Model ID
inclusionAI/Ring-2.6-1T
Capabilities
Ring 2.6 1T
Ring-2.6-1T is a trillion-parameter Mixture-of-Experts reasoning model designed for advanced agentic workflows, complex reasoning, and long-horizon task execution. It features an 80-layer architecture with 8,192 hidden size and 64 attention heads, activating 8 experts per token across 256 routed experts and a shared expert. The model incorporates hybrid attention, QK normalization, Multi-Token Prediction, and YaRN context extension for efficient long-context inference. Supporting up to a 256K token context window, it is optimized for enterprise automation, coding, scientific analysis, and multi-step tool-integrated reasoning.
About this model
Ring-2.6-1T is a trillion-parameter Mixture-of-Experts reasoning model designed for advanced agentic workflows, complex reasoning, and long-horizon task execution. It features an 80-layer architecture with 8,192 hidden size and 64 attention heads, activating 8 experts per token across 256 routed experts and a shared expert. The model incorporates hybrid attention, QK normalization, Multi-Token Prediction, and YaRN context extension for efficient long-context inference. Supporting up to a 256K token context window, it is optimized for enterprise automation, coding, scientific analysis, and multi-step tool-integrated reasoning.
Model specifications and capabilities are published by the model author and reproduced here from HF Model Card (inclusionAI/Ring-2.6-1T). Released under MIT. Further reading: Paper
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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 · vLLM. 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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