# llmux
[](https://crates.io/crates/llmux)
[](https://github.com/doublewordai/llmux)
LLM multiplexer for vLLM. Host multiple models on a single GPU, switching
between them on demand using vLLM's sleep/wake API.
When a request arrives for a model that isn't currently loaded, llmux puts the
active model to sleep (freeing GPU memory) and wakes the requested model. The
OpenAI-compatible API stays up throughout - clients just change the `model`
field.
## How it works
```
Client requests
|
+---------+
| llmux | port 3000 (OpenAI-compatible)
+---------+
/ \
[vLLM:8001] [vLLM:8002]
(active) (sleeping)
```
llmux spawns vLLM processes lazily on first request and manages their
lifecycle. Only one model is active at a time - the rest are evicted using
a configurable two-axis policy.
### Eviction policy
When a model is evicted, llmux applies two strategies in sequence:
1. **Weight strategy** — what vLLM does with model weights (via the sleep API)
2. **Process strategy** — what happens to the OS process afterward
#### Weight strategy
Applied first, via vLLM's sleep endpoint. Controls what happens to weights and KV cache:
| `retain` | Nothing happens. Weights and KV cache stay on GPU. |
| `offload` | Weights copied to pinned CPU RAM. KV cache and CUDA graphs discarded. Frees most GPU memory but uses significant host RAM. |
| `discard` | Weights dropped entirely (reloaded from disk on wake). KV cache and CUDA graphs discarded. Frees most GPU memory with no CPU RAM cost. |
Both `offload` and `discard` leave a small CUDA context (~500 MiB) on the GPU.
#### Process strategy
Applied second, to the process left behind by the weight strategy:
| `keep_running` | Process stays as-is. Fast, but whatever's still on GPU stays there. |
| `cuda_suspend` | Snapshots remaining VRAM to host RAM via `cuda-checkpoint`, freeing 100% of GPU memory. Process stays alive. |
| `checkpoint` | CRIU dumps the entire process (including host RAM) to disk, then kills it. Frees 100% of GPU and CPU memory. |
| `stop` | Kills the process. Everything is lost. |
#### How they interact
The weight strategy determines what's on GPU vs. CPU before the process strategy runs.
This affects speed, memory usage, and what survives a wake cycle:
| **`retain`** | No-op (model stays loaded) | Full VRAM snapshot to CPU — weights, KV cache, CUDA graphs all preserved | Full process checkpoint to disk — large image (includes VRAM) | Everything lost |
| **`offload`** | Weights on CPU, KV lost, ~500 MiB CUDA context remains on GPU | Remaining CUDA context → CPU, weights already on CPU | Large CRIU image (weights in host RAM get written to disk) | Everything lost |
| **`discard`** | Weights gone, KV lost, ~500 MiB CUDA context remains on GPU | Remaining CUDA context → CPU, weights gone | Small CRIU image (no weights — reloaded from HF cache on wake) | Everything lost |
Common choices:
- **`offload` + `keep_running`** — Fast wake (weights already in RAM), but holds CPU memory and ~500 MiB GPU
- **`discard` + `keep_running`** — No CPU RAM cost, but slow wake (reload from disk) and ~500 MiB GPU
- **`retain` + `cuda_suspend`** — Frees 100% GPU, full state preserved, but holds all VRAM in CPU RAM
- **`discard` + `checkpoint`** — Frees 100% GPU *and* CPU, small CRIU image; wake reloads weights from disk but restores KV cache, CUDA graphs, and warmed allocator from checkpoint
- **`offload` + `checkpoint`** — Like above but CRIU image is large (includes weights); wake is faster (no disk reload) but checkpoint is slower and uses more disk
If eviction fails, llmux automatically escalates to `stop` to guarantee GPU
memory is freed.
#### CUDA suspend
Uses `cuda-checkpoint --toggle` to suspend CUDA state and copy VRAM to host
RAM. The process stays alive — no serialization, no CRIU. Wake is just another
toggle to copy state back to GPU.
Like `offload`, this holds state in CPU RAM. Unlike `offload`, it frees 100% of
GPU memory (`offload` keeps ~500 MiB for CUDA context) and preserves full state.
For TP>1, llmux coordinates NCCL teardown before checkpoint and rebuild after
restore. This requires patched vLLM with `suspend_nccl`/`resume_nccl` support
(included in the Docker image).
**Requirements:**
- `cuda-checkpoint` utility (included in Docker image)
- Root access (or passwordless `sudo` for `cuda-checkpoint`)
- For TP>1: `--enforce-eager` and `--disable-custom-all-reduce` in `extra_args`
#### CRIU checkpoint
CRIU checkpointing uses `cuda-checkpoint` and `criu` to snapshot the entire
vLLM process tree to disk, then kill it. On restore, CRIU brings the process
back with all state intact — including GPU VRAM contents, KV cache, CUDA
graphs, and the warmed memory allocator. First inference after restore is ~30ms
(no warmup needed).
**Requirements:**
- CRIU 4.x with the CUDA plugin (`libcuda_plugin.so`) (included in Docker image)
- `cuda-checkpoint` utility (included in Docker image)
- Root access (or passwordless `sudo` for `criu` and `cuda-checkpoint`)
- vLLM process must not use `io_uring` or `libuv` (set automatically)
- For TP>1: `--enforce-eager` and `--disable-custom-all-reduce` in `extra_args`
**Trade-offs vs offload:**
- Slower sleep/wake than `offload`, but no CPU RAM cost and full state preservation
- Slower wake than `offload` but faster first-inference (no warmup)
## Quickstart
Create a `config.json`:
```json
{
"models": {
"qwen-14b": {
"model_path": "Qwen/Qwen3-14B",
"port": 8001,
"eviction": { "weights": "offload", "process": "keep_running" }
},
"gemma-12b": {
"model_path": "google/gemma-3-12b-it",
"port": 8002,
"eviction": { "weights": "discard", "process": "keep_running" }
}
},
"port": 3000
}
```
### With Docker (recommended)
The Docker image bundles vLLM v0.15.1 with patches for NCCL suspend/resume
and sleep mode fixes:
```bash
docker run --gpus all --init \
-v ~/.cache/huggingface:/root/.cache/huggingface \
-v ./config.json:/etc/llmux/config.json:ro \
-p 3000:3000 \
ghcr.io/doublewordai/llmux:latest
```
For `cuda_suspend` or `checkpoint` process strategies, additional flags are required:
```bash
docker run --gpus all \
--privileged \
--pid=host \
--ipc=host \
-v ~/.cache/huggingface:/root/.cache/huggingface \
-v ./config.json:/etc/llmux/config.json:ro \
-v /tmp/llmux-checkpoints:/tmp/llmux-checkpoints \
-p 3000:3000 \
ghcr.io/doublewordai/llmux:latest
```
The extra flags are needed because:
- `--privileged` — CRIU requires broad namespace and ptrace access
- `--pid=host` — cuda-checkpoint needs to ptrace vLLM worker PIDs
- `--ipc=host` — NCCL uses shared memory for inter-GPU communication
- `-v /tmp/llmux-checkpoints:...` — CRIU checkpoints can be tens of GB; mount a host volume to avoid filling the container filesystem
**Important:** Do NOT use `--init` with CRIU (`checkpoint` process strategy). Docker's init process (tini)
redirects stdin to the host's `/dev/null`, whose mount ID is invisible inside the container.
CRIU dump fails with "Can't lookup mount=N for fd=0 path=/dev/null".
### From source
Requires vLLM installed and available as `vllm` on PATH:
```bash
cargo install llmux
llmux --config config.json
```
For `cuda_suspend` or `checkpoint` process strategies, you also need `cuda-checkpoint`
and `criu` (with CUDA plugin) installed and either run as root or configure passwordless sudo.
### Send requests
```bash
# First request starts vLLM for qwen-14b
curl http://localhost:3000/v1/chat/completions \
-H "Content-Type: application/json" \
-d '{"model": "qwen-14b", "messages": [{"role": "user", "content": "Hello"}]}'
# Switching: sleeps qwen-14b, starts gemma-12b
curl http://localhost:3000/v1/chat/completions \
-H "Content-Type: application/json" \
-d '{"model": "gemma-12b", "messages": [{"role": "user", "content": "Hello"}]}'
```
## Configuration
### Model options
| `model_path` | *required* | HuggingFace model ID or local path |
| `port` | *required* | Port for this model's vLLM instance |
| `eviction` | `retain` + `stop` | Eviction policy (see below) |
| `extra_args` | `[]` | Additional vLLM CLI arguments |
| `checkpoint_path` | *none* | Path to CRIU checkpoint images for lazy restore on first request |
The `eviction` field takes an object with `weights` and `process` keys:
```json
{
"eviction": { "weights": "offload", "process": "keep_running" }
}
```
All vLLM-specific flags (e.g. `--gpu-memory-utilization`, `--tensor-parallel-size`,
`--dtype`) should be passed via `extra_args`:
```json
{
"model_path": "Qwen/Qwen3-14B",
"port": 8001,
"extra_args": ["--gpu-memory-utilization", "0.9", "--tensor-parallel-size", "2"]
}
```
#### Tensor parallelism with cuda_suspend/checkpoint
When using `cuda_suspend` or `checkpoint` with TP>1, you **must** include `--enforce-eager`
and `--disable-custom-all-reduce` in `extra_args`:
```json
{
"model_path": "NousResearch/Meta-Llama-3.1-8B-Instruct",
"port": 8001,
"eviction": { "weights": "retain", "process": "cuda_suspend" },
"extra_args": [
"--tensor-parallel-size", "2",
"--enforce-eager",
"--disable-custom-all-reduce",
"--gpu-memory-utilization", "0.85"
]
}
```
- `--enforce-eager` — CUDA graphs hold stale NCCL handles and crash on resume
- `--disable-custom-all-reduce` — CustomAllReduce IPC buffers cannot survive cuda-checkpoint
llmux validates the config at startup and warns if these flags are missing.
### Top-level options
| `port` | `3000` | Proxy listen port |
| `metrics_port` | `9090` | Prometheus metrics port (0 to disable) |
| `vllm_command` | `"vllm"` | vLLM binary path |
### Checkpoint config
To use `cuda_suspend` or `checkpoint` process strategies, add a `checkpoint` section:
```json
{
"models": {
"qwen-14b": {
"model_path": "Qwen/Qwen3-14B",
"port": 8001,
"eviction": { "weights": "retain", "process": "cuda_suspend" }
}
},
"checkpoint": {
"cuda_checkpoint_path": "cuda-checkpoint"
}
}
```
For CRIU checkpointing, the full config is:
```json
{
"checkpoint": {
"criu_path": "criu",
"cuda_plugin_dir": "/usr/lib/criu/",
"images_dir": "/tmp/llmux-checkpoints",
"cuda_checkpoint_path": "cuda-checkpoint"
}
}
```
| `criu_path` | `"criu"` | Path to the criu binary |
| `cuda_plugin_dir` | `"/usr/lib/criu/"` | Directory containing `libcuda_plugin.so` |
| `images_dir` | `"/tmp/llmux-checkpoints"` | Base directory for checkpoint images |
| `cuda_checkpoint_path` | `"cuda-checkpoint"` | Path to the cuda-checkpoint utility |
### vLLM logging
vLLM process output (stdout/stderr) is always captured and forwarded to the
`vllm` tracing target at `debug` level. Use `RUST_LOG` to control visibility:
```bash
# Default: only llmux info logs, vLLM output hidden
llmux --config config.json
# Show vLLM output
RUST_LOG=info,vllm=debug llmux --config config.json
# --verbose includes vLLM output automatically
llmux --config config.json --verbose
```
ANSI color codes are stripped from vLLM output. The `NO_COLOR=1` environment
variable is also set on spawned vLLM processes.
### Policy options
| `policy_type` | `"fifo"` | Switching policy |
| `request_timeout_secs` | `60` | Request timeout |
| `drain_before_switch` | `true` | Wait for in-flight requests before sleeping |
| `eviction` | `retain` + `stop` | Default eviction policy |
## Validation
llmux includes a built-in validation tool that tests sleep/wake cycles
against a running model, verifying GPU memory is freed and responses are
deterministic after wake:
```bash
llmux --config config.json --validate qwen-14b \
--policies offload+keep_running,discard+keep_running,retain+cuda_suspend \
--verbose
```
Output:
```
Eviction Sleep (s) Wake (s) GPU Before GPU After GPU Wake Response Pass
------------------------------------------------------------------------------------------------
Offload+KeepRun 35.9 1.2 45899 MiB 1341 MiB 44033 MiB match OK
Discard+KeepRun 0.3 8.2 44033 MiB 1341 MiB 44033 MiB match OK
Result: ALL PASSED
```
## Checkpoint management
Pre-create CRIU checkpoints for fast model switching:
```bash
# Create checkpoint (start model, warm up, CRIU dump to disk)
llmux --config config.json --checkpoint qwen-14b
# Use a different weight strategy (affects CRIU image size)
llmux --config config.json --checkpoint qwen-14b --eviction retain+checkpoint
# Skip warmup inference before checkpointing
llmux --config config.json --checkpoint qwen-14b --no-warmup
# Restore detached (CRIU restore, health check, exit — process keeps running)
llmux --config config.json --restore-detached qwen-14b
```
The default eviction for `--checkpoint` is `discard+checkpoint`, which produces
small CRIU images (weights are reloaded from the HF cache on restore). Use
`retain+checkpoint` or `offload+checkpoint` for larger images that restore
faster (weights already in the snapshot).
After `--restore-detached`, the vLLM process continues running on its configured
port. This is useful for testing checkpoints or running a single model without
the daemon.
### Lazy restore via config
Instead of restoring manually, set `checkpoint_path` in your model config so
the daemon restores from checkpoint on first request:
```json
{
"models": {
"qwen-14b": {
"model_path": "Qwen/Qwen3-14B",
"port": 8001,
"eviction": { "weights": "discard", "process": "checkpoint" },
"checkpoint_path": "/tmp/llmux-checkpoints/qwen-14b/images"
}
}
}
```
When the daemon starts, models with `checkpoint_path` are initialized in
checkpointed state. The first request triggers a CRIU restore instead of a
cold start — typically 3-5x faster.
`keep_images` must be `true` (the default) in the `checkpoint` config when
using `checkpoint_path`, since the images must persist across daemon restarts.
## Docker Compose
### Basic setup
```yaml
services:
llmux:
image: ghcr.io/doublewordai/llmux:latest
init: true
command: ["--config", "/etc/llmux/config.json"]
volumes:
- ~/.cache/huggingface:/root/.cache/huggingface
- ./config.json:/etc/llmux/config.json:ro
ports:
- "3000:3000"
deploy:
resources:
reservations:
devices:
- capabilities: [gpu]
```
### With cuda-checkpoint/CRIU
```yaml
services:
llmux:
image: ghcr.io/doublewordai/llmux:latest
init: true
command: ["--config", "/etc/llmux/config.json"]
pid: host
ipc: host
cap_add:
- SYS_PTRACE
- CHECKPOINT_RESTORE
volumes:
- ~/.cache/huggingface:/root/.cache/huggingface
- ./config.json:/etc/llmux/config.json:ro
ports:
- "3000:3000"
deploy:
resources:
reservations:
devices:
- capabilities: [gpu]
```
### With onwards (API key auth)
For production, put [onwards](https://github.com/doublewordai/onwards) in
front for API key authentication:
```yaml
services:
llmux:
image: ghcr.io/doublewordai/llmux:latest
init: true
command: ["--config", "/etc/llmux/config.json"]
pid: host
ipc: host
cap_add:
- SYS_PTRACE
- CHECKPOINT_RESTORE
volumes:
- ~/.cache/huggingface:/root/.cache/huggingface
- ./config.json:/etc/llmux/config.json:ro
deploy:
resources:
reservations:
devices:
- capabilities: [gpu]
onwards:
image: ghcr.io/doublewordai/onwards:latest
command: ["--targets", "/etc/onwards/targets.json"]
volumes:
- ./targets.json:/etc/onwards/targets.json:ro
ports:
- "3000:3000"
```
Where `targets.json` maps model names to llmux with API keys:
```json
{
"targets": {
"qwen-14b": {
"url": "http://llmux:3000/v1",
"keys": ["sk-your-api-key"],
"onwards_model": "qwen-14b"
},
"gemma-12b": {
"url": "http://llmux:3000/v1",
"keys": ["sk-your-api-key"],
"onwards_model": "gemma-12b"
}
}
}
```
## Tensor parallelism
All eviction strategies work with TP>1:
| `offload` | Yes | vLLM manages NCCL teardown/rebuild internally |
| `discard` | Yes | Same — vLLM handles it |
| `cuda_suspend` | Yes | llmux tears down NCCL before cuda-checkpoint, rebuilds after restore |
| `checkpoint` | Yes | Same — NCCL teardown before checkpoint, rebuild after restore |
| `stop` | Yes | Kill + cold restart always works |
For `cuda_suspend` and `checkpoint`, llmux uses vLLM's `/collective_rpc` endpoint to call
`suspend_nccl` (before cuda-checkpoint) and `resume_nccl` (after restore)
on all TP workers. This tears down NCCL IPC handles that cuda-checkpoint
cannot checkpoint, then rebuilds them after CUDA state is restored.
This requires patched vLLM with `suspend_nccl`/`resume_nccl` support. The
Docker image includes these patches. For bare-metal installs, apply
`patches/nccl-suspend-resume-v0.15.1.patch` to your vLLM installation.
## Known issues
The `--validate` flag exists specifically to catch these kinds of problems
before they hit production.
### vLLM v0.14+ sleep regression
Sleep mode (`offload`/`discard`) has a regression where weights are not discarded from GPU
memory ([vllm#32714](https://github.com/vllm-project/vllm/issues/32714)).
The Docker image includes a patch (`fix-sleep-mode-v0.15.1.patch`) that fixes
this. For bare-metal installs, apply the patch or use `cuda_suspend`/`stop` instead.
### vLLM v0.13.0
- **`openai/gpt-oss-20b` `discard` reload fails.** The MXFP4 weight loader crashes on
wake with `default_weight_loader() got an unexpected keyword argument
'weight_name'`. `offload` works fine (19.6s sleep, 0.6s wake). Use `offload` for this
model.
- `offload` and `discard` both work correctly for `Qwen/Qwen3-14B` and
`google/gemma-3-12b-it`.
### NVIDIA driver requirements
The Docker image uses vLLM v0.15.1 which requires CUDA 12.9 and
nvidia-driver-580 or later. Check your driver version with `nvidia-smi`.
## Compatibility
- **`offload`/`discard`:** Works with vLLM v0.13.x out of the box. Works with v0.15.1 with the sleep fix patch (included in Docker image).
- **`cuda_suspend`/`checkpoint`:** Works with vLLM v0.13.x+ with NCCL patches (included in Docker image). Requires `cuda-checkpoint` and CRIU (included in Docker image).
- **TP>1 with `cuda_suspend`/`checkpoint`:** Requires vLLM NCCL suspend/resume patches (included in Docker image) plus `--enforce-eager` and `--disable-custom-all-reduce` flags.
## License
MIT