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.. _offload_kv_cache:
Example: Offload KV cache to CPU
================================
.. warning::
This page documents the behavior of LMCache's in-process mode (deprecated). Please consider using :doc:`LMCache MP mode </mp/index>` for better feature support and performance.
In this example, we will show you how to offload KV cache to CPU memory.
.. note::
Besides CPU memory, LMCache also supports offloading KV cache to many different destinations.
See :ref:`getting_started/quickstart/offload_kv_cache:Supported offloading destinations` for more details.
Prerequisites
-------------
Before you begin, make sure you have:
- vLLM v1 with LMCache installed (see :doc:`Installation <../installation>`)
- A GPU that can run a LLM
Use CPU offloading in offline inference
---------------------------------------
This section demonstrates how to use CPU memory offloading in offline inference scenarios using LMCache with vLLM.
The example script we use here is available in `vLLM examples <https://github.com/vllm-project/vllm/blob/main/examples/others/lmcache/cpu_offload_lmcache.py>`_.
See the `examples README <https://github.com/vllm-project/vllm/tree/main/examples/others/lmcache#2-cpu-offload-examples>`_ to understand how to run the script for vLLM v1.
First, set up the necessary environment variables for LMCache:
.. code-block:: python
import os
# Set token chunk size to 256
os.environ["LMCACHE_CHUNK_SIZE"] = "256"
# Enable CPU memory backend
os.environ["LMCACHE_LOCAL_CPU"] = "True"
# Set CPU memory limit to 5GB
os.environ["LMCACHE_MAX_LOCAL_CPU_SIZE"] = "5.0"
Next, configure vLLM with LMCache integration:
.. code-block:: python
from vllm import LLM, SamplingParams
from vllm.config import KVTransferConfig
# Configure KV cache transfer to use LMCache
ktc = KVTransferConfig(
kv_connector="LMCacheConnectorV1",
kv_role="kv_both",
)
# Initialize LLM with LMCache configuration
# Adjust gpu_memory_utilization based on your GPU memory
llm = LLM(model="Qwen/Qwen3-8B",
kv_transfer_config=ktc,
max_model_len=8000,
gpu_memory_utilization=0.8)
Now you can run inference with automatic KV cache offloading:
.. code-block:: python
# Create example prompts with shared prefix
shared_prompt = "Hello, how are you?" * 1000
prompts = [
shared_prompt + "Hello, my name is",
]
# Define sampling parameters
sampling_params = SamplingParams(temperature=0, top_p=0.95, max_tokens=10)
# Run inference
outputs = llm.generate(prompts, sampling_params)
for output in outputs:
generated_text = output.outputs[0].text
print(f"Generated text: {generated_text!r}")
When the inference is complete, clean up the LMCache backend:
.. code-block:: python
from lmcache.v1.cache_engine import LMCacheEngineBuilder
from lmcache.integration.vllm.utils import ENGINE_NAME
LMCacheEngineBuilder.destroy(ENGINE_NAME)
During inference, LMCache will automatically handle storing and managing KV cache in CPU memory. You can monitor this through the logs, which will show messages like::
LMCache INFO: Storing KV cache for 6006 out of 6006 tokens for request 0
This indicates that the KV cache has been successfully offloaded to CPU memory.
.. note::
- Adjust ``gpu_memory_utilization`` based on your GPU's available memory
- The CPU offloading buffer size can be adjusted through ``LMCACHE_MAX_LOCAL_CPU_SIZE``
Use CPU offloading in online inference
--------------------------------------
This section demonstrates how to use CPU memory offloading in online serving scenarios.
First, create a configuration file named ``lmcache_config.yaml`` with the following content:
.. code-block:: yaml
chunk_size: 256
local_cpu: true
max_local_cpu_size: 5
.. note::
LMCache supports extensive configuration through a ``lmcache_config.yaml`` file where you can customize chunk sizes, memory limits, storage backends, and more. We'll cover advanced configuration options in later examples. For now, let's run a minimal example with default configuration.
Launch the vLLM server with LMCache integration using environment variables. Here's an example command:
.. code-block:: bash
LMCACHE_CONFIG_FILE=lmcache_config.yaml \
vllm serve \
Qwen/Qwen3-8B \
--kv-transfer-config \
'{"kv_connector":"LMCacheConnectorV1",
"kv_role":"kv_both"
}'
Key parameters explained:
- ``LMCACHE_CONFIG_FILE``: Path to the LMCache configuration file.
- ``--kv-transfer-config``: Configures LMCache integration
- ``kv_connector``: Specifies the LMCache connector
- ``kv_role``: Set to "kv_both" for both storing and loading KV cache
Once the server is running, you can send requests to it using curl. Here's an example of how to send a request to the vLLM server with LMCache integration:
.. code-block:: bash
curl http://localhost:8000/v1/completions \
-H "Content-Type: application/json" \
-d '{
"model": "Qwen/Qwen3-8B",
"prompt": "<|im_start|>system\nYou are a helpful AI assistant.<|im_end|>\n<|im_start|>user\nWhat is the capital of France?<|im_end|>\n<|im_start|>assistant\n",
"max_tokens": 100,
"temperature": 0.7
}'
You should see the following logs:
.. code-block:: text
:emphasize-lines: 1
LMCache INFO: Storing KV cache for 31 out of 31 tokens for request cmpl-274bcaa80837444dbf9fbba4155d2620-0 (vllm_v1_adapter.py:497:lmcache.integration.vllm.vllm_v1_adapter)
Once you send the same curl request again, you should see the following logs:
.. code-block:: text
:emphasize-lines: 1
LMCache INFO: Reqid: cmpl-4ddf8863a6ac4dc3b6a952f2a107e9b2-0, Total tokens 31, LMCache hit tokens: 30, need to load: 14 (vllm_v1_adapter.py:543:lmcache.integration.vllm.vllm_v1_adapter)
Example: CPU offloading benefits
--------------------------------
This section demonstrates the performance benefits of using CPU offloading with LMCache. We'll use a script that generates multiple prompts and compare the performance with and without LMCache.
Prerequisites (Setup)
~~~~~~~~~~~~~~~~~~~~~~
- A CUDA GPU. The example picks a model that fits the GPU automatically:
- ``Qwen/Qwen3-8B`` (bf16) when the GPU has ~36 GiB or more (e.g. A100-80G, H100).
- ``Qwen/Qwen3-8B-FP8`` with ``kv_cache_dtype="fp8"`` when the GPU has ~24 GiB
and supports native FP8 (Ada Lovelace / Hopper, ``sm_89+``; e.g. L4, L40, RTX 4090).
- ``Qwen/Qwen3-1.7B`` as the fallback for smaller GPUs (~10 GiB and up),
including Ampere 24 GiB cards (RTX A5000, RTX 3090) where FP8 is unsupported.
- Sufficient CPU memory. The example clamps the LMCache pinned host buffer to
fit your system RAM and ``RLIMIT_MEMLOCK`` (``ulimit -l``), so it also works
on smaller hosts without manual tuning.
Example script
~~~~~~~~~~~~~~
Save the following script as ``cpu-offloading.py``:
.. code-block:: python
# SPDX-License-Identifier: Apache-2.0
"""
This file demonstrates the example usage of cpu offloading
with LMCache in vLLM v1.
Note that lmcache needs to be installed to run this example.
Learn more about LMCache in https://github.com/LMCache/LMCache.
"""
import os
import torch
import argparse
import time
from lmcache.v1.cache_engine import LMCacheEngineBuilder
from lmcache.integration.vllm.utils import ENGINE_NAME
from vllm import LLM, SamplingParams
from vllm.config import KVTransferConfig
def parse_arguments() -> argparse.Namespace:
"""Parse command line arguments."""
parser = argparse.ArgumentParser(description="CPU offloading example with LMCache")
parser.add_argument("--num-prompts", type=int, default=10,
help="Number of prompts to generate (default: 10)")
parser.add_argument("--num-tokens", type=int, default=10000,
help="Number of tokens per prompt (default: 10000)")
parser.add_argument("--enable-lmcache", action="store_true",
help="Enable LMCache for CPU offloading (default: True)")
return parser.parse_args()
def pick_cpu_size_gb(workload_gb: float) -> float:
"""
Clamp the LMCache pinned host buffer to fit system RAM and RLIMIT_MEMLOCK.
cudaHostAlloc pins pages, so the buffer cannot exceed total RAM nor the
per-process memlock limit (`ulimit -l`). On hosts where either is small,
the original "1.5 GB per 10k tokens" formula fails with cudaErrorMemoryAllocation.
Args:
workload_gb: Desired buffer size for the workload, in GiB.
Returns:
float: A buffer size in GiB that fits both caps, never below 1.0.
"""
import psutil
ram_gib = psutil.virtual_memory().total / (1024 ** 3)
try:
import resource
memlock_soft, _ = resource.getrlimit(resource.RLIMIT_MEMLOCK)
memlock_gib = (
float("inf")
if memlock_soft == resource.RLIM_INFINITY
else memlock_soft / (1024 ** 3)
)
except ImportError:
# `resource` is POSIX-only; on Windows treat memlock as unbounded.
memlock_gib = float("inf")
return max(min(workload_gb, ram_gib * 0.5, memlock_gib * 0.9), 1.0)
def setup_lmcache_environment(num_prompts: int, num_tokens: int) -> None:
"""
Configure LMCache environment variables.
Args:
num_prompts: Number of prompts to process
num_tokens: Number of tokens per prompt
"""
workload_gb = num_prompts * num_tokens * 1.5 / 10000 # 1.5 GB per 10k tokens
cpu_size = pick_cpu_size_gb(workload_gb)
env_vars = {
"LMCACHE_CHUNK_SIZE": "256", # Set tokens per chunk
"LMCACHE_LOCAL_CPU": "True", # Enable local CPU backend
"LMCACHE_MAX_LOCAL_CPU_SIZE": str(cpu_size) # CPU memory limit (GB)
}
for key, value in env_vars.items():
os.environ[key] = value
def pick_model_and_kwargs() -> tuple[str, dict]:
"""
Pick a Qwen model that fits the current GPU's memory and compute capability.
Tiers:
- >= 36 GiB -> Qwen/Qwen3-8B (bf16)
- >= 20 GiB and sm >= 89 -> Qwen/Qwen3-8B-FP8 (native FP8)
- >= 10 GiB -> Qwen/Qwen3-1.7B
- otherwise -> RuntimeError
Returns:
tuple[str, dict]: (model id, extra kwargs to pass to ``LLM``).
Raises:
RuntimeError: If no CUDA GPU is visible or it is too small.
"""
if not torch.cuda.is_available():
raise RuntimeError("No GPU available")
total_gib = torch.cuda.get_device_properties(0).total_memory / (1024 ** 3)
major, minor = torch.cuda.get_device_capability(0)
sm = major * 10 + minor
has_fp8 = sm >= 89 # Ada Lovelace / Hopper
if total_gib >= 36:
return "Qwen/Qwen3-8B", {}
if total_gib >= 20 and has_fp8:
print(f"[fallback] GPU {total_gib:.1f} GiB sm_{sm}: using Qwen3-8B-FP8")
return "Qwen/Qwen3-8B-FP8", {"kv_cache_dtype": "fp8"}
if total_gib >= 10:
print(f"[fallback] GPU {total_gib:.1f} GiB sm_{sm}: using Qwen3-1.7B")
return "Qwen/Qwen3-1.7B", {}
raise RuntimeError(
f"GPU has {total_gib:.1f} GiB; need at least 10 GiB for Qwen3-1.7B"
)
def create_test_prompts(num_prompts: int = 10, num_tokens: int = 1000) -> list[str]:
"""
Create test prompts with index prefix and dummy body.
Args:
num_prompts: Number of prompts to generate
num_tokens: Approximate number of tokens per prompt (using 'Hi ' as token unit)
Returns:
list: List of prompts with format '[index] Hi Hi Hi...'
"""
prompts = []
dummy_text = "Hi " * num_tokens
for i in range(num_prompts):
prompt = f"[Prompt {i}] {dummy_text} how are you?"
prompts.append(prompt)
return prompts
def initialize_llm(max_len: int = 16384, enable_lmcache: bool = True) -> LLM:
"""
Initialize the LLM with a model auto-selected for the current GPU.
Args:
max_len: Maximum sequence length
enable_lmcache: Whether to wire up the LMCache KV connector
Returns:
LLM: Configured LLM instance
"""
model_name, extra_kwargs = pick_model_and_kwargs()
ktc = KVTransferConfig(
kv_connector="LMCacheConnectorV1",
kv_role="kv_both",
) if enable_lmcache else None
return LLM(
model=model_name,
kv_transfer_config=ktc,
max_model_len=max_len,
enable_prefix_caching=False,
gpu_memory_utilization=0.9,
**extra_kwargs,
)
def generate_and_print_output(
llm: LLM,
prompts: list[str],
sampling_params: SamplingParams,
) -> float:
"""
Generate text and print the results.
Args:
llm: LLM instance
prompts: List of input prompts
sampling_params: Configured sampling parameters
Returns:
float: Time taken for generation in seconds
"""
start_time = time.time()
outputs = llm.generate(prompts, sampling_params)
end_time = time.time()
for output in outputs:
generated_text = output.outputs[0].text
print(f"Generated text: {generated_text!r}")
return end_time - start_time
def main() -> None:
"""Main execution function."""
# Parse command line arguments
args = parse_arguments()
# Setup environment if LMCache is enabled
if args.enable_lmcache:
setup_lmcache_environment(args.num_prompts, args.num_tokens)
# Create prompts and sampling parameters
prompts = create_test_prompts(num_prompts=args.num_prompts, num_tokens=args.num_tokens)
sampling_params = SamplingParams(temperature=0, top_p=0.95, max_tokens=1)
# Initialize model
llm = initialize_llm(enable_lmcache=args.enable_lmcache)
# First run
print("\nFirst run:")
first_run_time = generate_and_print_output(llm, prompts, sampling_params)
print(f"First run time: {first_run_time:.2f} seconds")
# Second run
print("\nSecond run:")
second_run_time = generate_and_print_output(llm, prompts, sampling_params)
print(f"Second run time: {second_run_time:.2f} seconds")
# Print speedup
if first_run_time > 0:
speedup = first_run_time / second_run_time
print(f"\nSpeedup (first run / second run): {speedup:.2f}x")
# Cleanup if LMCache was enabled
if args.enable_lmcache:
LMCacheEngineBuilder.destroy(ENGINE_NAME)
if __name__ == "__main__":
main()
Running the Example
~~~~~~~~~~~~~~~~~~~
1. First, run the script without LMCache:
.. code-block:: bash
python cpu-offloading.py
You'll see output like:
.. code-block:: text
Speedup (first run / second run): 1.00x
Without LMCache, there's no speedup between runs even if vLLM has prefix caching enabled.
This is because the KV cache exceeds GPU memory and can't be reused.
2. Now, run with LMCache enabled:
.. code-block:: bash
python cpu-offloading.py --enable-lmcache
You'll see output like:
.. code-block:: text
Speedup (first run / second run): 7.43x
The significant speedup in the second case demonstrates how LMCache effectively manages KV cache offloading to CPU memory.
When the total size of KV cache exceeds GPU memory, LMCache allows you to store and reuse the cache from CPU memory,
resulting in much faster subsequent generations for prompts with shared prefixes.
Supported offloading destinations
---------------------------------
LMCache now supports offloading KV cache to the following destinations:
- :doc:`CPU memory <../../kv_cache/storage_backends/cpu_ram>`
- :doc:`Local file system <../../kv_cache/storage_backends/local_storage>`
- :doc:`Mooncake Storage <../../kv_cache/storage_backends/mooncake>`
- :doc:`InfiniStore <../../kv_cache/storage_backends/infinistore>`
- :doc:`Redis <../../kv_cache/storage_backends/redis>`
- :doc:`ValKey <../../kv_cache/storage_backends/valkey>`
Troubleshooting
---------------
If you encounter the following error:
.. code-block:: text
(EngineCore_DP0 pid=55437) ERROR 10-04 14:44:47 [core.py:708] RuntimeError:
Cannot re-initialize CUDA in forked subprocess. To use CUDA with multiprocessing, you must use the 'spawn' start method
You can resolve this issue using one of the following methods:
- Set ``VLLM_WORKER_MULTIPROC_METHOD=spawn`` in the environment variables.
- Or update the Python code to guard usage of vllm behind a if ``__name__ == '__main__':`` block.
.. code-block:: python
if __name__ == '__main__':
from vllm import LLM, SamplingParams
from vllm.config import KVTransferConfig
from lmcache.v1.cache_engine import LMCacheEngineBuilder
from lmcache.integration.vllm.utils import ENGINE_NAME
main()
For details, please refer to the `vLLM Troubleshooting Guide: Python multiprocessing <https://docs.vllm.ai/en/latest/usage/troubleshooting.html#python-multiprocessing>`_.