memory capacity (right). While higher parallelism degrees reduce per-GPU throughput, they enable processing of significantly larger batch sizes by reducing the activation memory.
Let’s summarize our observations:
We have seen how TP helps us shard activations across several GPUs by splitting the attention and feedforward operations along the hidden dimension and how SP is a natural complement for the remaining operations by splitting along the sequence dimension.
However, there are two limits to TP and SP: 1) if we scale the sequence length the activation memory will still blow up in the TP region and 2) if the model is too big to fit with TP=8 then we will see a massive slow-down due to the inter-node connectivity.
We can tackle problem 1) with Context parallelism and problem 2) with Pipeline parallelism. Let’s first have a look at Context parallelism!
For attribution in academic contexts, please cite this work as
XXX, et al., "The Ultra-Scale Playbook: Training LLMs on GPU Clusterse", 2025.
BibTeX citation
@misc{TODO,
title={The Ultra-Scale Playbook: Training LLMs on GPU Clusters},
author={TODO},
year={2025},
}