LLM Models
Browse the world's large language models. Compare parameters, benchmarks, VRAM and more.
NVIDIA-Nemotron-3.5-Lightning-30B-A3B-BF16
The NVIDIA Nemotron-3.5-Lightning-30B-A3B-NVFP4-DSpark model is the DSpark speculative decoding checkpoint for NVIDIA's Nemotron-3.5-Lightning-30B-A3B model family, which is a hybrid LatentMoE language model designed for reasoning, chat, and agentic workflows. For more informatio…
Description: The NVIDIA Kimi-K2-Thinking-NVFP4 model is the quantized version of the Moonshot AI's Kimi-K2-Thinking model, which is an auto-regressive language model that uses an optimized transformer architecture. For more information, please check here. The NVIDIA Kimi-K2-Think…
--- --- Developers Granite Team, IBM Model Type Decoder-only Dense Transformer (Reasoning) Architecture GraniteForCausalLM Base Model Granite-4.1-8B-Base Parameters 8B Context Length Natively Supports 128K (Long-context extension to 512K) Precision bfloat16 Tested Languages Engli…
--- pipeline tag: text-generation base model: google/diffusiongemma-26B-A4B-it license: apache-2.0 license name: apache-license-2.0 license link: https://ai.google.dev/gemma/apache 2 tags: - nvidia - ModelOpt - DiffusionGemma-26B-A4B-IT - quantized - NVFP4 - nvfp4 ---
--- --- Developers Granite Team, IBM Model Type Decoder-only Dense Transformer (Reasoning) Architecture GraniteForCausalLM Base Model Granite-4.1-3B-Base Parameters 3B Context Length Natively Supports 128K (Long-context extension to 512K) Precision bfloat16 Tested Languages Engli…
LFM2.5-2.6B is part of LFM2.5, a family of hybrid models designed for on-device deployment . It builds on the LFM2 architecture with a 128K context window and agentic post-training.
LFM2.5 is a new family of hybrid models designed for on-device deployment . It builds on the LFM2 architecture with extended pre-training and reinforcement learning.
[!NOTE] Note: " -Paddle " models use PaddlePaddle weights, while " -PT " models use Transformer-style PyTorch weights.
STEP3-VL-10B is a lightweight open-source foundation model designed to redefine the trade-off between compact efficiency and frontier-level multimodal intelligence. Despite its compact 10B parameter footprint , STEP3-VL-10B excels in visual perception , complex reasoning , and hu…
[!NOTE] Note: " -Paddle " models use PaddlePaddle weights, while " -PT " models use Transformer-style PyTorch weights.
LFM2.5 is a new family of hybrid models designed for on-device deployment. It builds on the LFM2 architecture with extended pre-training and reinforcement learning.