Running KVBM in TensorRT-LLM — NVIDIA Dynamo Documentation

Source:https://docs.nvidia.com/dynamo/latest/kvbm/trtllm-setup.html
Last updated: 11/7/2025

Title: Running KVBM in TensorRT-LLM — NVIDIA Dynamo Documentation

URL Source: https://docs.nvidia.com/dynamo/latest/kvbm/trtllm-setup.html

Published Time: Fri, 07 Nov 2025 17:51:24 GMT

Markdown Content: Running KVBM in TensorRT-LLM#

This guide explains how to leverage KVBM (KV Block Manager) to manage KV cache and do KV offloading in TensorRT-LLM (trtllm).

To learn what KVBM is, please check here

Note

  • Ensure that etcd and nats are running before starting.

  • KVBM does not currently support CUDA graphs in TensorRT-LLM.

  • KVBM only supports TensorRT-LLM’s PyTorch backend.

  • Disable partial reuse enable_partial_reuse: false in the LLM API config’s kv_connector_config to increase offloading cache hits.

  • KVBM requires TensorRT-LLM v1.1.0rc5 or newer.

  • Enabling KVBM metrics with TensorRT-LLM is still a work in progress.

Quick Start#

To use KVBM in TensorRT-LLM, you can follow the steps below:

Start up etcd for KVBM leader/worker registration and discovery

docker compose -f deploy/docker-compose.yml up -d

Build a dynamo TRTLLM container (KVBM is built in by default)

./container/build.sh --framework trtllm

Launch the container

./container/run.sh --framework trtllm -it --mount-workspace --use-nixl-gds

Configure KVBM cache tiers (choose one of the following options):

Option 1: CPU cache only (GPU -> CPU offloading)

4 means 4GB of pinned CPU memory would be used

export DYN_KVBM_CPU_CACHE_GB=4

Option 2: Both CPU and Disk cache (GPU -> CPU -> Disk tiered offloading)

export DYN_KVBM_CPU_CACHE_GB=4

8 means 8GB of disk would be used

export DYN_KVBM_DISK_CACHE_GB=8

[Experimental] Option 3: Disk cache only (GPU -> Disk direct offloading, bypassing CPU)

NOTE: this option is only experimental and it might not give out the best performance.

NOTE: disk offload filtering is not support when using this option.

export DYN_KVBM_DISK_CACHE_GB=8

Note: You can also use DYN_KVBM_CPU_CACHE_OVERRIDE_NUM_BLOCKS or

DYN_KVBM_DISK_CACHE_OVERRIDE_NUM_BLOCKS to specify exact block counts instead of GB

Allocating memory and disk storage can take some time.

We recommend setting a higher timeout for leader–worker initialization.

1200 means 1200 seconds timeout

export DYN_KVBM_LEADER_WORKER_INIT_TIMEOUT_SECS=1200

Enable disk zerofill fallback for KVBM

Set to true to enable fallback behavior when disk operations fail

export DYN_KVBM_DISK_ZEROFILL_FALLBACK=true

Note

When disk offloading is enabled, to extend SSD lifespan, disk offload filtering would be enabled by default. The current policy is only offloading KV blocks from CPU to disk if the blocks have frequency equal or more than 2. Frequency is determined via doubling on cache hit (init with 1) and decrement by 1 on each time decay step.

To disable disk offload filtering, set DYN_KVBM_DISABLE_DISK_OFFLOAD_FILTER to true or 1.

write an example LLM API config

Note: Disable partial reuse "enable_partial_reuse: false" in the LLM API config’s "kv_connector_config" to increase offloading cache hits.

cat > "/tmp/kvbm_llm_api_config.yaml" <<EOF backend: pytorch cuda_graph_config: null kv_cache_config: enable_partial_reuse: false free_gpu_memory_fraction: 0.80 kv_connector_config: connector_module: dynamo.llm.trtllm_integration.connector connector_scheduler_class: DynamoKVBMConnectorLeader connector_worker_class: DynamoKVBMConnectorWorker EOF

[DYNAMO] start dynamo frontend

python3 -m dynamo.frontend --http-port 8000 &

[DYNAMO] To serve an LLM model with dynamo

python3 -m dynamo.trtllm
--model-path Qwen/Qwen3-0.6B
--served-model-name Qwen/Qwen3-0.6B
--extra-engine-args /tmp/kvbm_llm_api_config.yaml &

Make a call to LLM

curl localhost:8000/v1/chat/completions -H "Content-Type: application/json" -d '{ "model": "Qwen/Qwen3-0.6B", "messages": [ { "role": "user", "content": "In the heart of Eldoria, an ancient land of boundless magic and mysterious creatures, lies the long-forgotten city of Aeloria. Once a beacon of knowledge and power, Aeloria was buried beneath the shifting sands of time, lost to the world for centuries. You are an intrepid explorer, known for your unparalleled curiosity and courage, who has stumbled upon an ancient map hinting at ests that Aeloria holds a secret so profound that it has the potential to reshape the very fabric of reality. Your journey will take you through treacherous deserts, enchanted forests, and across perilous mountain ranges. Your Task: Character Background: Develop a detailed background for your character. Describe their motivations for seeking out Aeloria, their skills and weaknesses, and any personal connections to the ancient city or its legends. Are they driven by a quest for knowledge, a search for lost familt clue is hidden." } ], "stream":false, "max_tokens": 30 }'

Alternatively, can use “trtllm-serve” with KVBM by replacing the above two [DYNAMO] cmds with below:

trtllm-serve Qwen/Qwen3-0.6B --host localhost --port 8000 --backend pytorch --extra_llm_api_options /tmp/kvbm_llm_api_config.yaml

Enable and View KVBM Metrics#

Follow below steps to enable metrics collection and view via Grafana dashboard:

Start the basic services (etcd & natsd), along with Prometheus and Grafana

docker compose -f deploy/docker-compose.yml --profile metrics up -d

Set env var DYN_KVBM_METRICS to true, when launch via dynamo

Optionally set DYN_KVBM_METRICS_PORT to choose the /metrics port (default: 6880).

DYN_KVBM_METRICS=true
python3 -m dynamo.trtllm
--model-path Qwen/Qwen3-0.6B
--served-model-name Qwen/Qwen3-0.6B
--extra-engine-args /tmp/kvbm_llm_api_config.yaml &

Optional if firewall blocks KVBM metrics ports to send prometheus metrics

sudo ufw allow 6880/tcp

View grafana metrics via http://localhost:3001 (default login: dynamo/dynamo) and look for KVBM Dashboard

Benchmark KVBM#

Once the model is loaded ready, follow below steps to use LMBenchmark to benchmark KVBM performance:

git clone https://github.com/LMCache/LMBenchmark.git

Show case of running the synthetic multi-turn chat dataset.

We are passing model, endpoint, output file prefix and qps to the sh script.

cd LMBenchmark/synthetic-multi-round-qa ./long_input_short_output_run.sh
"Qwen/Qwen3-0.6B"
"http://localhost:8000"
"benchmark_kvbm"
1

Average TTFT and other perf numbers would be in the output from above cmd

More details about how to use LMBenchmark could be found here.

NOTE: if metrics are enabled as mentioned in the above section, you can observe KV offloading, and KV onboarding in the grafana dashboard.

To compare, you can remove the kv_connector_config section from the LLM API config and run trtllm-serve with the updated config as the baseline.

cat > "/tmp/llm_api_config.yaml" <<EOF backend: pytorch cuda_graph_config: null kv_cache_config: enable_partial_reuse: false free_gpu_memory_fraction: 0.80 EOF

Run trtllm-serve for the baseline for comparison

trtllm-serve Qwen/Qwen3-0.6B --host localhost --port 8000 --backend pytorch --extra_llm_api_options /tmp/llm_api_config.yaml &

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