Distributed LLM Inference: How Large AI Models Scale Across GPUs
Published:
Some of today’s largest AI models contain more than a trillion parameters. At two bytes per parameter, storing a trillion parameters alone requires roughly 2 TB of memory, before accounting for the memory used while serving requests. That is far beyond the capacity of a single high-memory GPU.
Yet production AI services can serve enormous numbers of users. They do so by coordinating multiple GPUs and, in some deployments, multiple servers. This is distributed inference: dividing model storage, computation, or incoming requests across interconnected devices.
The challenge is not simply making a large model fit. A production system must solve three problems simultaneously:
- Model footprint: Can the model weights fit in available accelerator memory?
- KV-cache growth: Is there enough working memory for active requests and long contexts?
- Throughput and latency: Can the system handle many simultaneous users without excessive queuing?
Different parallelism techniques address different constraints.
1. Data Parallelism: More Replicas for More Traffic
Suppose a model and its working memory fit comfortably on one GPU, but requests arrive faster than that GPU can process them.
The simplest solution is to run multiple complete replicas of the model and distribute requests among them.
Incoming Requests
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Load Balancer
/ | \
v v v
GPU 1 GPU 2 GPU 3
Model Model Model
Copy Copy Copy
Each replica holds the full model and handles its assigned requests independently. A scheduler can route based on load, queue length, or opportunities to reuse cached context.
Best for: Increasing serving capacity when a full model fits on each replica.
Trade-off: Every replica needs its own copy of the weights, so data parallelism does not solve the problem of a model that is too large for one GPU.
2. Pipeline Parallelism: Split the Model by Layers
A transformer model contains a sequence of layers. If the entire model cannot fit on one GPU, we can place different groups of layers on different GPUs.
Input
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v
GPU 1: Layers 1–8
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v
GPU 2: Layers 9–16
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v
GPU 3: Layers 17–24
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v
Output
This is pipeline parallelism. Each device stores only its assigned layers, and intermediate activations move from one stage to the next.
The main challenge is utilization. With only one request in flight, later stages wait for earlier ones, leaving some GPUs idle. Keeping multiple requests or microbatches in flight can make the pipeline behave more like an assembly line.
Best for: Splitting a model across devices by layer groups.
Trade-off: Communication between stages and idle time (pipeline bubbles) can reduce efficiency, particularly for latency-sensitive, token-by-token generation.
3. Tensor Parallelism: Split the Computation Within Layers
Pipeline parallelism divides layers. Tensor parallelism divides the large matrix operations inside a layer.
For example, several GPUs can compute different slices of a matrix multiplication and then combine the partial results.
Transformer Layer
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Split Tensor Operation
/ | \
v v v
GPU 1 GPU 2 GPU 3
\ | /
Collective Operation
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Next Layer
The combination is performed using distributed collective communication, such as all-reduce or all-gather, depending on the operation.
Because GPUs may need to communicate repeatedly within each layer, tensor parallelism depends heavily on high-bandwidth, low-latency interconnects. It is often most practical among GPUs within the same server or tightly coupled accelerator domain.
Best for: Splitting large layers and accelerating computation across closely connected GPUs.
Trade-off: Frequent synchronization and communication can erase the benefits when interconnects are slow.
4. Expert Parallelism: Distribute Mixture-of-Experts Models
Mixture-of-Experts (MoE) models contain many expert subnetworks, but a router activates only a subset for each token.
Rather than copying every expert to every GPU, expert parallelism distributes experts across devices.
Token Representations
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Router
/ | \
v v v
GPU 1 GPU 2 GPU 3
Experts Experts Experts
\ | /
Combine Outputs
At each MoE layer, tokens are dispatched to the experts selected by the router. Their outputs are then collected and combined.
This makes it possible to store a very large total set of expert weights while activating only a fraction of them for each token.
Best for: Distributing large MoE expert collections across GPUs.
Trade-off: Token dispatch and output gathering generate substantial GPU-to-GPU communication. Expert load imbalance can also create bottlenecks.
Not every expert has a simple human-readable specialization; specialization is generally learned during training.
5. Prefill vs. Decode: Two Different Inference Workloads
Even after distributing model weights, inference still involves two phases with different hardware demands.
Prefill
During prefill, the model processes the input prompt and constructs the initial KV cache—stored attention keys and values that can be reused during subsequent generation.
Prefill processes many prompt tokens in parallel and is often relatively compute-intensive.
Input Prompt → Parallel Processing → Initial KV Cache
Decode
During decode, the model generates output tokens autoregressively, typically one token at a time per sequence. Each step reuses the accumulated KV cache and adds information for the newly generated token.
KV Cache + Current Token
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Next Token
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Updated KV Cache
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Repeat
Decode often becomes memory-bandwidth-sensitive, particularly when generating relatively few tokens per step. Its memory demand also grows as active sequences become longer.
These are useful generalizations, not universal laws: the actual bottleneck depends on batch size, sequence length, model architecture, and hardware.
6. Prefill/Decode Disaggregation
Because prefill and decode stress hardware differently, a serving system can assign them to separate GPU pools.
Incoming Request
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v
Prefill GPU Pool
- Process prompt
- Build KV cache
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| Transfer KV cache
v
Decode GPU Pool
- Generate tokens
- Extend KV cache
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v
Response
This is called prefill/decode disaggregation.
The idea is to optimize each pool for its own workload rather than having the two phases compete for resources on the same GPUs.
However, disaggregation introduces a costly handoff: the initial KV cache must move from the prefill pool to the decode pool. It is only beneficial when the scheduling and transfer overhead are lower than the gains from specialization. High-throughput, low-latency networking is therefore especially important.
Best for: Serving environments where separating the two phases improves utilization or latency under substantial load.
Trade-off: KV-cache transfer, network overhead, and additional scheduling complexity.
7. Putting the Techniques Together
These strategies are not mutually exclusive. Large deployments often combine them in what is sometimes called multi-dimensional parallelism.
| Technique | What is distributed? | Main problem addressed | Main trade-off |
|---|---|---|---|
| Data parallelism | Entire model replicas | Request throughput | Duplicate weight memory |
| Pipeline parallelism | Groups of layers | Model does not fit on one device | Pipeline bubbles and stage transfers |
| Tensor parallelism | Operations within layers | Large layers and distributed compute | Frequent collective communication |
| Expert parallelism | MoE experts | Large expert weight collections | Token dispatch and load imbalance |
| Prefill/decode disaggregation | Inference phases | Different compute/memory demands | KV-cache transfer overhead |
A hypothetical deployment could use tensor parallelism within tightly connected GPU groups, pipeline parallelism between groups, expert parallelism for MoE layers, and data parallelism to replicate the resulting serving configuration.
If beneficial, prefill and decode could also be assigned to different pools.
Request Router
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+------------+------------+
| |
Replica A Replica B
| |
Prefill / Decode Prefill / Decode
GPU Groups GPU Groups
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TP + PP + EP as needed TP + PP + EP as needed
The right combination depends on the model, network topology, hardware memory, traffic pattern, and latency targets.
8. The Orchestration Layer
Distributed inference requires more than placing weights on GPUs. A production orchestration layer must coordinate:
- Request routing: Send requests to appropriate replicas and GPU pools.
- Load balancing: Prevent a small subset of devices from becoming overloaded.
- Scheduling: Manage concurrent requests and their varying context lengths.
- Cache management: Allocate and reclaim KV-cache memory efficiently.
- Failure handling: Detect unhealthy workers and recover or retry when possible.
- Observability: Track throughput, queueing, latency, memory pressure, and failures.
The orchestration layer turns a collection of GPUs into a usable model-serving system. Distributed execution alone does not guarantee that requests will survive failures or meet latency targets; those properties require careful design.
9. Choosing the Right Strategy
A practical starting point is to identify the limiting resource.
If the model fits, but user traffic is too high: Add replicas using data parallelism.
If model weights do not fit: Split the model using pipeline or tensor parallelism, depending on layer sizes and interconnects.
If the model is MoE: Consider expert parallelism to distribute expert weights.
If prefill and decode interfere under load: Evaluate disaggregation, including the cost of transferring KV caches.
If the GPUs are underutilized or requests are queuing: Inspect scheduling, batch behavior, and communication overhead before assuming that adding GPUs will fix the problem.
More parallelism is not automatically better. Every technique trades memory or compute limitations for communication and coordination costs.
Final Thoughts
Distributed LLM inference is fundamentally about matching a model’s computational and memory requirements to the topology of available hardware—while meeting the needs of real users.
The core ideas are straightforward:
Too much traffic? Replicate the model.
Too much model? Split the weights across GPUs.
Too much competition between inference phases? Consider separating prefill and decode.
In practice, large-scale inference systems combine multiple forms of parallelism and rely on an orchestration layer to manage requests, memory, communication, and failures.
The challenge is not simply getting a massive model to run. It is getting that model to run efficiently, reliably, and at production scale.