The Tool Desk
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What Kubernetes does—and what LLM serving adds
Kubernetes orchestrates workloads: it schedules containers, manages services and supports scaling. It does not, by itself, make routing decisions based on prompt length, KV-cache locality or the different resource demands of processing a prompt and generating tokens. LLM-serving projects add those inference-specific decisions above the cluster layer.
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That distinction matters because a deployment can be healthy by ordinary Kubernetes measures while serving poorly. A high GPU utilization figure, for example, does not necessarily tell you whether requests are queued, time to first token is growing or cached prompt state is being reused effectively. Inference-aware routing and scaling aim to account for those signals.
Which parts have documented implementation patterns?
A Kubernetes API for common serving topologies
KServe’s version 0.20 documentation describes LLMInferenceService, a custom resource aimed at generative workloads. It is distinct from KServe’s traditional InferenceService and documents deployment patterns for single-node serving, multi-node serving and disaggregated prefill/decode. The API gives teams a higher-level way to express an LLM workload; it does not remove the need to select and validate the serving topology.
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An inference-aware layer around vLLM
llm-d documents a composable, vLLM-centered serving layer for coordinating a fleet. Its documented capabilities include prefix-aware routing, distributed KV indexing and offload, separating prefill from decode, expert-parallel execution, and SLO-aware autoscaling and flow control. The project recommends introducing capabilities as bottlenecks require them rather than treating the entire stack as a mandatory starting point.
A modular distributed-serving framework
NVIDIA’s Dynamo documentation describes support for vLLM, SGLang and TensorRT-LLM, with deployment on Kubernetes, Slurm or locally and adoption from an individual component to a fuller stack. Its current documentation lists NVIDIA and AMD GPUs and Intel XPUs. That is the project’s stated support scope, not proof that every engine, accelerator and feature combination works identically; check the matrix for the precise version and deployment you plan to run.
| Option | Documented role | Useful when | What to verify |
|---|---|---|---|
KServe LLMInferenceService (v0.20 documentation) |
Kubernetes API for single-node, multi-node and disaggregated serving patterns. | You want a higher-level Kubernetes resource for an LLM deployment. | Which topology and underlying serving components the chosen configuration requires. |
| llm-d | Composable vLLM fleet coordination with routing, KV-cache and scaling capabilities. | You need to address bottlenecks such as cache locality or separate prefill/decode demand. | Engine, hardware, networking, and operational behavior for the selected capabilities. |
| NVIDIA Dynamo (documentation identifies v1.5.0 as latest) | Modular distributed serving framework with multiple documented inference engines and deployment environments. | You want to adopt a serving component or build toward a broader distributed stack. | The specific framework-and-accelerator feature matrix for your release. |
These choices are not mutually exclusive categories of proof that one stack is best. Compare their abstraction level, serving topology, engine and accelerator compatibility, routing and state handling, and the operational work each leaves to your team.
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How to serve an LLM on Kubernetes
A useful deployment plan starts with the workload rather than the most elaborate architecture. Begin with the simplest topology that can meet the service’s measured latency and throughput needs, then add distributed features only when tests show where it falls short.
- Describe the workload. Record the model, typical and long prompts, expected concurrency, latency objectives and whether the service is dominated by prompt processing, token generation or both. These are the conditions your later comparisons must preserve.
- Choose an engine and supported hardware. Select a documented serving framework and verify its compatibility with the intended accelerator, release and deployment mode. Project-level support statements do not establish support for every combination.
- Select a topology. Use a single-node pattern when one node can serve the model and meet the workload target. Consider multi-node serving when the model or throughput needs require distributed execution. Consider separate prefill and decode pools only if their distinct demand or performance characteristics justify the extra coordination.
- Choose the Kubernetes abstraction. For a KServe-based deployment, review the version 0.20
LLMInferenceServicepatterns and the resources they configure. For llm-d or Dynamo, map the desired capabilities to that project’s deployment documentation and release-specific requirements. - Instrument and test the service. Track user-facing latency, queue depth, throughput, accelerator availability and, where relevant, KV-cache utilization. Test the actual model, request distribution, concurrency and target hardware; a healthy pod is not evidence that the inference service meets its objective.
- Exercise lifecycle and recovery behavior. Test startup and model loading, scale-up and scale-down, worker replacement, network setup and in-flight requests. For a disaggregated design, include cache-transfer failures and prefill-worker shutdown in the test plan.
The deployment documentation establishes available patterns, not a universal manifest or a guarantee that a workload will meet its SLO. Configuration details depend on the chosen release, engine, hardware and topology.
How to scale LLM inference on Kubernetes
Use inference signals, not replica counts alone
KServe documents Workload Variant Autoscaler configuration using signals such as queue depth and KV-cache utilization. Its documented actuator paths include HPA and KEDA. These signals can help scaling respond to inference demand that GPU utilization alone may not capture, but the autoscaler cannot create accelerator capacity or make a newly started model ready instantly.
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Scale prefill and decode according to their separate demand
In a disaggregated deployment, KServe documents the ability to scale prefill and decode pools independently. That lets operators respond to imbalances between prompt processing and token generation. It also means capacity planning must account for both pools, their model and hardware requirements, and the work of transferring KV state between them.
Include capacity and startup in the control loop
Replica targets are only one part of scaling. Model loading, accelerator availability, cluster topology and concurrency constrain how quickly new serving capacity becomes useful. Set and test scaling behavior against those limits; inference-aware metrics improve the control signal but do not eliminate them.
What disaggregation improves—and what it complicates
Prefill/decode disaggregation runs prompt processing and token generation in separate worker pools. It can help when the two stages have different bottlenecks, but it makes communication and lifecycle coordination part of the serving path. KV state produced during prefill must be available to decode workers, and changes in pool membership can affect that transfer.
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llm-d’s operational documentation, accessed October 3, 2026, describes a new NIXL handshake establishing an RDMA connection at roughly five seconds per prefill/decode worker pair. The same page says prefill-worker shutdown cannot currently wait for every KV block to be retrieved, so an in-flight decode may fail to load its cache. The documented mitigation is to recompute prefill on the decode worker: that can preserve progress, but costs extra work.
These details make scale-down and worker replacement important test cases, not just infrastructure housekeeping. A design that performs well while all workers remain stable may behave differently during routine cluster changes.
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What the published benchmarks do—and do not—show
llm-d’s integration documentation, dated July 28, 2026, reports the following representative results. Each figure applies to its stated model, hardware and comparison:
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| Reported result | Workload and comparison | Attribution |
|---|---|---|
| 3× output throughput and 2× faster time to first token | Prefix-aware routing versus round-robin; Llama 3.1 70B on AMD MI300X. | llm-d project documentation, 2026. |
| Up to 70% higher tokens per second | Prefill/decode disaggregation; GPT-OSS on NVIDIA B200. The documentation’s stated result is workload-specific. | llm-d project documentation, 2026. |
| 13.9× throughput | Hierarchical KV offloading at high concurrency versus GPU-only; NVIDIA H100. | llm-d project documentation, 2026. |
These are project-reported comparisons, not independent guarantees or predictions for a different model, prompt mix, concurrency level or cluster. Use them to identify techniques worth evaluating, then reproduce the comparison on the hardware and workload that matter to your service.
What remains unproven at the ecosystem level
Documentation can establish that a project describes a capability and offers an implementation pattern. It cannot, on its own, establish broad adoption, universal reliability, uptime distributions or lower total cost across providers. The cited material identifies llm-d as a CNCF Sandbox project, but that status and the documented feature sets are not substitutes for production evidence tied to a specific release and workload.
There is no industry-wide adoption or cost statistic established by these sources. For an operational decision, compare the complete system you would run—including accelerator time, model loading, networking, cache movement, engineering effort and failure recovery—rather than treating a project benchmark as a cost or reliability result.
Quick Recap
A practical decision rule
- Start with a single-node deployment if it can satisfy the measured workload; do not add distributed components without a demonstrated need.
- Evaluate inference-aware routing when request distribution and cache locality are limiting performance.
- Evaluate disaggregation when prompt processing and token generation need different capacity or show distinct bottlenecks, and include KV transfer and worker lifecycle behavior in the evaluation.
- Choose an autoscaling signal and actuator based on the service’s demand and capacity constraints, not on the assumption that adding replicas instantly adds usable model capacity.
- Require a workload-matched test on the target release and hardware before treating a documented feature or project benchmark as a production result.
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