Ling-3.0-flash-VL

Ling-3.0-flash-VL is inclusionAI's native multimodal SKU on the Ling-3.0-flash stack. The Hugging Face card lists license MIT. The card says 124B total parameters and 5.5B activated per token, with image and video inputs and a context window up to 1M tokens. Hugging Face showed 31 likes and 42 downloads in the last month on 2026-09-09. createdAt is 2026-09-04. r/LocalLLaMA had the card on the hot list.

This is not Ling-3.0-flash. Flash is the text hybrid-reasoning SKU (card: 5.1B active). Flash-VL adds a ViT encoder, a two-layer MLP projector, and VideoRoPE.

Compare Ling-3.0-flash if you wanted text-only, Qwen3.8-Flash-Next if you wanted Alibaba's sparse open MoE, or DeepSeek-V4-Flash-Vision-Exp if you wanted a screenshot-and-text Flash VLM.

Key Features

• Vision in the loop: card frames understand / reason / act: charts and documents, visual evidence for multi-step work, then UI actions.
• Hybrid backbone: 42-layer stack alternating KDA and gated MLA at 5:1. Sparse MoE keeps 124B capacity with 5.5B active.
• VideoRoPE: spatial plus temporal encodings for event localization, long-video QA, and clip editing claims on the card.
• Thinking on by default: disable per request with "chat_template_kwargs": {"enable_thinking": false}. Card sampling: temperature=0.6 (eval note also cites 1.0), top_p=0.95, top_k=20.
• Serving: SGLang image lmsysorg/sglang:dev-Ling-3.0-flash-VL with a 256K YaRN recipe on 4x 141GB-class GPUs. vLLM path is the inclusionAI/vllm-ling-v3 fork, not a stock wheel.

Limitation: you need the Ling-3.0 VL runtime. The Artificial Analysis Intelligence Index v4.1.1 score of 42 (flash was 38) is vendor-cited. We did not rerun it. 31 likes is a heat signal, not an audit.

Specs

Use Cases

• Teams already on Ling-3.0-flash who need image and video on the same MoE family.
• Long-context multimodal work that wants 256K-class recipes without switching vendors.
• r/LocalLLaMA readers who saw the HF link and needed the VL SKU, not tiny or text flash.

If you needed a 7.9B / 1.3B-active local text model, use Ling-3.0-tiny.

Getting Started

1. Open inclusionAI/Ling-3.0-flash-VL.
2. docker pull lmsysorg/sglang:dev-Ling-3.0-flash-VL.
3. Launch with --tp 4 --context-length 262144 and the YaRN override on the card. On 80GB cards, card says --tp 8.
4. Send image_url or video_url parts in the OpenAI-compatible chat body.

First-party resource: SGLang cookbook for Ling-3.0-flash-VL.

Frequently Asked Questions

Is this Apache-2.0?

No. The card is MIT.

Same active params as Ling-3.0-flash?

No. Flash card says 5.1B active. Flash-VL card says 5.5B active.

Will stock vLLM load it?

Not as a random wheel. Card points at inclusionAI/vllm-ling-v3.

Alternatives

• Ling-3.0-flash: text hybrid-reasoning SKU.
• Qwen3.8-Flash-Next: Alibaba sparse open MoE.
• DeepSeek-V4-Flash-Vision-Exp: MIT Flash vision experiment.

Tips

1. Use the VL Docker tag, not the text-flash image.
2. Keep enable_thinking off only when you want a short caption, not agent traces.
3. Do not copy flash SWE numbers onto this VL page.

Ling-3.0-flash-VL is the MIT multimodal drop r/LocalLLaMA linked in September 2026. Start at the Hugging Face card, then decide whether text flash already covered the job.