Ling-3.0-tiny

About

What Ling-3.0-tiny is

Introduction We are introducing Ling-3.0-tiny, a lightweight hybrid reasoning MoE model with 7.9B total parameters and only 1.3B activated parameters per token. It is designed to deliver strong reasoning and agentic capabilities at low inference cost, making advanced model capabilities more accessible for local and resource-constrained deployment. BF16, FP8, and INT4 weights are provided for a wide range of hardware and deployment settings. Key highlights of the model are summarized below: + Efficient Hybrid-Linear Architecture: Ling-3.0-tiny integrates a 3:1 alternating stacking of KDA and MLA (3 Kimi Delta Attention layers followed by 1 Multi-Head Latent Attention layer per 4-layer block) with a sparse MoE FFN comprising 128 routed experts. Each token activates only 8 routed experts and 1 shared expert, allowing the model to balance long-context modeling capability, parameter efficiency, and computational cost. + Native Hybrid Reasoning and Agentic Capabilities: Ling-3.0-tiny supports both fast responses and multi-step reasoning, with thinking mode configurable per request through enablethinking. It delivers balanced performance across general agent tasks, coding, mathematical and scientific reasoning, and instruction following. + Local and Edge Deployment: Designed for efficient local deployment, Ling-3.0-tiny has been validated on NVIDIA DGX Spark, Apple Silicon MacBook, and Mac mini, enabling capable reasoning and agentic workloads without datacenter-class GPUs. With FP8, Ling-3.0-tiny reaches around 100-105 tokens/s on DGX Spark and 86-90 tokens/s on an M4 Pro MacBook, with approximately 8.34 GiB peak memory usage at an 8K context length. Model Overview Ling-3.0-tiny inherits the hybrid linear attation architecture of Ling-3.0 series, while being specifically optimized for lightweight and accessible deployment. The model has 7.9B total parameters, with only 1.3B parameters activated per token. The architecture of Ling-3.0-tiny is designed to make computational efficiency serve real-world agentic performance. + A 3:1 KDA–MLA architecture (3 KDA layers and 1 MLA layer per 4-layer block) provides more efficient long-context processing; + A sparse MoE FFN with

Summarised from the published model card. Read the full card on the HuggingFace links below.

Specifications

What it is

How it works

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Variants

Sizes & precisions

Open weights ship in several sizes and precisions. One page, all the variants — pick the one that fits your GPU. VRAM figures are estimates from model size.

Using it via the API

Call it like any OpenAI endpoint

Once AxForge deploys ling-3-0-tiny for you, it answers on the OpenAI-compatible API — the same base URL and keys as every other model. (ling-3-0-tiny below is illustrative; you get the exact model name on deployment.)

$ curl -sS https://api.axforge.ai/v1/chat/completions \
  -H "Authorization: Bearer $AXFORGE_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"model":"ling-3-0-tiny","messages":[{"role":"user","content":"Hello"}]}'

Details

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Licence

Open weights

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Weights & code

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