Model ID
inclusionAI/Ling-3.0-flash-Fin
Capabilities
Ling 3.0 Flash Fin
Ling 3.0 Flash Fin is a finance-enhanced Mixture-of-Experts model designed for financial research, valuation, spreadsheet workflows, source-grounded analysis, and long-horizon agentic tasks. It features 124B total parameters with 5.1B activated per token, using a 42-layer architecture with 2,560 hidden size, 32 attention heads, and 32 KV heads. The architecture employs a hybrid KDA and attention design, 512 routed experts with 8 activated per token, and 1 shared expert, alongside a native Multi-Token Prediction layer. Supporting a 256K-token context window, it is optimized for multi-document financial reasoning, tool-intensive workflows, and professional research outputs.
About this model
Ling 3.0 Flash Fin is a finance-enhanced Mixture-of-Experts model designed for financial research, valuation, spreadsheet workflows, source-grounded analysis, and long-horizon agentic tasks. It features 124B total parameters with 5.1B activated per token, using a 42-layer architecture with 2,560 hidden size, 32 attention heads, and 32 KV heads. The architecture employs a hybrid KDA and attention design, 512 routed experts with 8 activated per token, and 1 shared expert, alongside a native Multi-Token Prediction layer. Supporting a 256K-token context window, it is optimized for multi-document financial reasoning, tool-intensive workflows, and professional research outputs.
Model specifications and capabilities are published by the model author and reproduced here from HF Model Card (inclusionAI/Ling-3.0-flash-Fin). 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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