# SPDX-FileCopyrightText: Copyright (c) 2025-2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: Apache-2.0
apiVersion: v1
kind: ConfigMap
metadata:
  name: llm-config-kimi-agg-kvbm
data:
  config.yaml: |
    max_batch_size: 128
    max_num_tokens: 8448
    max_seq_len: 8212
    tensor_parallel_size: 8
    moe_expert_parallel_size: 8
    enable_attention_dp: true
    pipeline_parallel_size: 1
    print_iter_log: true
    kv_cache_config:
      free_gpu_memory_fraction: 0.75
      dtype: fp8
    cache_transceiver_config:
      backend: UCX
      max_tokens_in_buffer: 8448
    trust_remote_code: true
    kv_connector_config:
      connector_module: kvbm.trtllm_integration.connector
      connector_scheduler_class: DynamoKVBMConnectorLeader
      connector_worker_class: DynamoKVBMConnectorWorker
---
apiVersion: nvidia.com/v1alpha1
kind: DynamoGraphDeployment
metadata:
  name: kimi-k25-agg-kvbm
spec:
  backendFramework: trtllm
  pvcs:
    - name: model-cache
      create: false
  services:
    Frontend:
      componentType: frontend
      extraPodSpec:
        affinity:
          podAntiAffinity:
            requiredDuringSchedulingIgnoredDuringExecution:
            - labelSelector:
                matchExpressions:
                - key: nvidia.com/dynamo-graph-deployment-name
                  operator: In
                  values:
                  - kimi-k25-agg-kvbm-frontend
              topologyKey: kubernetes.io/hostname
        mainContainer:
          args:
          - python3 -m dynamo.frontend --router-mode kv --http-port 8000
          command:
          - /bin/sh
          - -c
          image: nvcr.io/nvidia/ai-dynamo/tensorrtllm-runtime:my-tag
      replicas: 1
    TrtllmWorker:
      componentType: worker
      envFromSecret: hf-token-secret
      volumeMounts:
        - name: model-cache
          mountPoint: /opt/models
      sharedMemory:
        size: 80Gi
      extraPodSpec:
        affinity:
          nodeAffinity:
            requiredDuringSchedulingIgnoredDuringExecution:
              nodeSelectorTerms:
              - matchExpressions:
                - key: nvidia.com/gpu.present
                  operator: In
                  values:
                  - "true"
        mainContainer:
          ports:
          - name: system
            containerPort: 9090
          - name: nixl
            containerPort: 19090
          - name: kvbm
            containerPort: 6880
          args:
          - |
            python3 -m dynamo.trtllm \
              --model-path "${MODEL_NAME}" \
              --served-model-name "${MODEL_NAME}" \
              --extra-engine-args "${ENGINE_ARGS}" \
              --tensor-parallel-size 8 \
              --dyn-reasoning-parser kimi_k25 \
              --dyn-tool-call-parser kimi_k2
          command:
          - /bin/sh
          - -c
          image: nvcr.io/nvidia/ai-dynamo/tensorrtllm-runtime:my-tag
          env:
          - name: TRTLLM_ENABLE_PDL
            value: "1"
          - name: MODEL_NAME
            value: nvidia/Kimi-K2.5-NVFP4
          - name: ENGINE_ARGS
            value: /opt/dynamo/configs/config.yaml
          - name: HF_HOME
            value: /opt/models
          # Adjust CPU cache size as needed; start small for faster startup
          - name: DYN_KVBM_CPU_CACHE_GB
            value: "10"
          # Enable KVBM metrics
          - name: DYN_KVBM_METRICS
            value: "true"
          - name: DYN_KVBM_METRICS_PORT
            value: "6880"
          volumeMounts:
          - mountPath: /opt/dynamo/configs
            name: llm-config-kimi-agg-kvbm
            readOnly: true
          workingDir: /workspace/examples/backends/trtllm
        volumes:
        - configMap:
            name: llm-config-kimi-agg-kvbm
          name: llm-config-kimi-agg-kvbm
      replicas: 1
      resources:
        limits:
          gpu: "8"
        requests:
          gpu: "8"
