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bge-m3

furiosa-ai

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Embeddings & similarity
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This repository contains `LGAI-EXAONE/EXAONE-4.0-32B-FP8` together with a Furiosa Executable Bundle (FXB) for running it on FuriosaAI RNGD with Furiosa-LLM. The same model also runs on other frameworks (such as vLLM, SGLang, and Transformers); for usage with those, see the upstream `LGAI-EXAONE/EXAONE-4.0-32B-FP8` model card. Weights are quantized to **FP8** (static), following the upstream FP8 release, and activations use **dynamic FP8 quantization** at runtime (per-token / per-block). The KV cache stays in 16-bit precision. On RNGD, EXAONE-4.0-32B-FP8 runs with a **tensor-parallel size of 32 PEs**, which maps to **four RNGD cards** (8 PEs per card).

Text generation
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基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。

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Visual QA
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This repository contains RedHatAI/Meta-Llama-3.1-8B-Instruct-FP8-dynamic together with a Furiosa Executable Bundle (FXB) for running it on FuriosaAI RNGD with Furiosa-LLM. The same model also runs on other frameworks (such as vLLM, SGLang, and Transformers); for usage with those, see the upstream RedHatAI/Meta-Llama-3.1-8B-Instruct-FP8-dynamic model card. Llama-3.1-8B-Instruct is Meta's 8B instruction-tuned model, an auto-regressive dense transformer optimized for multilingual dialogue, instruction following, and tool usage. This is the FP8-dynamic quantized variant published by RedHatAI, derived from meta-llama/Llama-3.1-8B-Instruct. Its intended use is the same as the upstream RedHatAI/Meta-Llama-3.1-8B-Instruct-FP8-dynamic, and it is released under the Llama 3.1 Community License. The linear (Transformer block) weights are quantized to FP8 (static), and activations use dynamic FP8 quantization at runtime. Activations are quantized per token at inference time rather than with offline calibration.

Text generation
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Qwen3-32B-FP8

furiosa-ai

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Text generation
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Qwen3.6-27B-FP8

furiosa-ai

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Visual QA
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