Qwen3-Coder-30B-A3B-Instruct-GGUF
Qwen3-Coder-30B-A3B-Instruct is a 30.5B parameter mixture-of-experts (MoE) coding model with 3.3B activated parameters, distributed as a GGUF quantization by Unsloth. It natively supports 256K token context (extendable to 1M) and is optimized for code generation, agentic coding tasks, and tool-calling workflows. Licensed under Apache 2.0, it is freely available and gated=false.
Key facts
Objective fields from the source. Values we can't verify are shown as “Unknown” rather than guessed.
| Field | Value |
|---|---|
| Developer | unsloth |
| Parameters | Unknown |
| Context window | Unknown |
| License | apache-2.0 — OSI-approved |
| Modality / task | text-generation |
| Gated on HuggingFace | No |
| Downloads | 247.8k |
| Likes | 782 |
| Last updated | 2026-01-30 |
| Source | unsloth/Qwen3-Coder-30B-A3B-Instruct-GGUF |
What Qwen3-Coder-30B-A3B-Instruct-GGUF is
Causal language model trained via pretraining and post-training. Architecture: 48 layers, 32 query heads + 4 KV heads (GQA), 128 experts with 8 activated per token. Supports 262,144 native context length with Yarn extension. GGUF quantization format enables CPU/GPU inference. Requires transformers ≥4.51.0. Does not generate thinking tokens. Supports OpenAI-compatible API and tool-calling with custom function definitions.
Run Qwen3-Coder-30B-A3B-Instruct-GGUF locally
Load the open weights with 🤗 Transformers and generate — the same model, self-hosted.
from transformers import pipelinepipe = pipeline("text-generation", model="unsloth/Qwen3-Coder-30B-A3B-Instruct-GGUF")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: Full precision (bfloat16) ≈ 60–70 GB VRAM (8×A100 80GB or equivalent). GGUF quantizations significantly reduce footprint, but exact requirements unknown without Unsloth's quantization benchmarks. Model card recommends monitoring for OOM and optionally reducing context to 32K tokens. Context=256K with bfloat16 inference likely requires multi-GPU setup.
Model card references Unsloth's free Colab notebooks for fine-tuning Qwen3 (14B variant). Unsloth claims 3× speedup and 70% memory reduction for Qwen3 SFT. MoE architecture with expert selection may complicate LoRA; full-model or selective expert LoRA feasibility unknown. No explicit LoRA/QLoRA compatibility stated for 30B variant.
When to avoid it — and what to weigh
- Thinking/reasoning-heavy tasks requiring chain-of-thought — This model explicitly does not generate <think></think> blocks and is optimized for direct code generation, not step-by-step reasoning or mathematical proof generation.
- Production inference without performance profiling — Model card notes OOM risks and recommends reducing context to 32K if memory-constrained. Without benchmarking Unsloth's quantization on your hardware, deployment risk is elevated.
- Non-code generalist tasks — Qwen3-Coder is domain-specialized for code. General-purpose instruction following, creative writing, or non-technical QA may underperform compared to balanced foundation models.
- Environments without GPU or modern CPU with SIMD support — GGUF inference via llama.cpp requires AVX2+ on CPU. Even quantized, 30B models are memory-intensive; practical CPU-only deployment uncertain without detailed benchmarking.
License & commercial use
Apache License 2.0 (apache-2.0). OSI-approved permissive license allowing modification, distribution, and commercial use with attribution and license reproduction.
Apache 2.0 is a permissive OSI license explicitly permitting commercial use. No gating (gated=false) and no usage restrictions stated. Commercial deployment, proprietary applications, and SaaS integration are legally permitted provided Apache 2.0 headers remain. Unsloth quantization (GGUF) is community-provided; confirm Unsloth's terms if seeking indemnification.
DEV.co evaluation signals
Editorial assessment — not user reviews. Directional, with an explicit confidence level.
| Signal | Assessment |
|---|---|
| Maintenance | Active |
| Documentation | Strong |
| License clarity | Clear |
| Deployment complexity | Moderate |
| DEV.co fit | Strong |
| Assessment confidence | High |
Standard LLM supply-chain and deployment considerations apply: (1) GGUF artifacts sourced from Unsloth; verify integrity via checksums. (2) No adversarial robustness or jailbreak resistance testing disclosed. (3) Code generation models may produce insecure code patterns; always review generated code. (4) Self-hosted deployment eliminates vendor data collection but requires proper network isolation and access controls. (5) MoE architecture complexity may increase attack surface; no formal security audit mentioned.
Alternatives to consider
DeepSeek-Coder-V2 (Instruct)
Also 30B+-scale code-specialist MoE, publicly available. Compare agentic coding performance, context length, and quantization maturity. License and gating requirements differ.
Llama-3.2 Code (11B/70B variants)
Meta's code-optimized instruct model. Smaller 11B option for resource-constrained inference; 70B for maximum capability. Well-established quantization support (ollama, llama.cpp).
Mistral Large or Codestral
Permissive licensing, strong code performance. Smaller parameter count than Qwen3-Coder-30B; trade-off context length and agentic coding capabilities for inference cost reduction.
Ship Qwen3-Coder-30B-A3B-Instruct-GGUF with senior software developers
Evaluate this Apache 2.0 licensed coding model in your environment. Unsloth's GGUF quantization enables CPU/GPU inference with llama.cpp, Ollama, or custom APIs. Start with a Colab notebook to test agentic coding capabilities.
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Qwen3-Coder-30B-A3B-Instruct-GGUF FAQ
Can I use this model in a commercial product?
What are the actual GPU memory requirements for inference?
Does this model support fine-tuning? What is the easiest method?
How does performance compare to other 30B coding models?
Work with a software development agency
DEV.co helps companies turn open-source tools like Qwen3-Coder-30B-A3B-Instruct-GGUF into production software. Our software development services cover the full lifecycle — architecture, web development, integration, and maintenance — delivered by software developers and web developers who ship. Engage our software development agency to implement or customize it for your open-source llms stack.
Deploy Qwen3-Coder on Your Infrastructure
Evaluate this Apache 2.0 licensed coding model in your environment. Unsloth's GGUF quantization enables CPU/GPU inference with llama.cpp, Ollama, or custom APIs. Start with a Colab notebook to test agentic coding capabilities.