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Kubernetes can run LLM inference, but it does not by itself solve LLM serving. In 2026, projects including KServe, llm-d and NVIDIA Dynamo document ways to add inference-aware APIs, routing, KV-cache handling, distributed execution and autoscaling. Those are usable implementation patterns—not proof that every workload is turnkey, universally reliable or cheaper to operate. The right design depends on the model, hardware, traffic and operational trade-offs you can validate.
What does Kubernetes solve for LLM serving?
Kubernetes provides the orchestration substrate: it can manage workloads and their lifecycle across a cluster. LLM-serving frameworks add decisions Kubernetes does not make on its own, such as where a request should go based on prompt length or cache locality, how to handle KV cache, and how to scale in response to inference demand.
That distinction matters. A running model server is not necessarily an efficient serving system. At scale, routing requests without regard to cached prefixes can reduce cache locality; long prompts can affect time to first token; and accelerator use can diverge from the demand users actually experience. The llm-d integration documentation describes these as challenges that emerge across a fleet of vLLM replicas.
In this article, “solved” means a project documents an implementation pattern. It does not mean the pattern works identically across frameworks, configurations, hardware or workloads.
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Which Kubernetes serving approach should you choose?
Three documented approaches have different scopes. They are not interchangeable products with an identical feature set: KServe offers a higher-level Kubernetes API, llm-d documents a vLLM-centered cluster layer, and NVIDIA Dynamo is a modular distributed serving framework.
| Option | Abstraction and scope | Documented deployment patterns or engines | What to verify |
|---|---|---|---|
| KServe LLMInferenceService | A Kubernetes custom resource definition (CRD) for generative inference, documented separately from KServe’s traditional InferenceService. | KServe version 0.20 documents single-node, multi-node and prefill/decode-disaggregated patterns. | Confirm the feature and configuration details for version 0.20 and the topology you intend to run. |
| llm-d | A composable, vLLM-centered serving layer for coordinating a fleet and adding capabilities as bottlenecks require them. | Its documentation describes prefix-aware routing, distributed KV indexing and offload, prefill/decode separation, expert-parallel execution, and SLO-aware autoscaling and flow control. | Check which capabilities your deployment actually needs and how they interact in its chosen configuration. |
| NVIDIA Dynamo | A modular distributed serving framework that can be adopted as individual components or as a fuller stack. | NVIDIA’s documentation lists vLLM, SGLang and TensorRT-LLM; Kubernetes, Slurm or local deployment; and NVIDIA and AMD GPUs and Intel XPUs. | These are the project’s stated support scope, not a guarantee that every framework, accelerator and feature combination is supported. Verify the matrix for the intended version; the documentation identifies v1.5.0 as its latest version. |
Choose by the problem you need to address, not by the length of a feature list. If a high-level Kubernetes API and documented topology patterns fit your deployment, evaluate KServe’s LLMInferenceService. If you are building around vLLM and need composable routing, cache or scaling capabilities, evaluate llm-d. If you need a modular framework with choices among documented engines and deployment environments, evaluate Dynamo’s version-specific compatibility matrix.
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How do you serve large language models on Kubernetes?
Start with the simplest topology that meets your workload’s needs. KServe documents single-node and multi-node LLMInferenceService patterns, as well as separate prefill and decode pools. llm-d recommends adding capabilities as bottlenecks call for them rather than assuming every deployment needs its full set. Neither recommendation means that a production configuration can be selected without checking the model, engine, accelerators, network and operational requirements.
- Choose the serving framework and version. Compare the API or framework scope, engine and accelerator compatibility, and documented deployment patterns. Treat compatibility as a version-specific matrix, not a blanket promise.
- Select a topology. Use a single-node pattern where it fits; consider multi-node execution or prefill/decode disaggregation when the workload and available infrastructure justify their additional coordination.
- Decide how requests and cache state should be handled. Compare ordinary round-robin routing with prefix- or KV-aware routing, and GPU-only cache handling with the distributed or tiered KV approaches documented by the selected project.
- Configure scaling around inference demand. KServe documents Workload Variant Autoscaler configurations using signals such as queue depth and KV-cache utilization, with HPA or KEDA actuator paths. In a disaggregated deployment, prefill and decode pools can scale independently.
- Validate under representative conditions. Test the target model, accelerator, topology, traffic and concurrency, including startup, scale-up, scale-down and recovery behavior. A project-reported benchmark is a useful reference point, not a substitute for this validation.
How should you scale LLM inference on Kubernetes?
Scaling LLM inference is not simply a matter of adding replicas when GPU utilization crosses a threshold. Queue depth and KV-cache utilization can provide inference-relevant signals that utilization alone misses. KServe’s documented Workload Variant Autoscaler supports those kinds of signals and can direct scaling through HPA or KEDA. In a disaggregated deployment, it can also support separate scaling of prefill and decode pools.
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Those controls improve the scaling signal; they do not create accelerator capacity or make additional workers ready immediately. Planning still needs to account for model loading, accelerator availability, topology, concurrency, and the different resource demands of prompt processing and token generation. Independent pool scaling is a documented capability, not a universal capacity-planning solution.
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What do the published performance figures actually show?
llm-d’s 2026 project documentation reports the following representative comparisons. Each number belongs to its stated model, hardware and comparison; none is an independently established production guarantee.
| llm-d-reported result | Workload and hardware stated | Comparison and qualification |
|---|---|---|
| 3× higher output throughput and 2× faster time to first token | Llama 3.1 70B on AMD MI300X | Prefix-aware routing versus round-robin routing, as reported by llm-d project documentation in 2026. |
| Up to 70% higher tokens per second | GPT-OSS on NVIDIA B200 | Prefill/decode disaggregation; “up to” is the project-reported result, not a general expected gain. |
| 13.9× throughput | NVIDIA H100 at high concurrency | Hierarchical KV offloading versus GPU-only handling, as reported by llm-d project documentation in 2026. |
These figures help identify patterns worth testing: cache-aware routing, prefill/decode separation and KV offloading. They do not establish how those patterns compare on a different model, accelerator, request mix or concurrency level. The available documentation also does not establish an industry-wide adoption rate, uptime distribution or total-cost comparison. Project-specific benchmarks cannot stand in for those measures.
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What remains operationally hard?
Prefill/decode coordination and shutdown
Disaggregated serving introduces coordination between workers, including transferring KV state. The llm-d operations documentation, accessed in 2026, describes a new NIXL handshake that establishes an RDMA connection and takes roughly five seconds per worker pair. That is the page’s description of its behavior, not a general network benchmark.
The same llm-d page says that prefill-worker shutdown cannot currently wait for every KV block to be retrieved. An in-flight decode may therefore fail to load its cache. Its documented mitigation is to recompute prefill on the decode worker, accepting extra work in exchange for resilience. Because this operations page is on a mutable main branch, check the current documentation and behavior for the version you deploy.
Capacity and startup behavior
Inference-aware autoscaling can respond to better signals, but it cannot remove model-loading delays, guarantee that accelerators are available or make topology constraints disappear. A scaling policy must be evaluated against the time it takes to add usable capacity and against the distinct needs of prompt processing and token generation.
Configuration-specific support
Framework capability lists describe project scope, not identical support across all combinations. Confirm the chosen project’s current version, inference engine, accelerator, topology and feature configuration together. A capability documented for one stack or setup should not be assumed for another.
Quick Recap
What is solved—and what is still open?
- Documented implementation patterns exist for a Kubernetes API aimed at LLM workloads, inference-aware routing, KV-cache management, distributed serving and inference-aware scaling.
- Topology choices are available in documentation, including single-node, multi-node and prefill/decode-separated deployments, but each choice has different operational demands.
- Scale signals can be more relevant than GPU utilization alone, including queue depth and KV-cache utilization, but better signals do not supply capacity or eliminate startup delays.
- Performance claims remain workload-specific. The reported figures are useful hypotheses to test on the target workload and hardware, not universal outcomes.
- Broad maturity is not established by project documentation. The cited materials describe features and project status, including llm-d’s CNCF Sandbox status, but do not independently prove widespread adoption, universal reliability or lower total cost.
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