Clears all KV / prefix cache tiers (GPU, CPU, and FS offload) on every vLLM pod in an llm-d deployment for a clean state, including both roles of a disaggregated prefill/decode setup. Use it to flush the cache, reset vLLM state, or start fresh before a test run. Prefers the /reset_prefix_cache API and falls back to pod restart. Acts on an already deployed stack; pairs with run-llm-d-benchmark for clean-cache benchmark runs.
8 on GitHubby llm-dApache-2.0updated 15 days agodevops
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Plugin clear-kv-cache-tiers-in-llm-d-deployment from marketplace llm-d in llm-d-incubation/llm-d-skills. Commands read from the repo's manifest — 12 days ago.
What it installs
Skills (8)
llm-d-autoconfig
Configure an EPP EndpointPickerConfig for an llm-d deployment based on workload + SLA inputs, then optionally deploy it to the user's cluster. Activate when the user asks about configuring llm-d, setting up the inference scheduler, tuning EPP plugins, choosing what config to use for a model or workl
clear-kv-cache-tiers-in-llm-d-deployment
Clears all KV / prefix cache tiers (GPU, CPU, and FS offload) on every vLLM pod in an llm-d deployment for a clean state — including both roles of a disaggregated prefill/decode setup. Use when the user wants to flush the cache, reset vLLM state, or start fresh before a test run — even if they don't
compare-llm-d-configurations
Compare the benchmark performance of two llm-d stack configurations end-to-end. For each configuration, deploys the stack, runs a benchmark, tears down, then generates a side-by-side results comparison. One configuration may already have been benchmarked previously — in that case, only the new confi
configure-wva-autoscaling-llm-d
Configure and deploy Workload Variant Autoscaler (WVA) for llm-d inference deployments. Guides users through namespace selection, WVA repo location, configuration (with presets or custom values), deployment via Makefile + kubectl apply, and verification. Produces a reusable deployment script.
create-gke-infra-llm-d
Create or prepare GKE infrastructure (cluster, GPU node pools, RDMA/RoCE networking, DRA and DRANET drivers, Gateway API prerequisites) so that an llm-d stack can be deployed on it. Use this skill when users want to provision a new GKE cluster for llm-d, or make an existing GKE cluster llm-d-ready,
deploy-llm-d
Configure and deploy an llm-d stack on an existing Kubernetes or OpenShift cluster by driving the machine-readable guide.yaml of a Well-Lit Path guide (llm-d v1.0 and later). Use this skill when users want to deploy, verify, or customize llm-d from a guide they have already chosen.
run-llm-d-benchmark
Benchmark an already-deployed llm-d stack using the llmdbenchmark CLI. Use this skill when the user wants to run a benchmark workload against a deployed llm-d stack — whether following a named guide (e.g. optimized-baseline, pd-disaggregation) or using a custom workload profile against any inference
teardown-llm-d
Tears down, removes, cleans up, or undeploys a deployed llm-d stack (llm-d v1.0 and later) on Kubernetes or OpenShift. Use this skill whenever the user wants to remove or destroy an llm-d deployment, clean up a namespace after inference workloads, uninstall the llm-d router Helm release, or free up
Compare the benchmark performance of two llm-d stack configurations end-to-end. For each configuration, deploys the stack, runs a benchmark, tears down, then generates a side-by-side results comparison. If one configuration was already benchmarked previously, only the new one is deployed and benchmarked, and its results are compared against the existing ones. Use it to A/B test llm-d configurations, compare against a baseline with no llm-d Inference Scheduler, compare models or serving strategies, evaluate the effect of a configuration change, or compare a new run against a previous one. Drives the deploy, benchmark, and teardown skills directly, so installing it also installs deploy-llm-d, run-llm-d-benchmark, and teardown-llm-d.
Create or prepare GKE infrastructure (cluster, GPU node pools, RDMA/RoCE networking, DRA and DRANET drivers, Gateway API prerequisites) so that an llm-d stack can be deployed on it. Use this skill when users want to provision a new GKE cluster for llm-d, or make an existing GKE cluster llm-d-ready, especially for prefill/decode disaggregation or wide expert parallelism which require RDMA. Stops at a cluster that is ready for llm-d: install deploy-llm-d (config already decided) or llm-d-autoconfig (config to be recommended) to deploy onto it.
Configure and deploy an llm-d stack on an existing Kubernetes or OpenShift cluster by driving the machine-readable guide.yaml of a Well-Lit Path guide (llm-d v1.0 and later). Use this skill when users want to deploy, verify, or customize llm-d from a guide they have already chosen.
Install every llm-d skill at once. Ships no skills of its own, it only pulls in the eight individual plugins, so it can be combined with them without duplicating anything.
Configure an EPP EndpointPickerConfig for an llm-d deployment based on workload + SLA inputs, then optionally deploy it to the user's cluster. Activate when the user asks about configuring llm-d, setting up the inference scheduler, tuning EPP plugins, choosing what config to use for a model or workload, or deploying llm-d. Owns config generation and its own deploy path; install the single-purpose plugins (deploy-llm-d, run-llm-d-benchmark, teardown-llm-d, configure-wva-autoscaling-llm-d) for standalone operations on a config you already have.
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