Ling-3.0-flash

Ling-3.0-flash is inclusionAI's next-generation native hybrid reasoning model. License on the card is MIT. The card says 124B total parameters and 5.1B active (~12.4% / ~8.1% of their previous 1T-class Ring-2.6-1T). Hugging Face listed 355 likes and 16,219 downloads in the last month on that date. createdAt is 2026-08-02.

r/LocalLLaMA treated Ling-3.0 as news because AntLing published the six-checkpoint base matrix (tiny and flash, each pretrained / mid-trained / WSM-merged). This page is the production flash instruct SKU, not every checkpoint.

Compare Qwen3.8-27B if you wanted a dense Apache 2.0 27B, and Ling-3.0-tiny if you wanted the 7.9B / 1.3B-active sibling.

Key Features

• Hybrid-linear MoE: 5:1 stacking of Kimi Delta Attention (KDA) and gated MLA, 1/64 sparse MoE. Card table: 35 KDA + 7 gated MLA layers, 512 routed experts, 8 activated, 1 shared, hidden size 2560, vocab 157,184.
• Context schedule: 8K to 32K to 256K training. SGLang cookbook recipe uses --context-length 262144.
• Thinking on by default: disable per request with "chat_template_kwargs": {"enable_thinking": false}. Recommended sampling on the flash card: temperature=0.6, top_p=0.95, top_k=20.
• Agent harnesses named on the card: Claude Code, Kilo Code, Qwen Code, Hermes Agent, OpenClaw. That is vendor copy, not our eval.
• Serving: SGLang image lmsysorg/sglang:dev-Ling-3.0-flash and a ling_3_0 vLLM fork at github.com/inclusionAI/vllm-ling-v3. OpenRouter lists inclusionai/ling-3.0-flash:free on the card; recheck that slug.

Limitation: you need the Ling-3.0 runtime, not a random vLLM wheel. The card's SWE-Bench numbers are vendor-measured with OpenHands. We did not rerun them.

Specs

Those scores are on the card. Treat them as vendor numbers.

Use Cases

• Agent coding loops where 5.1B active is cheaper than a dense 27B at similar context.
• People already on SGLang who can pull lmsysorg/sglang:dev-Ling-3.0-flash.
• LocalLLaMA readers who saw the six base checkpoints and wanted the flash instruct line.

If you only have a laptop, start with Ling-3.0-tiny on the sibling card (DGX Spark / M4 Pro numbers live there), not this 124B.

Getting Started

1. Open inclusionAI/Ling-3.0-flash.
2. Follow the SGLang cookbook.
3. docker pull lmsysorg/sglang:dev-Ling-3.0-flash and launch with --tp 4 on 141GB-class GPUs, or --tp 8 on 80GB cards, as the card says.
4. Measure on your own prompts before you quote SWE-Bench Pro.

Frequently Asked Questions

Is it dense?

No. Sparse MoE. 5.1B active per token.

Tiny vs flash?

Tiny is 7.9B / 1.3B active on Ling-3.0-tiny. Flash is the 124B production SKU.

Can I use stock vLLM?

The card installs inclusionAI/vllm-ling-v3 on branch ling_3_0.

Alternatives

• Qwen3.8-27B: dense Apache 2.0, simpler local deploy.
• DFlash 2: drafter for Qwen3.8-27B, not a chat model.
• Muse Spark 1.2: Meta coding flagship via API, not MIT weights.

Tips

1. Keep thinking on unless you have a reason to turn it off.
2. Confirm OpenRouter :free before you promise a $0 hosted seat.
3. Do not copy Ring-2.6-1T numbers onto this page.

Conclusion

Ling-3.0-flash is the MIT flash SKU LocalLLaMA was pointing at: 124B total, 5.1B active, 256K-class context, SGLang image on the card. Start at the Hugging Face card. If your GPU cannot hold it, open the tiny card instead.