Qwen3-30B-A3B-NVFP4
NVIDIA's quantized version of Alibaba's Qwen3-30B-A3B model, compressed to FP4 (4-bit) precision using TensorRT Model Optimizer. Reduces memory footprint by ~3.3x while maintaining near-baseline accuracy across reasoning and code benchmarks. Designed for production deployment on NVIDIA GPUs via TensorRT-LLM. Non-gated, Apache 2.0 licensed, ready for immediate use in chatbots, RAG, and agent systems.
Key facts
Objective fields from the source. Values we can't verify are shown as “Unknown” rather than guessed.
| Field | Value |
|---|---|
| Developer | nvidia |
| Parameters | 15.6B |
| Context window | Unknown |
| License | apache-2.0 — OSI-approved |
| Modality / task | text-generation |
| Gated on HuggingFace | No |
| Downloads | 77.7k |
| Likes | 36 |
| Last updated | 2025-09-10 |
| Source | nvidia/Qwen3-30B-A3B-NVFP4 |
What Qwen3-30B-A3B-NVFP4 is
A 15.6B-parameter auto-regressive transformer (MoE variant) quantized from BF16 to FP4 post-training. Context window: 131K tokens. Only linear operators within transformer blocks are quantized. Optimized for TensorRT-LLM inference on NVIDIA Blackwell GPUs (tested on B200). Calibrated on CNN/DailyMail dataset. Evaluated on MMLU Pro, GPQA Diamond, coding, and math benchmarks. Last updated Sept 10, 2025.
Run Qwen3-30B-A3B-NVFP4 locally
Load the open weights with 🤗 Transformers and generate — the same model, self-hosted.
from transformers import pipelinepipe = pipeline("text-generation", model="nvidia/Qwen3-30B-A3B-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
ESTIMATE: ~4–6 GB GPU VRAM for inference at FP4 precision (vs. ~15–20 GB for BF16 baseline). Requires NVIDIA Blackwell or compatible GPU with CUDA support. TensorRT-LLM inference engine mandatory. Linux preferred OS. B200 used for benchmarking; compatibility with other Blackwell SKUs (B100) likely but unconfirmed.
Not supported. Model card provides no guidance on LoRA/QLoRA with quantized weights. Fine-tuning would require dequantization and retraining, returning to BF16 baseline, then re-quantization. For custom adaptation, consider starting from base Qwen3-30B-A3B (non-quantized) or requesting quantized checkpoint of a fine-tuned variant.
When to avoid it — and what to weigh
- Strict accuracy requirements in reasoning-heavy tasks — FP4 quantization shows measurable accuracy loss on some benchmarks (e.g., HLE 0.07→0.05, SciCode 0.28→0.32). Verify against your domain-specific test set before production use.
- Non-NVIDIA infrastructure — Model is explicitly optimized for NVIDIA Blackwell and TensorRT-LLM. Deployment on CPU, AMD/Intel GPUs, or non-TensorRT runtimes (vLLM, TGI without TensorRT backend) will not meet performance targets and is unsupported.
- Real-time fine-tuning or rapid model updates — Quantized model is not designed for continued training. LoRA/QLoRA feasibility on FP4 weights is not documented. Retraining requires access to base BF16 model and recalibration.
- Multi-language or non-English specialized domains — Training and calibration dataset properties are undisclosed. No evaluation metrics for non-English performance, specialized domains, or safety/content filtering. Requires internal validation.
License & commercial use
Apache License 2.0. Permissive OSI-approved license allowing commercial and non-commercial use, modification, and distribution with attribution and liability waiver. No reciprocal obligations or patent clauses.
Clear for commercial use under Apache 2.0. Model card explicitly states 'ready for commercial/non-commercial use.' No gated access or additional restrictions. Underlying base model (Qwen3-30B-A3B) is Alibaba's; verify Alibaba's license terms for base model if derivative licensing is a concern. NVIDIA provides quantized artifact; attribution to both NVIDIA and Alibaba recommended but not legally mandated by Apache 2.0.
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 |
No explicit security audit, adversarial robustness testing, or prompt injection mitigations documented. Quantization itself is not a security mechanism; FP4 weights can be extracted and analyzed. Recommend internal red-team testing before deploying in user-facing applications. NVIDIA provides ethical guidelines and requests reporting of vulnerabilities via NVIDIA AI Concerns process, but specifics are opaque. Base model (Qwen3) inherits any upstream security posture.
Alternatives to consider
Qwen3-30B-A3B (base, BF16 precision)
Full precision, no quantization-induced accuracy loss. Supports broader serving frameworks (vLLM, TGI, ollama). 3.3x higher VRAM requirement. Start here if accuracy > inference cost.
Llama 3.1 70B quantized (various providers)
Larger model (70B), more generalist training. FP4/INT4 variants available. Broader community support and serving options. Potentially higher accuracy on reasoning tasks but higher memory footprint.
Mistral 7B or Mixtral 8x7B (quantized)
Smaller, faster, lower resource cost. Strong reasoning benchmarks. Broader framework support (llama.cpp, Ollama, vLLM). Trade-off: lower absolute performance on complex tasks compared to 30B model.
Ship Qwen3-30B-A3B-NVFP4 with senior software developers
NVIDIA's pre-quantized Qwen3-30B-A3B model is ready to deploy on your Blackwell GPUs today. Use TensorRT-LLM for fast, efficient inference. Ideal for cost-optimized chatbots, RAG pipelines, and multi-agent systems. Start with the sample code above, or contact our team to optimize for your infrastructure.
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Qwen3-30B-A3B-NVFP4 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 Qwen3-30B-A3B-NVFP4. 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.
Deploy a Production-Ready Quantized LLM in Minutes
NVIDIA's pre-quantized Qwen3-30B-A3B model is ready to deploy on your Blackwell GPUs today. Use TensorRT-LLM for fast, efficient inference. Ideal for cost-optimized chatbots, RAG pipelines, and multi-agent systems. Start with the sample code above, or contact our team to optimize for your infrastructure.