Qwen2-72B-Instruct-FP8
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Qwen2-7B-Instruct-FP8
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View details ↗Qwen3-0.6B-FP8-BLOCK
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View details ↗Qwen3-0.6B-FP8-dynamic
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View details ↗Qwen3-0.6B-quantized.w4a16
RedHatAIThis model was obtained by quantizing the weights of Qwen3-0.6B to INT4 data type. This optimization reduces the number of bits per parameter from 16 to 4, reducing the disk size and GPU memory requirements by approximately 75%. Only the weights of the linear operators within transformers blocks are quantized. Weights are quantized using a asymmetric per-group scheme, with group size 64. The GPTQ algorithm is applied for quantization, as implemented in the llm-compressor library. The model was evaluated on the OpenLLM leaderboard tasks (versions 1 and 2), using lm-evaluation-harness, and on reasoning tasks using lighteval. vLLM was used for all evaluations.
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View details ↗Qwen3-1.7B-FP8-dynamic
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View details ↗Qwen3-1.7B-quantized.w4a16
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View details ↗Qwen3-14B-FP8-dynamic
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View details ↗Qwen3-14B-NVFP4
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View details ↗Qwen3-14B-speculator.eagle3
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View details ↗Qwen3-30B-A3B-FP8-block
RedHatAIThis model was obtained by quantizing the weights and activations of Qwen/Qwen3-30B-A3B to FP8 data type. This optimization reduces the number of bits per parameter from 16 to 8, reducing the disk size and GPU memory requirements by approximately 50%. Only the weights and activations of the linear operators within transformers blocks of the language model are quantized. The model was evaluated on the OpenLLM leaderboard task, using lm-evaluation-harness. vLLM was used for all evaluations.
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View details ↗Qwen3-30B-A3B-FP8-dynamic
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View details ↗- **Model Architecture:** Qwen3ForCausalLM - **Input:** Text - **Output:** Text - **Model Optimizations:** - **Weight quantization:** INT4 - **Intended Use Cases:** - Reasoning. - Function calling. - Subject matter experts via fine-tuning. - Multilingual instruction following. - Translation. - **Out-of-scope:** Use in any manner that violates applicable laws or regulations (including trade compliance laws). - **Release Date:** 05/05/2025 - **Version:** 1.0 - **Model Developers:** RedHat (Neural Magic) This model was obtained by quantizing the weights of Qwen/Qwen3-30B-A3B-Instruct-2507 to INT4 data type. This optimization reduces the number of bits per parameter from 16 to 4, reducing the disk size and GPU memory requirements by approximately 75%. Only the weights of the linear operators within transformers blocks are quantized. Weights are quantized using a symmetric per-group scheme, with group size 128. The GPTQ algorithm is applied for quantization, as implemented in the llm-compressor library.
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View details ↗Qwen3-30B-A3B-NVFP4
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View details ↗Qwen3-30B-A3B-quantized.w4a16
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View details ↗Qwen3-32B-FP8-dynamic
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View details ↗Qwen3-32B-NVFP4
RedHatAI- **Model Architecture:** Qwen/Qwen3-32B - **Input:** Text - **Output:** Text - **Model Optimizations:** - **Weight quantization:** FP4 - **Activation quantization:** FP4 - **Out-of-scope:** Use in any manner that violates applicable laws or regulations (including trade compliance laws). Use in languages other than English. - **Release Date:** 6/25/2025 - **Version:** 1.0 - **Model Developers:** RedHatAI This model is a quantized version of Qwen/Qwen3-32B. It was evaluated on a several tasks to assess the its quality in comparison to the unquatized model. This model was obtained by quantizing the weights and activations of Qwen/Qwen3-32B to FP4 data type, ready for inference with vLLM>=0.9.1 This optimization reduces the number of bits per parameter from 16 to 4, reducing the disk size and GPU memory requirements by approximately 75%.
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View details ↗Qwen3-32B-quantized.w4a16
RedHatAIThis model was obtained by quantizing the weights of Qwen3-32B to INT4 data type. This optimization reduces the number of bits per parameter from 16 to 4, reducing the disk size and GPU memory requirements by approximately 75%. Only the weights of the linear operators within transformers blocks are quantized. Weights are quantized using a symmetric per-group scheme, with group size 128. The GPTQ algorithm is applied for quantization, as implemented in the llm-compressor library. The model was evaluated on the OpenLLM leaderboard tasks (versions 1 and 2), using lm-evaluation-harness, and on reasoning tasks using lighteval. vLLM was used for all evaluations.
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View details ↗Qwen3-32B-speculator.eagle3
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View details ↗Qwen3-4B-FP8-dynamic
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