Qwen3-Coder-Next-NVFP4
Qwen3-Coder-Next-NVFP4 is an NVIDIA FP4-quantized version of Qwen's 80B parameter mixture-of-experts code generation model. It compresses the model from ~149GB to 45GB (70% reduction) while maintaining most capability. The model uses NVFP4 quantization (4-bit weights and activations), supports 256K context length, and is optimized for vLLM serving. It is publicly available under Apache 2.0 license with no access restrictions.
Key facts
Objective fields from the source. Values we can't verify are shown as “Unknown” rather than guessed.
| Field | Value |
|---|---|
| Developer | GadflyII |
| Parameters | Unknown |
| Context window | Unknown |
| License | apache-2.0 — OSI-approved |
| Modality / task | text-generation |
| Gated on HuggingFace | No |
| Downloads | 103.6k |
| Likes | 45 |
| Last updated | 2026-02-04 |
| Source | GadflyII/Qwen3-Coder-Next-NVFP4 |
What Qwen3-Coder-Next-NVFP4 is
Base model: Qwen/Qwen3-Coder-Next (80B total, 3B activated per token). Architecture: Hybrid DeltaNet + Attention + MoE with 512 total experts, 10 activated + 1 shared, 48 layers, 262K token context. Quantization: NVFP4 (4-bit weights + 4-bit activations via llmcompressor 0.9.0.1). Key layers remain BF16 (lm_head, MoE gates, DeltaNet linear attention). Calibration: 20 samples from HuggingFaceH4/ultrachat_200k, sequence length 2048. MMLU-Pro performance: 51.27% (BF16: 52.90%, -1.63% delta). Tested to 128K context with FP8 KV cache. Format: compressed-tensors. Requires vLLM 0.16.0+, Transformers 5.0.0+. Developer notes vLLM fork for SM120 Blackwell P2P/TP=2 workaround.
Run Qwen3-Coder-Next-NVFP4 locally
Load the open weights with 🤗 Transformers and generate — the same model, self-hosted.
from transformers import pipelinepipe = pipeline("text-generation", model="GadflyII/Qwen3-Coder-Next-NVFP4")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 for inference:** 2x NVIDIA A100 40GB or equivalent (dual H100, RTX 6000 Ada, or better). ESTIMATE: 45GB model weight + KV cache (context-dependent, up to ~30–50GB for batch size >1 at 64K+ context). FP8 KV cache reduces memory footprint. Multi-GPU recommended (TP=2) for throughput. **No CPU quantization support evident; NVIDIA GPU required.** Single A6000 (48GB) may work for small batches, low context; untested by maintainer beyond 128K.
Unknown from card. Base model architecture (MoE + DeltaNet) supports LoRA, but no guidance on quantized fine-tuning provided. NVFP4 quantization may complicate gradient computation; QLoRA feasibility requires testing. Recommend starting with full-precision LoRA on unquantized base (Qwen/Qwen3-Coder-Next) or quantization-aware fine-tuning using llmcompressor. Community support likely limited; escalate to Qwen team or vLLM project if custom adaptation needed.
When to avoid it — and what to weigh
- Extremely latency-sensitive services (<10ms SLO) — NVFP4 quantization and MoE routing add overhead. vLLM serving with tensor parallelism (TP=2+) mitigates but introduces coordination latency. Confirm with benchmarks before SLA commitments.
- Consumer/edge single-GPU deployments — 45GB model size requires modern high-VRAM hardware (2x40GB+ or equivalent). Not practical on consumer GPUs. Recommend smaller quantized models (7B–13B) for edge.
- Production systems without vLLM/Transformers expertise — Requires vLLM 0.16.0+, Transformers 5.0.0+, and NVIDIA-specific quantization support. Non-trivial deployment; team must be comfortable with containerization, tensor parallelism, and debugging quantization artifacts.
- Zero-shot accuracy-critical tasks requiring >52% MMLU-Pro — MMLU-Pro drops 1.63pp vs. BF16 (52.90% → 51.27%). If benchmark accuracy is contractual requirement, benchmark application-specific performance first.
License & commercial use
Apache License 2.0 (same as base model Qwen/Qwen3-Coder-Next). No gating, publicly available.
Apache 2.0 is a permissive OSI license explicitly allowing commercial use, modification, and distribution. No proprietary restrictions. However, assume responsibility for: (1) compliance with Qwen base model's terms (verify Qwen/Qwen3-Coder-Next licensing if different); (2) quantization methodology attribution (llmcompressor, vLLM project); (3) infrastructure licensing (NVIDIA software, cloud provider). No warranty or support implied. Recommend legal review for high-stakes commercial deployments.
DEV.co evaluation signals
Editorial assessment — not user reviews. Directional, with an explicit confidence level.
| Signal | Assessment |
|---|---|
| Maintenance | Moderate |
| Documentation | Adequate |
| License clarity | Clear |
| Deployment complexity | High |
| DEV.co fit | Good |
| Assessment confidence | Medium |
Card does not disclose security posture, threat model, or vulnerability management. Standard OSS considerations apply: (1) Verify integrity of model weights and dependencies (vLLM, llmcompressor, Transformers); (2) Quantized weights may be harder to audit than source; (3) MoE routing and gating logic could introduce side-channel exposure under adversarial input—not addressed; (4) Self-hosted deployment requires securing inference endpoints, managing access logs, and monitoring token leakage; (5) No mention of adversarial robustness or jailbreak mitigations. Treat as development/staging artifact until formal security review.
Alternatives to consider
Qwen/Qwen3-Coder-Next (full BF16)
Baseline unquantized model; higher accuracy (52.90% MMLU-Pro), no quantization artifacts, simpler debugging. Trade-off: ~149GB, higher cost, longer latency.
deepseek-coder-33b-instruct (quantized, smaller)
33B parameter alternative; easier single-GPU deployment, lower latency, Apache 2.0 license. Trade-off: ~60% fewer parameters, may underperform on complex reasoning.
Meta Llama-3.1-70B-Instruct (fp8/int8 quantization)
70B general-purpose LLM with strong code ability; widely supported by vLLM/TGI; mature quantization ecosystem. Trade-off: not code-specialized; different license (requires review for commercial use—Llama license has restrictions).
Ship Qwen3-Coder-Next-NVFP4 with senior software developers
Qwen3-Coder-Next-NVFP4 enables enterprise-grade code generation on your infrastructure. Get started with vLLM, evaluate on your codebase, and contact our team for deployment architecture and licensing guidance.
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Qwen3-Coder-Next-NVFP4 FAQ
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Qwen3-Coder-Next-NVFP4 enables enterprise-grade code generation on your infrastructure. Get started with vLLM, evaluate on your codebase, and contact our team for deployment architecture and licensing guidance.