Leaderboard / InclusionAI (Ling)

Ring-mini-2.0

InclusionAI (Ling) · Ring 2.0 · released 2025-09-19 · data checked 2026-11-01 Needs verification

Ring-mini-2.0 is a 16.3B-parameter Mixture-of-Experts model (1.4B active per token) from InclusionAI (Ling) (China), released 2025-09-19 under the MIT license. It doesn't have enough reported benchmarks for an index score yet. It needs about 11 GB of memory at 4-bit with an 8K-token context.

Small LM Index
—
Needs 2 core benchmarks (has 0)
Parameters
16.3B
1.4B active · MoE
Price per 1M tokens
—
No hosted API on OpenRouter; self-host
Context window
128K
tokens

Benchmarks

Each bar shows this model's score. The tick marks the best medium model on that benchmark.

No benchmark results for this release yet. Know of one? Send it.

Specifications

Lab
InclusionAI (Ling) (CN · China)
Architecture
Mixture-of-Experts
Total parameters
16.3B
Active parameters
1.4B
Size class
Medium
Context window
128K tokens
Input modalities
text
Reasoning
Reasoning (thinking)
License
MIT
Openness
Open weights
Memory to run
~11 GB at 4-bit, 8K context (more sizes)
Hugging Face
1.2K downloads (30 days) · 188 likes

Compare with

Sources

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Run it locally

Uses inclusionAI/Ring-mini-2.0-GGUF (Q4_K_M). Swap the tag for another size: BF16, Q2_K, Q4_K_M, Q6_K, Q8_0.

Ollama
ollama run hf.co/inclusionAI/Ring-mini-2.0-GGUF:Q4_K_M
llama.cpp
llama-server -hf inclusionAI/Ring-mini-2.0-GGUF:Q4_K_M
Then open http://localhost:8080 for a chat window.
MLX (Mac)
pip install -U mlx-lm
mlx_lm.chat --model mlx-community/Ring-mini-2.0-4bit
vLLM (GPU server)
vllm serve inclusionAI/Ring-mini-2.0
Full-precision weights, for a server GPU.

Open in LM Studio

Memory needed: weights, context cache and runtime buffers.

Quantization4K context32K context128K context
16-bit F1634 GB35 GB39 GB
8-bit Q8_018 GB19 GB23 GB
4-bit Q4_K_M11 GB12 GB16 GB

Check it against your GPU, Mac or phone →