Qwen3-Coder-Next-GGUF
Qwen3-Coder-Next is an open-weight coding LLM with 80B total parameters but only 3B activated, optimized for local deployment and agentic coding tasks. It supports a 256k context window, tool calling, and runs on consumer hardware via quantized GGUF versions. The model is distributed via Unsloth in GGUF format and requires 30–45GB unified memory for practical use.
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 | 230.7k |
| Likes | 754 |
| Last updated | 2026-03-06 |
| Source | unsloth/Qwen3-Coder-Next-GGUF |
What Qwen3-Coder-Next-GGUF is
Qwen3-Coder-Next is a causal language model featuring a hybrid architecture: 48 layers with 12 repeating blocks combining Gated DeltaNet (linear attention) and Gated Attention, plus Mixture of Experts (512 experts, 10 activated per token). Context length is 262,144 tokens natively. The model does not generate thinking blocks and supports tool-calling for agentic workflows. This Unsloth GGUF distribution is quantized for local inference via llama.cpp, Ollama, LMStudio, and compatible APIs (vLLM, SGLang).
Run Qwen3-Coder-Next-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-Next-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
Minimum 30GB unified memory (RAM + VRAM combined) for 2-bit XL quantization; 45GB recommended for 4-bit quantization. Tested on multi-GPU setups (examples use tensor parallelism across 2–4 GPUs). Single-GPU inference feasible with smaller quants and context-length reduction. ESTIMATE: 4-bit GGUF ~20–25GB VRAM, 2-bit ~15GB VRAM (verify with your quantization choice).
Model card does not document LoRA, QLoRA, or fine-tuning procedures. Unsloth is known for enabling efficient fine-tuning frameworks, but specific guidance for Qwen3-Coder-Next is absent from provided documentation. Requires review of Unsloth docs and community resources.
When to avoid it — and what to weigh
- Real-Time Ultra-Low Latency Requirements — Quantized GGUF inference on consumer hardware may not meet sub-100ms latency targets. Requires careful optimization and hardware tuning.
- Non-English or Domain-Specific Coding Languages — Model card focuses on general coding. Performance on specialized or non-English code generation is not clearly documented.
- Deployment Without Thinking/Reasoning Output — Model does not generate reasoning blocks. If you need step-by-step reasoning in output, this model is not suitable.
- Production Deployment Without Rigorous Testing — Feb 2025 bug fixes in llama.cpp indicate instability in early releases. Requires thorough validation before critical deployments.
License & commercial use
Apache 2.0 license. This is a permissive OSI-approved open-source license allowing commercial use, modification, and distribution with attribution.
Apache 2.0 permits commercial use without restriction. No gating or licensing restrictions apply. Model weights are open-weight, and quantized GGUF versions are freely redistributable under Apache 2.0. No vendor lock-in or proprietary licensing.
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 |
Apache 2.0 and open-weight distribution mean source code and weights are auditable. No proprietary black-box inference. However: (1) Quantized GGUF artifacts are third-party redistributions—verify source integrity before production use; (2) Model is not evaluated against adversarial or injection attacks in provided documentation; (3) Local deployment mitigates data transmission risks but requires securing inference infrastructure. (4) Tool-calling capability introduces agent-execution risks—validate tool implementations. No formal security audit data provided.
Alternatives to consider
DeepSeek-Coder-V2 / DeepSeek-R1
Comparable coding performance; DeepSeek-R1 includes reasoning. Larger footprint; similar licensing (permissive). Consider if reasoning output or multilingual support is critical.
Llama 3.1 / Llama 3.2 (Meta)
General-purpose LLMs with strong code capabilities and broader community support. Llama 3.2 includes vision; Llama 3.1 larger context. Llama license is permissive but gated; verify for your use case.
Mistral Large or Mixtral (Mistral AI)
Mixture-of-Experts architecture similar to Qwen3; smaller footprint options. Apache 2.0 or Mistral license (permissive). Strong community tooling and integration ecosystem.
Ship Qwen3-Coder-Next-GGUF with senior software developers
Start with the Unsloth GGUF distribution and follow the quickstart guide. Use vLLM or SGLang for production serving, or Ollama/LMStudio for local development. Verify recent llama.cpp updates and test on your target hardware first.
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Qwen3-Coder-Next-GGUF FAQ
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Does this model support fine-tuning?
Work with a software development agency
Need help beyond evaluating Qwen3-Coder-Next-GGUF? DEV.co is a software development agency offering software development services and web development for teams of every size. Our software developers and web developers build custom software, web applications, APIs, and open-source llms integrations — and maintain them long-term.
Ready to deploy Qwen3-Coder-Next?
Start with the Unsloth GGUF distribution and follow the quickstart guide. Use vLLM or SGLang for production serving, or Ollama/LMStudio for local development. Verify recent llama.cpp updates and test on your target hardware first.