# Solar Open2 250B Nota NVFP4

URL: https://interfaze.ai/models/nota-aisolar-open2-250b-nota-nvfp4

Solar Open2 250B Nota NVFP4 by nota-ai, a text-generation model. Understand and compare features, benchmarks, and capabilities.

## Comparison

| Feature | Solar Open2 250B Nota NVFP4 | Interfaze |
| --- | --- | --- |
| Input Modalities | text | image, text, audio, video, document |
| Native OCR | No | Yes |
| Long Document Processing | No | Yes |
| Language Support | 3 partial | 162+ |
| Native Speech-to-Text | No | Yes |
| Native Object Detection | No | Yes |
| Guardrail Controls | No | Yes |
| Context Input Size | 1M | 1M |
| Tool Calling | Yes | Tool calling supported + built in browser, code execution and web search |

### Scaling

| Feature | Solar Open2 250B Nota NVFP4 | Interfaze |
| --- | --- | --- |
| Scaling | Self-hosted/Provider-hosted with quantization | Unlimited |

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View model card on [Hugging Face](https://huggingface.co/nota-ai/Solar-Open2-250B-Nota-NVFP4)

[Nota AI](https://www.nota.ai/) presents a 4-bit quantized release of [Upstage](https://www.upstage.ai/)'s [Solar Open2 250B](https://huggingface.co/upstage/Solar-Open2-250B), produced with Nota AI's proprietary quantization technology specialized for Mixture-of-Experts (MoE) large language models.

## Highlights

-   **NVFP4 (4-bit float, W4A4)** — `group_size=16`, packed in the `llm-compressor` (compressed-tensors) format for direct serving in [vLLM](https://github.com/vllm-project/vllm). Both weights and activations are quantized to 4-bit floating point.
    -   > **Requires NVIDIA Blackwell.** NVFP4 relies on the FP4 tensor cores introduced in the Blackwell architecture (e.g. B200 / GB200), so inference must run on a Blackwell-class GPU. Earlier architectures (Hopper, Ada, Ampere) do not support NVFP4 execution.
        
-   **Nota AI's proprietary MoE quantization framework.** This release is built upon a suite of techniques developed by Nota AI to preserve model quality under aggressive low-bit quantization of MoE architectures:
    -   A MoE-specialized calibration-dataset construction method, which achieved **1st place across all tracks** at the NVIDIA Nemotron Hackathon.
    -   [**DREAM-MoE**](https://openreview.net/pdf?id=Wyhqwjl51A) and [**SRA-MoE**](https://openreview.net/pdf?id=H0NoX02erJ), two quantization algorithms proposed by Nota AI (published at the _ICML 2026 Workshop on AdaptFM_), which preserve MoE routing decisions and align expert-routing behavior throughout the quantization process.

## License

Solar Open 2 is distributed under the [**Upstage Solar License**](https://huggingface.co/upstage/Solar-Open2-250B/blob/main/LICENSE).

**Key requirements for Derivative AI Models** (create / train / fine-tune / distill / improve using Solar Open 2):

-   **Naming:** prefix your model name with "Solar" (e.g., `Solar-MyModel-v1`).
    
-   **Attribution:** prominently display "Built with Solar" in related public-facing materials.
    
-   **Notice:** include a copy of the Upstage Solar License with your derivative model.
    

## Performance

### Weight footprint

| Precision | Weight footprint |
| --- | --- |
| BF16 | 500.6 GB |
| Nota NVFP4 | 153.3 GB |

### Benchmarks

| Benchmark | BF16 | Nota NVFP4 |
| --- | --- | --- |
| Tau2-Bench | 75.20 | 75.08 |
| HLE | 27.88 | 27.66 |
| GPQA Diamond | 86.26 | 85.45 |
| IFBench | 80.00 | 81.02 |
| LiveCodeBench (v5–v6) | 87.03 | 88.55 |
| MMLU-Pro | 86.19 | 86.15 |
| AIME 2026 (EN) | 95.67 | 96.67 |
| IFEval (EN) | 94.09 | 92.61 |
| HMMT | 92.05 | 90.15 |
| KMMLU-Pro | 78.38 | 77.93 |
| HAE-RAE Bench v1.1 | 73.84 | 72.84 |
| AIME (KO) | 97.67 | 97.00 |
| KBL | 75.51 | 75.40 |
| KBank-MMLU | 80.80 | 80.68 |
| KorMedMCQA | 92.99 | 93.05 |
| Avg. | 81.57 | 81.35 |

## Quick Start

This model is packed in the NVFP4 (compressed-tensors) format and can be served directly with vLLM on a **Blackwell-class GPU**:

```
uv venv --python 3.12 --seed solar_open2_venv
source .venv/bin/activate

VLLM_PRECOMPILED_WHEEL_LOCATION="https://github.com/vllm-project/vllm/releases/download/v0.22.0/vllm-0.22.0%2Bcu129-cp38-abi3-manylinux_2_28_x86_64.whl" \
VLLM_USE_PRECOMPILED=1 \
uv pip install --reinstall-package vllm --torch-backend=cu129 \
  "git+https://github.com/UpstageAI/vllm.git@v0.22.0-solar-open2"
```

```
vllm serve nota-ai/Solar-Open2-250B-Nota-NVFP4 \
  --served-model-name solar-open2-250b \
  --tensor-parallel-size 4 \
  --default-chat-template-kwargs '{"think_render_option":"preserved"}' \
  --reasoning-parser solar_open2 \
  --tool-call-parser solar_open2 \
  --enable-auto-tool-choice \
  --logits-processors vllm.v1.sample.logits_processor.solar_open2:SolarOpen2TemplateLogitsProcessor
```

-   Set `--tensor-parallel-size` according to the number of GPUs available in your serving environment.
    
-   See the original model card for the prompt format, parser configuration, and further details.
    

Send a chat completion request:

```
curl http://localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "solar-open2-250b",
    "messages": [
      {"role": "user", "content": "What is Upstage?"}
    ],
    "max_tokens": 131584,
    "temperature": 1.0,
    "top_p": 1.0,
    "reasoning_effort": "high"
  }'
```

## Citation

```
@inproceedings{park2026dreammoe,
  title     = {{DREAM-MoE}: Downstream Routing Error-Aware Margin-Preserving Quantization for Mixture-of-Experts Large Language Models},
  author    = {Park, Hancheol and Lee, Geonho and Kim, Tae-Ho},
  booktitle = {ICML 2026 Workshop on Resource-Adaptive Foundation Model Inference (AdaptFM)},
  year      = {2026},
  url       = {https://openreview.net/forum?id=Wyhqwjl51A},
}

@inproceedings{lee2026sramoe,
  title     = {{SRA-MoE}: Output-Aware Selective Router Alignment for MoE Quantization},
  author    = {Lee, Geonho and Park, Hancheol and Lee, Seunghyun and Choi, Jungwook and Kim, Tae-Ho},
  booktitle = {ICML 2026 Workshop on Resource-Adaptive Foundation Model Inference (AdaptFM)},
  year      = {2026},
  url       = {https://openreview.net/forum?id=H0NoX02erJ},
}
```

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