Ling-3.0-tiny: pricing, features, alternatives

Ling-3.0-tiny is inclusionAI's lightweight hybrid-reasoning MoE. License on the Hugging Face card is MIT. The card says 7.9B total parameters and 1.3B activated per token. Hugging Face listed 431 likes and 27,485 downloads in the last month on 2026-09-08. createdAt is 2026-08-10. r/LocalLLaMA was comparing it with Spark-X2.5-4B and Nanbeige4.2-3B in the same small-model thread.

Compare Ling-3.0-flash if you wanted the 124B / 5.1B-active production SKU, Spark-X2.5-4B if you wanted a dense Apache 2.0 4B, or Qwen3.8-27B if you wanted a larger dense local model.

Key Features

Efficient hybrid-linear MoE: 3:1 stacking of Kimi Delta Attention (KDA) and MLA (3 KDA then 1 MLA per 4-layer block). Sparse MoE FFN with 128 routed experts. Each token activates 8 routed experts plus 1 shared expert.
Native hybrid reasoning: thinking is on by default. Disable per request with "chat_template_kwargs": {"enable_thinking": false}. Recommended sampling on the tiny card: temperature=1.0, top_p=0.95, top_k=20.
Local and edge numbers on the card: validated on NVIDIA DGX Spark, Apple Silicon MacBook, and Mac mini. With FP8 the card claims about 100-105 tok/s on DGX Spark and 86-90 tok/s on an M4 Pro, with about 8.34 GiB peak memory at 8K context. Treat those as vendor measurements.
Serving: SGLang image lmsysorg/sglang:dev-Ling-3.0-tiny, a vLLM install path on the card, and an experimental Ollama MLX path via ollama/ollama#17643. OpenRouter lists inclusionai/ling-3.0-tiny:free on the card; recheck that slug.
Context: SGLang cookbook recipe uses --context-length 262144 with YaRN.

Limitation: you need the Ling-3.0 runtime, not a random vLLM wheel. Ollama support is a PR, not the official release. Card scores (Artificial Analysis Intelligence Index v4.1.1 25, Agentic Index 16) are vendor-cited. We did not rerun them.

Specs

Item	Value	Source
Total / active	7.9B / 1.3B	HF card
License	MIT	same
Likes / last-month downloads	431 / 27,485	HF API, 2026-09-08
Software price	$0 weights	MIT

Use Cases

Laptop and DGX Spark users who cannot hold Ling-3.0-flash.
Agent loops that want thinking plus tool parsers (ling3) without a 124B checkpoint.
LocalLLaMA readers who saw the tiny vs flash matrix and wanted the 1.3B-active instruct line.

If you have 4x 141GB-class GPUs, start with flash, not this page.

Getting Started

Open inclusionAI/Ling-3.0-tiny.
Follow the SGLang cookbook.
docker pull lmsysorg/sglang:dev-Ling-3.0-tiny and launch with --tp 1 as the card says.
Measure on your own prompts before you quote the AA index.

First-party resource: Ling-3.0-tiny model card.

Frequently Asked Questions

Is it dense?

No. Sparse MoE. 1.3B active per token.

Tiny vs flash?

Tiny is 7.9B / 1.3B active. Ling-3.0-flash is 124B / 5.1B active.

Can I use stock Ollama?

Not the official binary. The card builds from PR 17643 and MLX on Apple Silicon only.

Alternatives

Ling-3.0-flash: the 124B production SKU.
Spark-X2.5-4B: dense Apache 2.0 4B with native 1M context.
Qwen3.8-27B: denser Apache 2.0, simpler local deploy.

Tips

Keep thinking on unless you have a reason to turn it off. Tiny's recommended temperature is 1.0, not flash's 0.6.
Confirm OpenRouter :free before you promise a $0 hosted seat.
Do not copy flash SWE-Bench numbers onto this page.

Conclusion

Ling-3.0-tiny is the MIT tiny SKU LocalLLaMA was pointing at: 7.9B total, 1.3B active, 256K-class context, SGLang image on the card. Start at the Hugging Face card. If your GPU can hold flash, open Ling-3.0-flash instead.