Qwen3-Coder-30B-A3B-Instruct-AWQ-4bit
Qwen3-Coder-30B-A3B-Instruct is a 30.5B-parameter open-source coding LLM with a Mixture-of-Experts (MoE) architecture that activates only 3.3B parameters at inference time. It supports 256K tokens natively (extendable to 1M), excels at agentic coding tasks, and is distributed as a quantized 4-bit AWQ model under Apache 2.0 license. No gates or restrictions apply.
Key facts
Objective fields from the source. Values we can't verify are shown as “Unknown” rather than guessed.
| Field | Value |
|---|---|
| Developer | cyankiwi |
| Parameters | 5.3B |
| Context window | Unknown |
| License | apache-2.0 — OSI-approved |
| Modality / task | text-generation |
| Gated on HuggingFace | No |
| Downloads | 245.2k |
| Likes | 56 |
| Last updated | 2026-05-06 |
| Source | cyankiwi/Qwen3-Coder-30B-A3B-Instruct-AWQ-4bit |
What Qwen3-Coder-30B-A3B-Instruct-AWQ-4bit is
Causal language model based on Qwen3 MoE architecture with 48 layers, 128 total experts (8 activated per token), and GQA attention (32 Q-heads, 4 KV-heads). Instruction-tuned post-training. Quantized to 4-bit AWQ format for efficient inference. Native context window 262,144 tokens; rope scaling (Yarn) enables up to 1M tokens. Supports tool-calling/function-invocation for agentic workflows. Does not generate thinking blocks.
Run Qwen3-Coder-30B-A3B-Instruct-AWQ-4bit locally
Load the open weights with 🤗 Transformers and generate — the same model, self-hosted.
from transformers import pipelinepipe = pipeline("text-generation", model="cyankiwi/Qwen3-Coder-30B-A3B-Instruct-AWQ-4bit")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 (4-bit quantized): 8–16 GB VRAM for inference at 256K context on a single high-end GPU (e.g. RTX 4090, A100 40GB). Exact VRAM scales with batch size, context length, and framework overhead. Model card notes OOM risk and suggests reducing context to 32K if needed. Multi-GPU or CPU inference will be significantly slower. Requires transformers>=4.51.0 (earlier versions error on qwen3_moe architecture).
Not explicitly stated in card. MoE models support LoRA/QLoRA but routing dynamics and expert sparsity require careful hyperparameter tuning. LoRA on frozen base + adapter on routing layers is common practice. Recommend consulting Qwen documentation or GitHub for LoRA-specific guidance and any released training scripts.
When to avoid it — and what to weigh
- General-purpose conversation without code focus — Model is specialized for coding tasks. For general chat, translation, or domain-specific non-coding tasks, consider broader generalist models.
- Severe memory constraints (e.g., mobile, edge devices) — Even at 4-bit quantization, 30B parameters require substantial VRAM. Inference on consumer GPUs or CPUs without acceleration will be slow.
- Real-time, ultra-low latency requirements — MoE routing and 256K context window imply higher per-token latency than dense models of equivalent size. Verify latency with your workload before production use.
- Proprietary or closed-source deployment — Apache 2.0 requires code and modifications to be available; incompatible with projects requiring non-disclosure of model usage or derivative works.
License & commercial use
Apache 2.0. Permissive OSI-approved license. No usage gates, no commercial restrictions in the license terms themselves.
Apache 2.0 permits commercial use, modification, and distribution, provided the license and copyright notice are retained and changes are disclosed. Model is ungated. However, cyankiwi (uploader) is a community contributor, not Alibaba/Qwen official. For production deployment, verify that your use of derivatives complies with Apache 2.0 attribution and disclosure requirements. No indemnification or support guarantees from Alibaba or the uploader.
DEV.co evaluation signals
Editorial assessment — not user reviews. Directional, with an explicit confidence level.
| Signal | Assessment |
|---|---|
| Maintenance | Moderate |
| Documentation | Strong |
| License clarity | Clear |
| Deployment complexity | Moderate |
| DEV.co fit | Strong |
| Assessment confidence | High |
No explicit security audit or threat model stated. Quantization (AWQ) may introduce subtle numerical artifacts; validate outputs in safety-critical code generation. Model is open-source; review training data and mitigation techniques on Qwen GitHub/blog. As a code-generation model, outputs should be reviewed before execution (standard practice for all LLM code). No information on watermarking, jailbreak defenses, or adversarial robustness.
Alternatives to consider
CodeLlama-70B (Meta)
Larger dense model, broader language support. Lower inference efficiency; no MoE sparsity. Requires more VRAM. Apache 2.0 licensed.
DeepSeek-Coder-7B/33B
Competitive code performance at smaller parameter count. Requires review of DeepSeek's license and commercial use terms.
GPT-4o / Claude Opus (proprietary APIs)
Closed-source, SOC 2 compliant, no self-hosting required. Trade-off: data leaves your infrastructure; higher per-token cost; no customization.
Ship Qwen3-Coder-30B-A3B-Instruct-AWQ-4bit with senior software developers
Qwen3-Coder excels in agentic coding, repository analysis, and tool-integrated workflows. For guidance on self-hosting, fine-tuning, or integrating into your development stack, explore our AI deployment and custom LLM application services.
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Qwen3-Coder-30B-A3B-Instruct-AWQ-4bit FAQ
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Ready to Deploy Advanced Open-Source Coding LLMs?
Qwen3-Coder excels in agentic coding, repository analysis, and tool-integrated workflows. For guidance on self-hosting, fine-tuning, or integrating into your development stack, explore our AI deployment and custom LLM application services.