Qwen3.6-35B-A3B is a fully open-source sparse MoE model (35B total parameters / 3B active parameters) that excels at agentic coding, significantly outperforming its predecessor Qwen3.5-35B-A3B and holding its own against dense models such as Qwen3.5-27B and Gemma4-31B. Key features include:
Outstanding agentic coding capabilities, comparable to much larger models
Strong multimodal perception and reasoning abilities
Integrated Deployment
Out-of-the-box inference scripts with pre-configured hardware and software parameters
Released FlagOS-Metax container image supporting deployment within minutes
Consistency Validation
Rigorously evaluated through benchmark testing: Performance and results from the FlagOS software stack are compared against native stacks on multiple public.
Evaluation Results
Benchmark Result
Metrics
Qwen3.6-35B-A3B-nomtp-Nvidia-Origin
Qwen3.6-35B-A3B-nomtp-Metax-FlagOS
GPQA_Diamond
0.8283
0.803
ERQA
0.5875
0.6
Performance Benchmark Result
Metric
1k&1k 64 Concurrency
4k&1k 64 Concurrency
16k&1k 64 Concurrency
64k&1k 64 Concurrency
Equal Computing Power Ratio (FlagOS/H100)
136.94%
125.03%
113.468%
87.97%
User Guide
Environment Setup
Item
Version
Docker Version
Docker version 27.5.1, build 27.5.1-0ubuntu3~22.04.2
Choose the appropriate version for your OS (Windows/macOS/Linux)
Follow the installation wizard to complete the setup
2. Configuration
Launch AnythingLLM
Open settings (bottom left, fourth tab)
Configure core LLM parameters
Click "Save Settings" to apply changes
3. Model Interaction
After model loading is complete:
Click "New Conversation"
Enter your question (e.g., “Explain the basics of quantum computing”)
Click the send button to get a response
Technical Overview
FlagOS is a fully open-source system software stack designed to unify the "model–system–chip" layers and foster an open, collaborative ecosystem. It enables a “develop once, run anywhere” workflow across diverse AI accelerators, unlocking hardware performance, eliminating fragmentation among vendor-specific software stacks, and substantially lowering the cost of porting and maintaining AI workloads. With core technologies such as the FlagScale, together with vllm-plugin-fl, distributed training/inference framework, FlagGems universal operator library, FlagCX communication library, and FlagTree unified compiler, the FlagRelease platform leverages the FlagOS stack to automatically produce and release various combinations of <chip + open-source model>. This enables efficient and automated model migration across diverse chips, opening a new chapter for large model deployment and application.
FlagGems
FlagGems is a high-performance, generic operator libraryimplemented in Triton language. It is built on a collection of backend-neutralkernels that aims to accelerate LLM (Large-Language Models) training and inference across diverse hardware platforms.
FlagTree
FlagTree is an open source, unified compiler for multipleAI chips project dedicated to developing a diverse ecosystem of AI chip compilers and related tooling platforms, thereby fostering and strengthening the upstream and downstream Triton ecosystem. Currently in its initial phase, the project aims to maintain compatibility with existing adaptation solutions while unifying the codebase to rapidly implement single-repository multi-backend support. Forupstream model users, it provides unified compilation capabilities across multiple backends; for downstream chip manufacturers, it offers examples of Triton ecosystem integration.
FlagScale and vllm-plugin-fl
Flagscale is a comprehensive toolkit designed to supportthe entire lifecycle of large models. It builds on the strengths of several prominent open-source projects, including Megatron-LM and vLLM, to provide a robust, end-to-end solution for managing and scaling large models.
vllm-plugin-fl is a vLLM plugin built on the FlagOS unified multi-chip backend, to help flagscale support multi-chip on vllm framework.
FlagCX
FlagCX is a scalable and adaptive cross-chip communication library. It serves as a platform where developers, researchers, and AI engineers can collaborate on various projects, contribute to the development of cutting-edge AI solutions, and share their work with the global community.
FlagEval Evaluation Framework
FlagEval is a comprehensive evaluation system and open platform for large models launched in 2023. It aims to establish scientific, fair, and open benchmarks, methodologies, and tools to help researchers assess model and training algorithm performance. It features:
Multi-dimensional Evaluation: Supports 800+ modelevaluations across NLP, CV, Audio, and Multimodal fields,covering 20+ downstream tasks including language understanding and image-text generation.
Industry-Grade Use Cases: Has completed horizonta1 evaluations of mainstream large models, providing authoritative benchmarks for chip-model performance validation.
