Model ID
inclusionAI/Ling-3.0-tiny
Capabilities
Ling 3.0 Tiny
Ling 3.0 Tiny is a lightweight hybrid-linear Mixture-of-Experts model designed for efficient reasoning, coding, and agentic workflows on local and resource-constrained hardware. It features 7.9B total parameters with 1.3B activated, using a 24-layer architecture with a 1,536 hidden size and 16 attention heads. The model combines Kimi Delta Attention (KDA) and Multi-Head Latent Attention (MLA) in a 3:1 ratio, with 128 routed experts and 1 shared expert, activating 8 routed experts per token. Supporting a 131K-token context window, it is optimized for low-cost deployment while providing hybrid reasoning and efficient long-context processing for local and edge environments.
About this model
Ling 3.0 Tiny is a lightweight hybrid-linear Mixture-of-Experts model designed for efficient reasoning, coding, and agentic workflows on local and resource-constrained hardware. It features 7.9B total parameters with 1.3B activated, using a 24-layer architecture with a 1,536 hidden size and 16 attention heads. The model combines Kimi Delta Attention (KDA) and Multi-Head Latent Attention (MLA) in a 3:1 ratio, with 128 routed experts and 1 shared expert, activating 8 routed experts per token. Supporting a 131K-token context window, it is optimized for low-cost deployment while providing hybrid reasoning and efficient long-context processing for local and edge environments.
Model specifications and capabilities are published by the model author and reproduced here from HF Model Card (inclusionAI/Ling-3.0-tiny). Released under MIT. Further reading: Blog
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Hardware
B200
1,440 GB
Engine
vLLM
SGLang
Deployment code
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Performance under load
Tokens/s (total)
Benchmarks measured by Vultr on B200 · SGLang. Throughput and latency vary with concurrency, input length and engine configuration, so treat these as a comparison baseline, not a service guarantee.
NVIDIA HGX B200
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