LLM Inference

Imagine a restaurant kitchen at 8 PM on a Saturday. One chef is twenty minutes into chopping vegetables for a twelve-course tasting menu, while thirty tables that ordered an hour ago watch their half-finished plates get cold every time the chef turns back to the chopping board. No real kitchen runs this way: prep cooks handle the chopping in the back, line cooks handle the plating up front. Two different jobs, two different stations.

Prefill/decode disaggregation runs each inference phase on its own GPU pool

Prefill/decode disaggregation is a serving architecture that runs the two phases of LLM inference on separate GPU pools. Prefill (processing the input prompt) runs on one set of GPUs, and decode (generating the response token by token) runs on another. When prefill finishes, the model’s memory of the prompt (the KV cache) is sent over the network to a decode GPU, which then generates the response.

Prefill and decode compete for opposite GPU resources

The two phases of LLM inference, prefill and decode, stress different hardware limits. Prefill is bound by compute, while decode is bound by memory bandwidth. Separating them stops long prompts from interfering with everyone else’s token generation. However, this separation comes at the cost of extra hardware, network transfers, and operational complexity.

Disaggregation trades shared-GPU interference for a KV cache transfer

Disaggregated serving splits inference across two separate pools of GPUs: a prefill pool that processes incoming prompts, and a decode pool that generates tokens. When the model finishes reading the prompt, it has built up a working memory of everything it read, which is the KV cache. In a traditional setup, this memory just sits on the same GPU that will generate the response. In a disaggregated setup, it has to physically travel across the network from the prefill machine to the decode machine before a single token can be generated.

Mooncake, DeepSeek, and NVIDIA Dynamo have shipped disaggregation in production

Several real systems have shipped disaggregated serving, including Mooncake, DeepSeek, and NVIDIA Dynamo. These systems have reported improvements in throughput and latency, but also highlight the challenges of managing the KV cache transfer between machines.

Disaggregation isn’t always the right choice

Disaggregated inference may not always be the best choice, as it can require more hardware and increase complexity. The decision to use disaggregated serving should be based on the specific workload and requirements of the application.

Technology teams are watching llm inference closely because changes in this space often arrive faster than internal policies can adapt.

For product and engineering leaders, the practical question is how this could reshape roadmaps, vendor choices, and security reviews over the next few quarters.

Organizations that document lessons early tend to respond more calmly when similar patterns appear again.

In many companies, the first impact shows up in planning meetings: teams reassess priorities, revisit risk registers, and check whether existing tooling still fits.

Smaller businesses feel these shifts too. A single platform change or market move can affect customer trust, delivery timelines, and hiring plans.

The most resilient teams treat stories like this as input for quarterly reviews rather than one-day headlines.

If your business depends on modern software, ERP, VoIP, or customer-facing apps, staying informed helps you separate noise from decisions that require action.

Looking ahead, disciplined follow-through matters: assign owners, set review dates, and measure whether your response improved outcomes.

Security and compliance stakeholders should ask whether current controls still match the pace of change described in this update.

Operations leaders can reduce friction by translating the headline into a short internal brief with clear next steps for each department.

Customer support teams may see early signals through tickets, outages, or policy questions long before leadership reviews are scheduled.

Finance and procurement groups should note whether licensing, vendor risk, or implementation costs need revisiting after this development.

Training programs benefit from timely updates so staff understand what changed, what did not change, and what requires escalation.

Architecture reviews are a practical place to test assumptions, especially when new tools, platforms, or threats enter the conversation.

Documentation quality often determines how quickly a company recovers from surprises; capture decisions while context is still clear.