QuantumFlow: A Distributed Large Model Inference Scheduling Framework for Production Environments

QuantumFlow is an open-source distributed LLM inference scheduling platform designed to address the core challenge of efficiently running hundred-billion-parameter models in heterogeneous hardware environments. It supports multi-backend engines, intelligent scheduling strategies, and enterprise-level cluster management. Its core philosophy is to make inference task scheduling as flexible as managing Kubernetes Pods, improving resource utilization and reducing operational complexity.

In the implementation of LLMs, the stability and efficiency of inference services are core challenges for enterprise-level applications: How to schedule models of different scales with limited GPU resources? How to achieve unified management and elastic scaling across heterogeneous hardware? QuantumFlow is positioned as the "next-generation distributed large model inference platform", with the vision of enabling hundred-billion-parameter models to run on every machine, replacing traditional manual resource allocation methods through an intelligent scheduling layer.

QuantumFlow adopts a layered architecture (execution layer, cluster management layer, scheduling layer, access layer) with the following core highlights:

1. Multi-backend support: The execution layer provides a unified API, supporting HuggingFace Transformers (verified), vLLM (to be fixed), TGI/SGLang/TensorRT-LLM (planned);
2. Intelligent scheduling: Gang scheduling (for large models, allocates resources in one go), Pack scheduling (optimized for small models, multiple requests share a GPU), adaptive scheduling (dynamically selects mode, under development);
3. Cluster management: Single-machine mode is completed; distributed multi-node and Ascend NPU adaptation are under planning.

QuantumFlow optimizes user experience and provides multiple interaction methods:

• One-click startup: Run ./scripts/qf to start the service, then visit http://localhost:8000 to enter the visual console;
• CLI tool: Supports commands such as viewing cluster status, listing models, loading models, and generating conversations (e.g., python -m quantumflow.cli chat Qwen2.5-1.5B -p "Hello");
• Interactive terminal: Suitable for exploration and debugging.

Performance test data based on NVIDIA A100 80GB:

Model	Parameter Count	Parallel Strategy	Throughput	Latency
Qwen2.5-7B	7B	TP=1	150 tok/s	45ms
Qwen2.5-72B	72B	TP=4	80 tok/s	120ms
LLaMA-3-70B	70B	TP=8	60 tok/s	180ms
DeepSeek-V2	236B	TP=16	40 tok/s	300ms
Covers models ranging from 7B to 236B parameters, adapting to different hardware requirements.

QuantumFlow is under active development:

• ✅ Completed: REST API (FastAPI), core scheduler logic, HuggingFace backend, CLI tool, 266 unit tests;
• 🔄 To be fixed: vLLM backend (memory bug);
• 📋 Planned: TGI/SGLang backend, distributed multi-node, Ascend NPU adaptation, enterprise features such as multi-tenancy/rate limiting/disaster recovery.

QuantumFlow is an important attempt in open-source LLM inference infrastructure and a complete solution for production. Through intelligent scheduling, multi-backend support, and layered architecture, it is expected to lower the threshold for enterprises to deploy large models and become an important force in domestic open-source LLM infrastructure.