Model ID
inclusionAI/Ling-3.1-flash
Capabilities
Ling 3.1 Flash
Ling 3.1 Flash is a native hybrid reasoning Mixture-of-Experts model designed for general-purpose agents, office productivity, and software engineering. Building on Ling 3.0 Flash, it scales to 560B total parameters with 25B active, using a 72-layer architecture with 4,096 hidden size, 64 attention heads, and 64 KV heads. Kimi Delta Attention and gated MLA layers alternate in a 7:1 ratio, while MoE layers route each token to 8 of 512 experts plus a shared expert. Supporting a 1M-token context, it adds a Multi-Token Prediction layer for speculative decoding.
About this model
Ling 3.1 Flash is a native hybrid reasoning Mixture-of-Experts model designed for general-purpose agents, office productivity, and software engineering. Building on Ling 3.0 Flash, it scales to 560B total parameters with 25B active, using a 72-layer architecture with 4,096 hidden size, 64 attention heads, and 64 KV heads. Kimi Delta Attention and gated MLA layers alternate in a 7:1 ratio, while MoE layers route each token to 8 of 512 experts plus a shared expert. Supporting a 1M-token context, it adds a Multi-Token Prediction layer for speculative decoding.
Model specifications and capabilities are published by the model author and reproduced here from HF Model Card (inclusionAI/Ling-3.1-flash). Released under MIT.
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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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