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- **Model Architecture:** MuseGlimmerForConditionalGeneration - **Input:** Text / Image - **Output:** Text - **Model Optimizations:** - **Weight quantization:** FP8 - **Activation quantization:** FP8 - **Release Date:** 2026-08-10 - **Version:** 1.0 - **Model Developers:** RedHatAI This model is a quantized version of meta-models/Muse-Glimmer-30B. This model was obtained by quantizing the weights and activations of meta-models/Muse-Glimmer-30B to FP8 (W8A8 block) data type, ready for inference with vLLM.

Visual QA
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- **Model Architecture:** NemotronHForCausalLM - **Input:** Text - **Output:** Text - **Model Optimizations:** - **Weight quantization:** FP8 - **Activation quantization:** FP8 - **Release Date:** 2026-08-13 - **Version:** 1.0 - **Model Developers:** RedHatAI This model is a quantized version of nvidia/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-BF16. This model was obtained by quantizing the weights and activations of nvidia/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-BF16 to the FP8 data type, using a per-tensor static scheme. This reduces the number of bits per parameter from 16 to 8, cutting GPU memory and disk requirements by approximately 50% and roughly doubling matrix-multiply compute throughput on FP8-capable hardware. Only the weights and activations of the linear operators within the transformer blocks are quantized, using LLM Compressor.

Text generationOpen weights
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Nemotron-Nano-3-30B-A3B-FP8 is a quantized version of Nemotron-Nano-3-30B-A3B and is a large language model (LLM) trained from scratch by NVIDIA, and designed as a unified model for both reasoning and non-reasoning tasks. It responds to user queries and tasks by first generating a reasoning trace and then concluding with a final response. The model's reasoning capabilities can be configured through a flag in the chat template. If the user prefers the model to provide its final answer without intermediate reasoning traces, it can be configured to do so, albeit with a slight decrease in accuracy for harder prompts that require reasoning. Conversely, allowing the model to generate reasoning traces first generally results in higher-quality final solutions to queries and tasks. The model employs a hybrid Mixture-of-Experts (MoE) architecture, consisting of 23 Mamba-2 and MoE layers, along with 6 Attention layers. Each MoE layer includes 128 experts plus 1 shared expert, with 6 experts activated per token. The model has 3.5B active parameters and 30B parameters in total. The supported languages include: English, German, Spanish, French, Italian, and Japanese. Improved using Qwen.

Text generation
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This model was obtained by quantizing activation and weights of phi-4 to FP8 data type. This optimization reduces the number of bits used to represent weights and activations from 16 to 8, reducing GPU memory requirements (by approximately 50%) and increasing matrix-multiply compute throughput (by approximately 2x). Weight quantization also reduces disk size requirements by approximately 50%. Only weights and activations of the linear operators within transformers blocks are quantized. Weights are quantized with a symmetric static per-channel scheme, whereas activations are quantized with a symmetric dynamic per-token scheme. The llm-compressor library is used for quantization. The model was evaluated on the OpenLLM leaderboard tasks (version 1) with the lm-evaluation-harness and the vLLM engine, using the following command:

Text generation
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