The way to Replace LLM Weights with No Downtime

Think about attempting to renovate the muse of a towering skyscraper with out asking its occupants to depart or pause their work. That’s precisely what MoonshotAI’s Checkpoint Engine does for AI fashions. It permits huge language fashions to replace their brains, the weights, whereas nonetheless operating, so there’s no downtime. This breakthrough lets builders enhance their AI rapidly and effectively, even on fashions with over a trillion parameters operating on 1000’s of GPUs. It’s quick, dependable, and designed to maintain AI programs operating easily whereas evolving in real-time, making it an important software for cutting-edge AI purposes. This text goes over what it’s, the way it works, and why it issues for the way forward for large-scale AI programs.

What’s Moonshot AI’s Checkpoint engine?

Moonshot AI’s Checkpoint Engine is a specialised middleware designed to replace the weights of giant language fashions (LLMs) in real-time throughout inference with out interrupting ongoing operations. This functionality is crucial in Reinforcement studying eventualities the place mannequin weights should be up to date continuously. The Checkpoint Engine at present integrates seamlessly with vLLM inference frameworks and provides optimized efficiency by way of pipelining and reminiscence administration strategies. It additionally offers options like reusing weights from present cases to cut back overhead in scaling eventualities.

Structure 

The core of the Checkpoint is the ParameterServer class, which handles the burden replace logic and orchestrates the information circulate.

1. H2D(Host to Machine): Strikes up to date weights from CPU reminiscence or storage to GPU reminiscence, utilizing optimized switch pipelines.
2. Broadcast: Distributes the burden throughout all inference engine cases effectively, leveraging CUDA IPC buffers for shared reminiscence communication.
3. Reload: Every inference engine then selectively reloads related weight shards from the broadcasted knowledge in accordance with its sharding sample.

These three-stage pipelines guarantee environment friendly, overlapping communication and copying for velocity.

When GPU reminiscence is restricted, the system can fall again to serial execution to keep up reliability.

Strategies Used

The Checkpoint Engine makes use of two foremost strategies to replace mannequin weights throughout inference.

1. Broadcast Technique: That is the quickest and the default method. That is best when a lot of inference cases should be up to date concurrently. It broadcasts the up to date weights from CPU reminiscence to all inference GPUs synchronously, making certain all cases keep completely in sync with minimal delay. 
2. P2P (Peer-to-Peer) Technique: It’s used when inference cases are added or eliminated dynamically throughout runtime. It avoids disrupting present inference workloads by sending weights immediately from CPUs in present cases to GPUs in new cases by way of a peer-to-peer switch system, permitting easy and versatile updates.

Working 

The Checkpoint Engine orchestrates your complete switch course of. It first gathers essential metadata to create a plan, together with deciding the correct bucket dimension for knowledge switch. Then, it executes the switch, controlling the inference engine by way of a ZeroMQ socket to maximise efficiency. It organizes knowledge switch into pipelines with overlapped communication and duplicate, enabling quick and environment friendly weight updates even underneath heavy workload.

By implementing the above-mentioned strategies and structure, the Checkpoint Engine permits reside weight updates for LLMs throughout 1000’s of GPUs with minimal latency and repair disruption.

Set up and Utilization

Set up

To make use of the quickest broadcast 

Use Code:

pip set up checkpoint-engine

To make use of the versatile P2P implementation:

Use Code:

pip set up 'checkpoint-engine[p2p]'

This can set up mooncake-transfer-engine to assist RDMA switch between totally different ranks.

Instance Use case

Step 1:

Put together an H800 or H20 machine with 8 GPUs with the most recent vLLM. Remember to embrace /collective_rpc API endpoint commit (out there in the primary department) since checkpoint-engine will use this endpoint to replace weights.

Step 2:

set up checkpoint-engine

Code:

uv pip set up 'checkpoint-engine[p2p]'

Step 3:

For our use case, we’re gonna use Qwen/Qwen3-235B-A22B-Instruct-2507 because the take a look at mannequin.

Code:

hf obtain Qwen/Qwen3-235B-A22B-Instruct-2507 --local-dir /choose/fashions/Qwen/Qwen3-235B-A22B-Instruct-2507/

Step 4:

Begin vLLM in dev mode and set –load-format dummy. Be certain to set –worker-extension-cls=checkpoint_engine.employee.VllmColocateWorkerExtension

Code:

VLLM_SERVER_DEV_MODE=1 python3 -m vllm.entrypoints.openai.api_server --host 0.0.0.0 --port 19730 --trust-remote-code      --tensor-parallel-size=8 --max-model-len 4096 --load-format dummy      --served-model-name checkpoint-engine-demo --model /choose/fashions/Qwen/Qwen3-235B-A22B-Instruct-2507/      --worker-extension-cls checkpoint_engine.employee.VllmColocateWorkerExtension

To replace weights by checkpoint-engine. No want to attend for vLLM to prepare. Use the code under.

Code:

torchrun --nproc-per-node 8 examples/replace.py --update-method all --checkpoint-path /choose/fashions/Qwen/Qwen3-235B-A22B-Instruct-2507/

To reuse weights from present cases

New checkpoint-engine cases can be part of present cases and reuse their weights.

Utilizing the tactic under:

Step 1: Begin the present occasion with –save-metas-file global_metas.pkl to save lots of international metas to a file.

Step 2: Use –sleep-time 300 to verify they keep alive.

Code:

torchrun --nproc-per-node 8 examples/replace.py --checkpoint-path $MODEL_PATH      --sleep-time 300 --save-metas-file global_metas.pkl

Step 3: After a checkpoint is registered, new cases can acquire a replica of the checkpoint by setting –load-metas-file global_metas.pkl

Code:

torchrun --nproc-per-node 8 examples/replace.py --load-metas-file global_metas.pkl

FP8 quantization

Presently, FP8 quantization doesn’t work in vLLM when updating weights. It makes use of a easy patch in patches/vllm_fp8.patch to deal with the proper weight replace. Additionally ,this patch is just examined in DeepSeek-V3.1 and Kimi-K2. So there are probabilities of having some compatibility points with different fashions.

Take a look at

Run a easy correctness take a look at for checkpoint_engine

Code:

torchrun --nproc-per-node 8 assessments/test_update.py

Benchmark

Insights

Listed here are just a few observations that I’ve made:

1. The published methodology usually provides the quickest replace time, optimized for synchronous weight updates throughout many inference cases.
2. The P2P methodology takes longer however permits dynamic updates when cases be part of or depart throughout runtime.
3. These benchmark reveals the scalability of Checkpoint Engine, dealing with a trillion parameter fashions effectively on clusters starting from 8 to 256 GPUs

Limitations of Checkpoint Engine

Whereas Checkpoint Engine is a strong answer for reside weight updates in LLMs, it at present has some limitations.

• Works Finest with vLLM for Now: The engine is especially examined with the vLLM framework. Should you’re hoping to make use of it with different AI frameworks or customized setups, you may want some additional work to get it operating easily.
• Pipeline Nonetheless Bettering: The best seamless pipeline that overlaps knowledge strikes completely isn’t absolutely completed but. This implies there’s nonetheless potential to make the updates even sooner.
• P2P Replace Might Be Smoother: The peer-to-peer methodology sends knowledge by way of a bottleneck at one foremost node earlier than sharing it with others, which may sluggish issues down when you’ve plenty of GPUs.
• Wants Further GPU Reminiscence: The intelligent broadcast system makes use of extra GPU reminiscence to hurry issues up. On machines with much less reminiscence, it switches to a slower, much less environment friendly course of.
• Restricted Help for FP8 Fashions: Should you’re working with the newer FP8 quantized fashions, you’ll want some experimental patches. And even then, not all fashions play properly, but past a few examined ones.

Conclusion

Moonshot AI’s Checkpoint Engine is a game-changer for updating enormous AI fashions with out stopping them. It retains every part operating easily, even whereas the mannequin’s “mind” is getting smarter in real-time. Whereas it nonetheless has just a few areas to enhance, the potential is large. Should you’re working with giant AI programs, this software is certainly price watching. It’s serving to make the way forward for AI sooner and extra environment friendly, with none downtime.

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