What Does It Mean to Disaggregate Data? A Practitioner’s Guide
It was 2023. We were running inference on a cluster of A100s for a client who needed low-latency answers from a 70B model. Every request felt like a gamble. Sometimes it took 200ms. Sometimes 8 seconds. The GPU memory was the bottleneck — one request’s prefill phase would hog 40GB, and then a decode request would sit idle waiting for memory to free up. I remember staring at a utilization graph: 30% average, spikes to 95%, then nothing. Wasted capacity.
That’s when I first asked: what does it mean to disaggregate data? Not as an academic question. As a fix for a broken system.
Disaggregation is the separation of tightly coupled resources — compute, memory, storage — so each can scale independently. In production AI, it’s the difference between a single monolithic server choking on traffic and a cluster that flexes like elastic. You decouple the prefill stage from the decode stage. You separate storage from compute. You stop treating the GPU as a sealed box and start treating it like a pool of services.
In this guide, I’ll walk you through the real-world meaning of disaggregation — not the PowerPoint version. We’ll cover disaggregated inference, disaggregated storage, the trade-offs, and the gotchas I’ve learned the hard way. By the end, you’ll know exactly when to reach for disaggregation and when to run the other way.
The Core Idea: Stop Treating Hardware Like a Monolith
Most people think disaggregation is about networking — putting NVMe over fabric or using RDMA. That’s a detail. The real answer to what does it mean to disaggregate data? is simpler:
You stop coupling the lifecycle of data with the lifecycle of compute.
In a monolithic setup, when you spin up a container, you get a fixed amount of CPU, RAM, and storage. If your inference request needs more memory than compute, that container wastes cycles. If your training job needs more I/O than memory, you over-provision storage. Everything is a compromise.
Disaggregation says: let the prefill server live in one pool of GPUs, the decode server in another. Let the model weights sit in a shared memory tier, not on local disks. Let storage be a shared fabric, not a SATA cable.
The result? You can scale prefill capacity independently of decode. You can add storage without adding compute. You can survive a GPU failure without losing data.
Sounds perfect, right? It’s not. But it’s close.
Disaggregated Inference: Prefill vs. Decode
The most practical form of data disaggregation in AI today is splitting the inference pipeline into two stages — prefill and decode.
Disaggregated inference separates the compute-intensive prefill phase (where the model processes the input prompt and builds a KV cache) from the memory-intensive decode phase (where tokens are generated one at a time). They run on different machines. Different GPUs. Different schedules.
Why? Because prefill saturates compute — it’s a batch of matrix multiplies. Decode is bandwidth-bound — it’s iterative reads from the KV cache. In a monolithic system, one request’s prefill steals memory from another request’s decode. In a disaggregated system, each phase gets its own dedicated hardware.
I tested this setup on a cluster of 8xA100s using the architecture described in Disaggregated Inference: 18 Months Later. We assigned 2 GPUs to prefill, 6 to decode. Latency dropped by 40%. Throughput doubled. The prefill GPUs ran at 90% utilization; decode GPUs ran at 70%. No more idle wait.
But here’s the contrarian take: disaggregated inference adds network latency. You have to transfer the KV cache from the prefill node to the decode node over the network. If your interconnect is slow (say, 25 Gbps), you lose the benefit. You need at least 100 Gbps RDMA. And even then, the serialization overhead matters. vLLM’s experimental support for disaggregated prefilling is a good start, but we found that torch.distributed is too heavy for this. We ended up using a custom async zero-copy transport over NVLink.
How to Configure It
Here’s a simplified config for a disaggregated vLLM setup:
yaml
# prefill_server.yaml
model: meta-llama/Llama-3-70b-chat-hf
gpu_memory_utilization: 0.95
max_num_seqs: 32
pipeline_parallel_size: 1
tensor_parallel_size: 2
phase: prefill
kv_cache_backend: remote
yaml
# decode_server.yaml
model: meta-llama/Llama-3-70b-chat-hf
gpu_memory_utilization: 0.85
max_num_seqs: 64
pipeline_parallel_size: 1
tensor_parallel_size: 2
phase: decode
kv_cache_input: remote
The KV cache is stored in a shared memory pool (we used NVIDIA’s NVLink Switch Fabric). The prefill server writes the cache; the decode server reads it. In practice, you need a coordinator to assign requests and manage load balancing. We built ours on top of NVIDIA TensorRT-LLM’s disaggregated serving, which already handles cache migration.
One thing nobody tells you: the prefill-to-decode ratio isn’t fixed. In our workload, 1:3 worked for short prompts (up to 1K tokens). For long documents (8K+), we needed 1:5. You have to monitor and rebalance. We wrote a feedback loop that adjusts the allocation every 10 minutes based on a rolling window of prompt lengths.
Disaggregated Storage: More Than “Slower but Cheaper”
The second big question is what does disaggregated storage mean? In the data infrastructure world, it’s separating storage from compute — putting your data on a shared fabric that many compute nodes can access, rather than on local disks that belong to one machine.
This isn’t just about cost. It’s about state. When you’re serving models, you need to store model weights, KV caches, and training checkpoints. If those weights live on local SSD, every time a GPU node fails you have to reconfigure everything. If they live on a disaggregated storage tier, you can spin up a new node and point it at the same storage in seconds.
We run a cluster of 64 H100s for a client in healthcare (name withheld). They have 50+ fine-tuned models, each ~150GB. Without disaggregated storage, managing versions was a nightmare — you’d update one model and have to copy it to every node. With an all-flash NVMe-over-Fabric (NVMe-oF) tier, we load any model on any node in under a second. The storage is a shared namespace, not a local disk.
But disaggregated storage has a latency penalty. Accessing remote storage over network is always slower than local PCIe — maybe 2-5 microseconds vs. 100 nanoseconds. For inference, that’s fine because weights are cached in GPU memory. For training, where you read and write checkpoints often, it can hurt. We mitigated this by using local NVMe as a write-back cache and the disaggregated pool as persistent backend. The wrote-through is async, so checkpoint writes feel local.
A Code Example: Accessing Disaggregated Storage in Python
python
import torch
from s3fs import S3FileSystem # Example for object-store backend
import ray
@ray.remote
def load_weights(model_name: str):
fs = S3FileSystem()
# model weights stored as torch tensors in s3-like disaggregated storage
with fs.open(f"models/{model_name}/weights.pt", "rb") as f:
weights = torch.load(f, map_location="cuda")
return weights
# On each GPU node
weights_ref = load_weights.remote("my-finetune-v3")
print("Weights loaded from disaggregated storage")
Ray handles the distributed loading. The disaggregated storage (S3-compatible object store with NVMe back-end) serves the weights. No local copies.
When Not to Disaggregate
I’ve painted a rosy picture. Let me balance it.
Don’t disaggregate if your model fits in a single GPU memory.
If you’re running a 7B model on an H100, you can fit the whole thing (weights + KV cache) without splitting. Disaggregation adds complexity, network dependency, and potential failure points. You’re better off using a monolithic approach with batch scheduling. We tested this — for 7B models with batch sizes under 32, disaggregation was actually slower because of the cache transfer overhead.
Don’t disaggregate if your interconnect is slow.
We ran a test on 40 Gbps Ethernet. The KV cache took 80ms to transfer. That killed any latency benefit. Use at least 100 Gbps RoCE or InfiniBand. And make sure your software stack is tuned — we spent two weeks debugging kernel parameters (net.core.rmem_max, etc.) before we got consistent sub-millisecond transfers.
Don’t disaggregate if your team can’t operate a distributed system.
Disaggregation turns a single-machine problem into a multi-machine orchestration problem. You now need a scheduler (we use Kubernetes with custom schedulers), a state store (etcd or Redis), health checks, and fallback logic. If your team is two people who don’t know how to debug a network timeout, start monolithic.
The Production Reality: What We Learned at SIVARO
In 2025, we deployed a 300-GPU cluster for a real-time chat application. The client had strict SLAs: 95th percentile latency under 1 second for 2048-token responses. Monolithic could do it, but only at 30% utilization. Disaggregation let us push utilization to 75% while meeting the SLA.
Key takeaways:
- Prefill servers need high compute: We used H100s with 80GB memory, set
max_num_seqslow (16) to keep each prefill batch small and fast. - Decode servers need high memory bandwidth: We used H100s with 80GB but we set
max_num_seqshigh (128) to soak up any memory reads. - Cache migration is the bottleneck: We reduced it by pinning KV cache page tables in memory and using GPUDirect RDMA (GDRCopy) to avoid a CPU copy. The Rafay article on disaggregated inference covers this well.
- Observe, don’t guess: We used Prometheus and custom metrics for prefill/decode latency, cache transfer time, and network queue depth. We found that 90% of latency spikes came from network retransmissions due to buffer bloat. We had to tune the NIC ring buffer size.
One specific number: cache transfer time for a 1K prompt (KV cache size ~120MB for Llama 3 70B) over 200 Gbps InfiniBand took 2.6 milliseconds. With 100 Gbps RoCE it was 4.1 ms. With 40 Gbps it was 22 ms. That 22 ms was enough to break our SLAs.
What Does It Mean to Disaggregate Data? — The Broader Picture
Let me return to the founding question: what does it mean to disaggregate data? At the architectural level, it means you stop building systems where data and compute are born, live, and die together. You treat data as a durable substrate that compute nodes tap into and out of.
That has implications beyond inference:
- Data pipelines: You can disaggregate storage from processing in ETL. Use S3 as a shared buffer, spin up ephemeral CPUs per task, no data locality needed.
- Model training: Instead of one giant machine with local NVMe, you share a high-speed storage fabric across many trainers. Checkpoint writes become snappier because they’re async to the fabric.
- Multi-tenant AI: Each tenant gets a slice of compute but all share a disaggregated model repository. Updates to a model are instantly available to all tenants.
The SambaNova blog on disaggregated inference makes a good point: disaggregation enables elastic scaling. You can add prefill nodes during peak hours and drop them at night. You can’t do that if every node has local storage with a unique state.
FAQ: Disaggregation in Practice
Q: What does it mean to disaggregate data? (short answer)
A: It means separating the management of data (storage, caching, state) from the computation that uses it. They become independent resources that can be scaled, failed, and upgraded separately.
Q: What does disaggregated storage mean for inference?
A: Model weights and KV caches are stored on shared storage (like NVMe over Fabrics or S3) instead of local disks. This lets any GPU node serve any model, reduces duplication, and simplifies failover.
Q: Do I need to rewrite my model code for disaggregated inference?
A: Not necessarily. Frameworks like vLLM and TensorRT-LLM have built-in support. You just configure the phase and add a network transport for the KV cache. Our code changes were about 200 lines of orchestration, not model logic.
Q: What’s the cost trade-off?
A: Disaggregation adds networking hardware (switches, NICs) and management overhead. For large clusters (50+ GPUs), the utilization gains outweigh the cost. For small clusters (4-8 GPUs), the overhead often negates the benefit.
Q: Does disaggregation help with multi-round conversations?
A: Yes, but it’s tricky. Prefill handles the new input, but the KV cache from previous rounds must be kept alive. In our setup, we kept the decode server alive per session and reused its KV cache across rounds. The arXiv paper on efficient multi-round inference shows session-aware scheduling can cut cache transfer by 40%.
Q: What about data privacy in disaggregated storage?
A: You now have network in the path. We encrypt all cache transfers using TLS 1.3. For storage at rest, we use AES-256. But disaggregation introduces a larger attack surface. Use network segmentation and mTLS between nodes.
Q: Is disaggregation the same as serverless?
A: Not exactly. Serverless abstracts compute away from the developer. Disaggregation abstracts storage from compute. They work well together — disaggregated storage makes serverless more portable because state isn’t tied to a machine. But serverless adds cold-start issues that disaggregation doesn’t.
Q: When should I NOT disaggregate?
A: When your model fits in one GPU, when your network is slow, when your dataset fits in local RAM, or when your team doesn’t have operations maturity. Start monolithic. Move to disaggregated when you hit a utilization or scaling wall.
Looking Ahead: 2026 and Beyond
We’re at an inflection point. In 2026, disaggregation is no longer experimental — it’s a production strategy for anyone running large models. The major clouds (AWS, GCP, Azure) now offer “disaggregated inference” as a service offering. AWS has “Inference Pods” that separate prefill and decode under the hood. Google’s TPU v6p has a dedicated KV cache network.
But the hard problems remain: observability, debugging latency, and managing state across nodes. We’ve built a tool at SIVARO that traces every cache transfer and flags slow hops. It saves us hours of debugging each week.
If you’re evaluating disaggregation, start small. Pick one model, one workload, and one week of monitoring. Measure your prefill vs. decode ratio. Then decide.
The answer to what does it mean to disaggregate data? isn’t a definition. It’s a choice to trade simplicity for efficiency. For most teams running models above 30B parameters, it’s the right trade.
Nishaant Dixit — Founder of SIVARO. Building data infrastructure and production AI systems since 2018. Built systems processing 200K events/sec.