Qwen3-30B-A3B-NVFP4
Qwen3-30B-A3B-NVFP4 is a quantized version of Qwen's 30-billion-parameter mixture-of-experts model, compressed to FP4 precision for weights and activations. It reduces memory footprint by ~75% compared to the base model while retaining 92–98% accuracy across standard benchmarks. Designed for vLLM inference, it supports multiple languages and is available under Apache 2.0 license.
Key facts
Objective fields from the source. Values we can't verify are shown as “Unknown” rather than guessed.
| Field | Value |
|---|---|
| Developer | RedHatAI |
| Parameters | 17.5B |
| Context window | Unknown |
| License | apache-2.0 — OSI-approved |
| Modality / task | text-generation |
| Gated on HuggingFace | No |
| Downloads | 34.5k |
| Likes | 2 |
| Last updated | 2025-12-04 |
| Source | RedHatAI/Qwen3-30B-A3B-NVFP4 |
What Qwen3-30B-A3B-NVFP4 is
This is a post-training quantized derivative of Qwen/Qwen3-30B-A3B created via LLM Compressor with FP4 weight and activation quantization applied to linear layers in transformer blocks. Quantization was calibrated on 512 UltraChat samples with per-group-16 strategy and symmetric quantization. The model is distributed in compressed-tensors format optimized for vLLM >=0.9.1. Evaluation shows recovery rates of 92.8–98.2% on OpenLLM v1/v2 and HumanEval benchmarks relative to the unquantized base model.
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="RedHatAI/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: ~8–12 GB VRAM for single-GPU serving (FP4 with ~17.5B parameters ≈ 8.75 GB weights + activation buffer; test with target hardware). Baseline: NVIDIA A10 (24GB) or RTX 4090 (24GB) sufficient. Multi-GPU tensor parallelism supported (eval used tensor_parallel_size=2). Requires vLLM >=0.9.1 and CUDA-capable GPU; CPU-only inference not mentioned.
Model card provides no guidance on fine-tuning or adaptation. FP4 quantization severely constrains gradient backpropagation; standard LoRA may degrade convergence. Options: (1) dequantize to FP16/BF16 before fine-tuning (memory overhead defeats quantization benefit); (2) use base Qwen/Qwen3-30B-A3B and quantize post-training; (3) parameter-efficient methods (e.g., prefix tuning) on frozen quantized weights untested. Requires experimentation.
When to avoid it — and what to weigh
- Strict accuracy requirements on reasoning tasks — OpenLLM v2 shows larger accuracy drops on complex reasoning (BBH, MMLU-Pro, Math benchmarks: 83–92% recovery vs. 97–98% on standard tasks); use base model if <5% performance loss is unacceptable.
- Non-English or low-resource language workloads — Model card explicitly marks use outside English as out-of-scope; multilingual support is stated but not evaluated; may degrade unexpectedly on minority languages.
- Fine-tuning or continued pretraining — FP4 quantization fundamentally limits gradient precision; no LoRA/QLoRA guidance provided; retraining on new data likely requires dequantization or base model use.
- Real-time latency-critical systems requiring extreme throughput — While vLLM-optimized, FP4 inference still requires GPU; no inference-time latency benchmarks provided; CPU or edge-device deployment unsuitable.
License & commercial use
Apache License 2.0 (OSI-approved permissive license). Permits unrestricted commercial and private use, modification, and distribution with minimal conditions (retain license notice and liability disclaimer). No export control or attribution clauses beyond Apache terms.
Commercial use is clearly permitted under Apache 2.0. However, model card includes out-of-scope restrictions: use must not violate applicable laws/regulations (including trade compliance) and is explicitly out-of-scope for non-English languages. For regulated industries (finance, healthcare, government) or export-controlled jurisdictions, review compliance requirements independently. Base model origin (Qwen/Qwen3-30B-A3B by Alibaba) may have upstream export restrictions; verify with legal counsel if targeting sanctioned regions.
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 | Moderate |
| DEV.co fit | Strong |
| Assessment confidence | High |
FP4 quantization does not inherently alter model outputs or reduce safety properties; relies on base Qwen model's training. Quantization artifacts (rounding errors) in weights/activations are minor (98% recovery on tasks). No security audit, adversarial robustness, or prompt-injection testing reported. Like all LLMs, model may generate false information, biased outputs, or harmful content; no built-in safety layer mentioned. For sensitive domains, conduct adversarial testing and implement guardrails independently. Deployment via vLLM in isolated/gated environments recommended for untrusted input.
Alternatives to consider
Qwen/Qwen3-30B-A3B (base, unquantized)
Full precision; no accuracy loss; preferred if VRAM budget allows (≈60–90GB for FP16/BF16). Better for fine-tuning and production systems where quality cannot be compromised.
Meta-Llama-3.1-70B-Instruct (or Llama 3 quantized variants)
Larger, more established model with stronger community support and broader quantization ecosystem (GGLM, GGUF, AWQ). Llama license (non-commercial restrictions for base; may require review) but better-documented fine-tuning paths.
Mistral-7B or Mistral-Nemo (small quantized models)
Smaller footprint, proven vLLM support, and extensive quantization options (GGUF, int4) if memory is primary constraint. Trade-off: lower capability for reasoning/coding tasks but easier to deploy on edge/CPU.
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Explore how Qwen3-30B-A3B-NVFP4 can cut your inference costs while maintaining high accuracy. Review the model card, test on your hardware, and integrate vLLM for production serving.