Qwen3-Coder-30B-A3B-Instruct
Qwen3-Coder-30B-A3B-Instruct is a 30.5B parameter open-source coding LLM from Alibaba's Qwen team, released December 2025. It uses a mixture-of-experts (MoE) architecture with only 3.3B parameters active per token, enabling efficient inference. The model natively supports 256K token context (extendable to 1M with Yarn) and is optimized for coding tasks, agentic coding, tool calling, and browser automation. Licensed under Apache 2.0, it is freely available for download without gating.
Key facts
Objective fields from the source. Values we can't verify are shown as “Unknown” rather than guessed.
| Field | Value |
|---|---|
| Developer | Qwen |
| Parameters | 30.5B |
| Context window | Unknown |
| License | apache-2.0 — OSI-approved |
| Modality / task | text-generation |
| Gated on HuggingFace | No |
| Downloads | 1.7M |
| Likes | 1.1k |
| Last updated | 2025-12-03 |
| Source | Qwen/Qwen3-Coder-30B-A3B-Instruct |
What Qwen3-Coder-30B-A3B-Instruct is
A causal language model with 48 layers, 32 Q-heads + 4 KV-heads (GQA), and 128 experts (8 activated). Trained via pretraining and post-training. Native 262,144 token context length. Requires transformers>=4.51.0 (supports qwen3_moe architecture). No thinking/reasoning tokens generated. Safetensors format. Compatible with OpenAI-API-compatible endpoints, Azure deployment, and frameworks like Ollama, llama.cpp, MLX-LM, LMStudio.
Run Qwen3-Coder-30B-A3B-Instruct locally
Load the open weights with 🤗 Transformers and generate — the same model, self-hosted.
from transformers import pipelinepipe = pipeline("text-generation", model="Qwen/Qwen3-Coder-30B-A3B-Instruct")out = pipe("Explain retrieval-augmented generation in one sentence.", max_new_tokens=128)print(out[0]["generated_text"])Swap in vLLM or Ollama for production-grade serving. DEV.co can stand up the inference stack.
How you'd run it
A typical self-hosted path — open weights, an inference server, your application.
DEV.co builds each layer — from GPU infrastructure to the application.
Best use cases
Running & fine-tuning it
**ESTIMATE for local inference (requires verification):** ~60–70 GB VRAM for full bf16 loading (30.5B params, ~2 bytes/param). MoE activation (8 of 128 experts) may reduce working memory vs. dense model. Recommend GPU with ≥48 GB for production. 256K context extension increases peak memory significantly; model card notes OOM risk and suggests reducing context to 32K if needed. Specific throughput benchmarks not provided in card.
Card does not discuss LoRA, QLoRA, or fine-tuning feasibility. MoE architecture may add complexity to parameter-efficient tuning. Requires investigation: whether expert selection is tunable, gradient flow across MoE layers, and community recipes for QLoRA on qwen3_moe.
When to avoid it — and what to weigh
- Reasoning-Heavy Scientific/Math Tasks — Model explicitly supports non-thinking mode only; does not generate internal reasoning blocks. Benchmark performance on general math/logic reasoning is not stated in card.
- Extreme Context Extension Demands — While 1M context is supported via Yarn, actual throughput, latency, and stability at that scale are not documented. Production use at maximum context requires careful validation.
- Real-Time / Ultra-Low-Latency Inference — MoE routing overhead and transformer complexity mean latency may exceed dense smaller models. Confirm inference timing for your SLA before deployment.
- Proprietary / Closed-Source Commercial Products Without Legal Review — Apache 2.0 allows commercial use, but ensure compliance with attribution and derived-work disclosure requirements in your specific jurisdiction and product context.
License & commercial use
Apache License 2.0 (OSI-approved permissive license). Permits commercial use, modification, and distribution. Requires inclusion of license notice and statement of material changes.
Apache 2.0 is a permissive OSI license that explicitly permits commercial use, provided attribution and license terms are preserved. No additional commercial license or review is required for the license itself. However, responsible parties should verify compliance with derived-work disclosure, trademark usage, and jurisdictional IP law. No gating; unrestricted download.
DEV.co evaluation signals
Editorial assessment — not user reviews. Directional, with an explicit confidence level.
| Signal | Assessment |
|---|---|
| Maintenance | Active |
| Documentation | Adequate |
| License clarity | Clear |
| Deployment complexity | Moderate |
| DEV.co fit | Strong |
| Assessment confidence | High |
Open-source model; weights and architecture are public. No mention of adversarial robustness, prompt injection mitigations, or jailbreak testing. As with all LLMs: code-generation outputs should be reviewed before execution; tool-calling capabilities require careful input validation to prevent abuse. MoE routing and function calling introduce additional code paths for security review. No security audit or red-team results provided in card.
Alternatives to consider
DeepSeek-Coder-V2 / DeepSeek-V3
Comparable or larger coding LLMs; may offer different efficiency/performance trade-offs. Requires license/gating review.
Code Llama (Meta)
Established open-source coding model; smaller variants available. Note: Llama 2/3 use non-OSI license; requires review for commercial use.
StarCoder2 / StarCoderBase (BigCode)
Permissively licensed (Open Rail-M or similar); specialized for code. Smaller models offer lower inference cost.
Ship Qwen3-Coder-30B-A3B-Instruct with senior software developers
Qwen3-Coder-30B-A3B-Instruct combines efficient MoE inference, long-context understanding, and tool-calling for agentic coding. Whether you're building self-hosted AI agents, optimizing code workflows, or deploying on-premise LLM services, explore how this model fits your architecture. Start with a local test deployment using Ollama or vLLM; consult our private-LLM and custom-LLM-apps services for production guidance.
Talk to DEV.coRelated open-source tools
Surfaced by semantic similarity across the DEV.co open-source index.
Related on DEV.co
Explore the category and the services that help you build with it.
Qwen3-Coder-30B-A3B-Instruct FAQ
Can I use this model in a commercial product?
What GPU do I need to run this locally?
Can I fine-tune this model?
Does it generate reasoning / thinking blocks like o1?
Software development & web development with DEV.co
DEV.co is a software development agency delivering custom software development services to companies building on open source. Our software developers and web developers design, integrate, and ship production systems — spanning web development, APIs, AI, data, and cloud. If Qwen3-Coder-30B-A3B-Instruct is part of your open-source llms roadmap, our team can implement, customize, migrate, and maintain it.
Ready to Deploy a Permissive, Production-Grade Coding LLM?
Qwen3-Coder-30B-A3B-Instruct combines efficient MoE inference, long-context understanding, and tool-calling for agentic coding. Whether you're building self-hosted AI agents, optimizing code workflows, or deploying on-premise LLM services, explore how this model fits your architecture. Start with a local test deployment using Ollama or vLLM; consult our private-LLM and custom-LLM-apps services for production guidance.