Model ID
nvidia/Qwen3-Nemotron-235B-A22B-GenRM
Capabilities
Qwen3 Nemotron 235B A22B GenRM
Qwen3 Nemotron 235B A22B GenRM is a large-scale Generative Reward Model (GenRM) built on the Qwen3-235B-A22B foundation and fine-tuned to evaluate assistant responses for helpfulness and quality. Based on a 235B-parameter Mixture-of-Experts (MoE) transformer, it features 94 layers, 64 attention heads, 128 experts (8 experts per token), and a 4,096 hidden size. The model processes up to 131K tokens and outputs structured helpfulness and ranking scores for candidate responses. It is designed for reinforcement learning from human feedback (RLHF), large-scale preference modeling, and advanced AI alignment workflows.
About this model
Qwen3 Nemotron 235B A22B GenRM is a large-scale Generative Reward Model (GenRM) built on the Qwen3-235B-A22B foundation and fine-tuned to evaluate assistant responses for helpfulness and quality. Based on a 235B-parameter Mixture-of-Experts (MoE) transformer, it features 94 layers, 64 attention heads, 128 experts (8 experts per token), and a 4,096 hidden size. The model processes up to 131K tokens and outputs structured helpfulness and ranking scores for candidate responses. It is designed for reinforcement learning from human feedback (RLHF), large-scale preference modeling, and advanced AI alignment workflows.
Model specifications and capabilities are published by the model author and reproduced here from HF Model Card (nvidia/Qwen3-Nemotron-235B-A22B-GenRM). Released under nvidia-open-model-license. Further reading: Paper · 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 · 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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