Qwen3.5-122B-A10B-NVFP4
Qwen3.5-122B-A10B-NVFP4 is NVIDIA's quantized version of Alibaba's 122B mixture-of-experts language model. It reduces model size and GPU memory by ~4x using NVFP4 (4-bit) quantization while maintaining near-baseline accuracy. The model handles text, image, and video inputs with a 262K context window. It is Apache 2.0 licensed, gated-free, and optimized for vLLM inference on NVIDIA GPUs. Primary use cases include AI agents, chatbots, RAG systems, and conversational applications.
Key facts
Objective fields from the source. Values we can't verify are shown as “Unknown” rather than guessed.
| Field | Value |
|---|---|
| Developer | nvidia |
| Parameters | 64.6B |
| Context window | Unknown |
| License | apache-2.0 — OSI-approved |
| Modality / task | text-generation |
| Gated on HuggingFace | No |
| Downloads | 89.4k |
| Likes | 33 |
| Last updated | 2026-06-02 |
| Source | nvidia/Qwen3.5-122B-A10B-NVFP4 |
What Qwen3.5-122B-A10B-NVFP4 is
A post-training quantized derivative of Qwen3.5-122B-A10B using NVIDIA Model Optimizer v0.44.0. Quantizes weights and activations of linear operators in transformer blocks to NVFP4 (4-bit floating point), reducing parameters from 16-bit to 4-bit representation. Activates 10B of 122B parameters during inference (MoE architecture). Calibrated on CNN DailyMail and Nemotron-Post-Training-Dataset-v2. Evaluated on MMMU Pro, GPQA Diamond, SciCode, AA-LCR, and IFBench with minimal accuracy loss versus FP8 baseline (e.g., GPQA Diamond 87.37% → 86.77%). Requires vLLM for inference, tested on NVIDIA B200 GPU. Context length stated as up to 262K; official model card references this property.
Run Qwen3.5-122B-A10B-NVFP4 locally
Load the open weights with 🤗 Transformers and generate — the same model, self-hosted.
from transformers import pipelinepipe = pipeline("text-generation", model="nvidia/Qwen3.5-122B-A10B-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: ~30–31 GB GPU VRAM (122B parameters at 4-bit ≈ 61 GB unquantized; 4x reduction ≈ 15–16 GB model weights + 10–15 GB KV cache and activations depending on context length and batch size). Tested on NVIDIA B200 (141 GB total VRAM). Suitable for A100 (80 GB) with careful batching; H100 (80 GB) recommended for production. Requires CUDA 12.x support and vLLM compatible PyTorch. Operating system: Linux preferred per card.
Not clearly stated in model card. NVFP4 quantization targets inference; fine-tuning feasibility unknown. QLoRA compatibility with 4-bit quantized weights is plausible but unsupported and untested. Recommend: (1) Fine-tune on unquantized Qwen3.5-122B-A10B base model if task-specific adaptation is critical, then quantize with Model Optimizer. (2) Test LoRA adapters with quantized weights only after internal validation. (3) Refer to NVIDIA Model Optimizer documentation for quantization-aware training guidance.
When to avoid it — and what to weigh
- Extreme Low-Latency Inference (<50ms) — MoE routing and vLLM-specific overhead may not meet hard real-time SLAs. Test end-to-end latency on target hardware; quantization does not guarantee speed without proper kernel optimization.
- Specialized Domain Fine-Tuning without Validation — Base model exhibits known biases and toxic language from training data. Requires domain-specific evaluation and mitigation before deployment in sensitive industries (healthcare, legal, finance). NVIDIA recommends use-case-specific testing per V-model methodology.
- Systems Without NVIDIA GPU Infrastructure — Model is optimized exclusively for NVIDIA GPU-accelerated systems (Blackwell confirmed; vLLM primary runtime). CPU-only or non-NVIDIA GPU deployment is not documented or supported.
- Applications Requiring Guaranteed Factual Accuracy — Model card explicitly warns of potential inaccuracy, key information omission, and irrelevant/redundant text generation. Not suitable for high-stakes factuality without external validation (e.g., RAG with trusted corpora).
License & commercial use
Apache License 2.0. Standard permissive open-source license allowing modification, distribution, and private use without restriction. Model card explicitly states: 'This model is ready for commercial/non-commercial use.' No proprietary restrictions on the quantized artifact itself.
Apache 2.0 is an OSI-approved permissive license. Commercial use is explicitly permitted in the model card ('ready for commercial/non-commercial use'). No gating or commercial license required. However, responsibility for compliance with downstream data privacy, IP, and regulatory requirements (HIPAA, GDPR, etc.) remains with the deployer. NVIDIA disclaims liability and recommends internal review for industry-specific legal and ethical standards. Derivative works must retain Apache 2.0 attribution.
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 |
Model card does not claim or assess security posture. Key considerations: (1) Quantized weights and activations may introduce side-channel attack surface if deployed in shared GPU environments; vLLM isolation is model-responsibility, not kernel-guaranteed. (2) Model trained on internet-crawled data; may encode backdoors, adversarial patterns, or prompt injection vulnerabilities. (3) Multimodal inputs (image, video) expand attack surface; model card warns to 'ensure proper rights and permissions' but does not detail content filtering. (4) Activates only 10B of 122B parameters (MoE); routing logic security is not documented. (5) No security audit, formal verification, or penetration testing results provided. NVIDIA maintains a vulnerability reporting channel (app.intigriti.com/programs/nvidia); use it for discovered issues. Standard LLM safety practices apply: input validation, output filtering, rate limiting.
Alternatives to consider
Qwen/Qwen3.5-122B-A10B-FP8
Unquantized Alibaba baseline with higher accuracy (GPQA 87.37% vs. 86.77%) but ~2x larger model size (~122B FP8 ≈ 244 GB). Choose if accuracy is critical and VRAM is abundant; otherwise NVFP4 offers better efficiency trade-off.
Llama 3.1 405B (Meta)
Denser 405B model; more parameters than Qwen3.5-122B-A10B but larger VRAM footprint. Strong on benchmarks; different training corpus and evaluation methodology. License (LLAMA 2 Community): requires review for commercial use beyond Meta's allowances. Choose if native English instruction-following is priority; Qwen excels on code and tool use.
Mixtral 8x22B (Mistral)
Smaller open-weight MoE model (~141B total, ~39B active); lower VRAM requirement. Apache 2.0 license (clear commercial use). Weaker on benchmarks (GPQA, code) than Qwen3.5; suitable for cost-optimized RAG and general chat. No official NVFP4 quantization; use GPTQ or AWQ instead.
Ship Qwen3.5-122B-A10B-NVFP4 with senior software developers
Qwen3.5-122B-A10B-NVFP4 offers efficient inference on NVIDIA GPUs with minimal accuracy trade-off. Test it in your environment with vLLM, validate on your benchmark tasks, and contact our team to architect a robust RAG or AI agent deployment. Learn more about quantization strategies and production best practices.
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Qwen3.5-122B-A10B-NVFP4 FAQ
Can I use this model commercially without additional licensing or NVIDIA approval?
What GPU hardware do I need to run this model?
How much accuracy loss is there due to NVFP4 quantization compared to the base FP8 model?
Does this model support fine-tuning or LoRA adapters?
Software development & web development with DEV.co
Adopting Qwen3.5-122B-A10B-NVFP4 is usually one piece of a larger software development effort. As a software development agency, DEV.co provides software development services and web development expertise — pairing senior software developers and web developers with your team to design, build, and operate open-source llms software in production.
Ready to deploy a quantized LLM at scale?
Qwen3.5-122B-A10B-NVFP4 offers efficient inference on NVIDIA GPUs with minimal accuracy trade-off. Test it in your environment with vLLM, validate on your benchmark tasks, and contact our team to architect a robust RAG or AI agent deployment. Learn more about quantization strategies and production best practices.