Qwen3-Coder-Next-AWQ-4bit
Qwen3-Coder-Next-AWQ-4bit is a 4-bit quantized version of Qwen's 80B-parameter mixture-of-experts coding model, with only 3B parameters activated per inference. It is designed for code generation, agentic workflows, and tool use, fitting into ~45GB VRAM with a 262k native context length. The model is open-weight under Apache 2.0, ungated, and ready for self-hosted deployment via vLLM or SGLang.
Key facts
Objective fields from the source. Values we can't verify are shown as “Unknown” rather than guessed.
| Field | Value |
|---|---|
| Developer | bullpoint |
| Parameters | 14.4B |
| Context window | Unknown |
| License | apache-2.0 — OSI-approved |
| Modality / task | text-generation |
| Gated on HuggingFace | No |
| Downloads | 154.3k |
| Likes | 29 |
| Last updated | 2026-02-03 |
| Source | bullpoint/Qwen3-Coder-Next-AWQ-4bit |
What Qwen3-Coder-Next-AWQ-4bit is
This is an AWQ-quantized (4-bit, group size 32) variant of Qwen3-Coder-Next, a hybrid-architecture model combining 12 layers of Gated DeltaNet and Gated Attention with Mixture-of-Experts routing (512 total experts, 10 activated, 1 shared). Quantization uses llm-compressor with selective precision preservation for embeddings, normalization, attention heads, and routing gates. Native context is 262,144 tokens. The model is distributed in safetensors format and compatible with transformers, vLLM ≥0.15.0, SGLang ≥0.5.8, and other inference frameworks. No thinking/reasoning blocks are generated.
Run Qwen3-Coder-Next-AWQ-4bit locally
Load the open weights with 🤗 Transformers and generate — the same model, self-hosted.
from transformers import pipelinepipe = pipeline("text-generation", model="bullpoint/Qwen3-Coder-Next-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
Quantized VRAM: ~45GB (4-bit AWQ). Original precision: ~151GB (BF16). Deployment recommendations: 2× GPU tensor parallelism suggested in documentation. Single-GPU inference possible but risks OOM; reducing context length to 32k may mitigate. GPU type not specified; assumes modern NVIDIA/AMD with sufficient memory bandwidth. CPU inference (e.g., llama.cpp) viability unknown.
Not addressed in provided documentation. Model is post-trained and specialized. LoRA or QLoRA fine-tuning feasibility on quantized weights is Unknown; typically requires testing with llm-compressor or standard PEFT. Fine-tuning is not mentioned as a supported workflow. Requires review if task-specific adaptation is planned.
When to avoid it — and what to weigh
- Latency-Critical Applications — MoE routing and 48-layer hybrid architecture may introduce higher per-token latency than dense models. Deployment documentation advises tensor parallelism across 2+ GPUs; single-GPU inference not explicitly benchmarked.
- General-Purpose NLP Beyond Coding — Model is specialized for code and agentic tasks. Performance on general language understanding, creative writing, or non-technical reasoning is not documented and likely underspecified versus general-purpose LLMs.
- Extreme Memory Constraints (< 45GB VRAM) — Documentation notes OOM risks and suggests reducing context to 32k. Not suitable for single-GPU <40GB VRAM deployments at full context. Quantization is already aggressive (4-bit); further compression options not detailed.
- Real-Time, Sub-100ms Inference SLAs — Model size and architecture suggest inference latency will be in the 200ms–1s+ range per token depending on hardware and batch size. No latency benchmarks provided.
License & commercial use
Licensed under Apache 2.0 (apache-2.0), a permissive OSI-approved license. Allows commercial use, modification, and distribution with attribution and inclusion of license notice.
Apache 2.0 is a permissive open-source license that explicitly permits commercial use. Model is ungated and downloadable without restrictions. Quantization was performed by bullpoint using public tooling (llm-compressor). No explicit limitations on commercial deployment are stated. However, verify compliance with base model (Qwen/Qwen3-Coder-Next) terms and any downstream service-level obligations in your deployment context.
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 |
Model is open-weight and publicly available. No security audit or vulnerability disclosure process mentioned. As a code-generation model, outputs could reflect training data biases or generate insecure code patterns; validate generated code before deployment. Quantization does not introduce new attack surface, but model weights are fully exposed for inspection/extraction. No secure enclave or attestation support documented.
Alternatives to consider
Qwen/Qwen3-Coder-Next (full precision)
Same base model at full BF16 precision (~151GB VRAM). Use if accuracy is critical and hardware supports it; this quantized variant trades precision for memory efficiency.
Meta Llama 3.1 or 3.3 (code variants)
General-purpose code models with simpler dense architecture, potentially lower latency. Llama-Code is less specialized for agents but may suit simpler code completion use cases.
DeepSeek-Coder-V2 or similar specialized coding models
Alternative specialized coding LLMs with different trade-offs in size, context, and agentic capability. Comparison benchmarks not provided in this card.
Ship Qwen3-Coder-Next-AWQ-4bit with senior software developers
Qwen3-Coder-Next-AWQ-4bit fits into modest GPU hardware with full agentic capabilities. Start with vLLM or SGLang for an OpenAI-compatible API. For production integration, evaluation, or deployment architecture guidance, reach out to our team.
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Qwen3-Coder-Next-AWQ-4bit FAQ
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Software development & web development with DEV.co
From first prototype to production, DEV.co delivers software development services around tools like Qwen3-Coder-Next-AWQ-4bit. Our software development agency staffs experienced software developers and web developers for custom software development, web development, integrations, and ongoing support across open-source llms and beyond.
Ready to Deploy a Self-Hosted Coding Agent?
Qwen3-Coder-Next-AWQ-4bit fits into modest GPU hardware with full agentic capabilities. Start with vLLM or SGLang for an OpenAI-compatible API. For production integration, evaluation, or deployment architecture guidance, reach out to our team.