All LLM Models
Browse 30 LLM models with VRAM requirements, quantization options, and hardware compatibility.
Understanding LLM VRAM Requirements
How much VRAM you need depends on the model size and quantization level. Quantization reduces the precision of model weights, trading small quality losses for significantly lower VRAM usage. For example, a 7B parameter model needs ~14 GB at FP16 but only ~4 GB at Q4_K_M quantization.
Model List
NVIDIA Nemotron 3.5 Lightning 30B A3B BF16
NVIDIA · 31.6B · runs from 9.1 GB
587.3K
221NVIDIA Nemotron 3.5 Lightning 30B A3B BF16 is a mixture-of-experts chat model in NVIDIA's Nemotron line, with roughly 30 billion total parameters and about 3 billion active per token. That active-parameter count drives inference speed, while the larger total governs memory footprint, and the Lightning name points to a variant tuned for fast responses; weights are published in BF16 precision. The model offers a 256K token context window and is released under a custom license rather than a standard open-source one. With only about 3 billion parameters active per token, it is well suited to running on a single high-end consumer GPU, especially once quantized, despite its larger total parameter count.
NVIDIA Nemotron Nano 9B v2
NVIDIA · 8.9B · runs from 3.5 GB
NVIDIA Nemotron Nano 9B v2 is a compact yet capable chat model from NVIDIA, packing 8.9 billion parameters into a size that runs comfortably on a wide range of consumer GPUs. Built on NVIDIA's Nemotron architecture, it delivers strong instruction-following and conversational performance while keeping VRAM requirements modest. This second-generation Nano model reflects NVIDIA's push to make high-quality language models accessible on local hardware. It's an excellent starting point for users who want a responsive, general-purpose assistant without needing top-tier GPU memory.
Nemotron Orchestrator 8B
NVIDIA · 8.2B · runs from 4.1 GB
Nemotron Orchestrator 8B is a 8.2B-parameter open language model from NVIDIA in the Nemotron family. It supports a context window of up to 40,960 tokens. See its VRAM requirements by quantization and which GPUs and Macs can run it locally below.
NVIDIA Nemotron Nano 12B v2
NVIDIA · 12.3B · runs from 5.8 GB
NVIDIA Nemotron Nano 12B v2 is a 12.3B-parameter open language model from NVIDIA in the Nemotron family. See its VRAM requirements by quantization and which GPUs and Macs can run it locally below.
Nemotron 3 Nano Omni 30B A3B Reasoning BF16
NVIDIA · 33.0B · runs from 10.0 GB
Nemotron 3 Nano Omni 30B A3B Reasoning BF16 is a 33.0B-parameter open language model from NVIDIA in the Nemotron family. See its VRAM requirements by quantization and which GPUs and Macs can run it locally below.
NVIDIA Nemotron Nano 9B v2 Japanese
NVIDIA · 8.9B · runs from 4.4 GB
NVIDIA Nemotron Nano 9B v2 Japanese is a specialized variant of the Nemotron Nano 9B v2, fine-tuned for Japanese language understanding and generation. At 8.9 billion parameters, it maintains the same hardware-friendly footprint as the English version while delivering natural Japanese conversational ability. For users looking to run a Japanese-language assistant locally, this model offers a rare combination of compact size and dedicated language optimization from a major hardware vendor. It handles Japanese text with the fluency you'd expect from a purpose-built model rather than a multilingual afterthought.
Nemotron Cascade 2 30B A3B
NVIDIA · 31.6B · runs from 9.1 GB
Nemotron Cascade 2 30B A3B is a 31.6B-parameter open language model from NVIDIA in the Nemotron family. It supports a context window of up to 262,144 tokens. See its VRAM requirements by quantization and which GPUs and Macs can run it locally below.
Nemotron Mini 4B Instruct
NVIDIA · 4B · runs from 1.8 GB
Nemotron Mini 4B Instruct is a 4B-parameter open language model from NVIDIA in the Nemotron family. It supports a context window of up to 4,096 tokens. See its VRAM requirements by quantization and which GPUs and Macs can run it locally below.
LocateAnything 3B
NVIDIA · 3.8B · runs from 2 GB
LocateAnything-3B is NVIDIA's 3.8-billion-parameter vision-language model for visual grounding rather than open-ended chat: referring-expression grounding, dense multi-object detection, GUI element grounding, point-based localization, and document or OCR layout grounding. Its core contribution is Parallel Box Decoding, which predicts a complete bounding box in one parallel step instead of token-by-token autoregressive decoding, giving up to 2.5x higher throughput while preserving geometric consistency. It combines a Qwen2.5-3B-Instruct language backbone with a MoonViT-SO-400M vision encoder and was trained on 12 million images with over 138 million grounding queries; NVIDIA has folded it into the Nemotron 3 Nano Omni model for agentic and computer-use grounding. At under 4 billion parameters, it runs on a single consumer GPU. Context length is 32,768 tokens. It is released under NVIDIA's non-commercial research license, permitting academic and non-profit use only; commercial use requires a separate license from NVIDIA. It was published in May 2026.
NVIDIA Nemotron 3 Nano 4B BF16
NVIDIA · 4.0B · runs from 2.2 GB
NVIDIA Nemotron 3 Nano 4B BF16 is a 4.0B-parameter open language model from NVIDIA in the Nemotron family. It supports a context window of up to 262,144 tokens. See its VRAM requirements by quantization and which GPUs and Macs can run it locally below.
Cosmos Reason2 2B
NVIDIA · 2.4B · runs from 1.1 GB
Cosmos Reason2 2B is NVIDIA's 2.4-billion-parameter open reasoning vision-language model for physical AI, built on the Qwen3-VL-2B-Instruct backbone and designed to let robots and embodied agents perceive a scene and reason step by step about space, time, and physical common sense before acting. It generates long chains of thought and can output structured spatial data such as 2D/3D point localization, bounding boxes, and trajectories, plus OCR reading of scene text, going beyond a general chat assistant into physical-world planning. At just 2.4 billion parameters it is small enough to run on a single consumer GPU. Context length is 256,000 tokens, up sharply from the 16,000 tokens of the original Cosmos Reason1. It is released under the NVIDIA Open Model License, a custom license permitting commercial use and redistribution provided derivatives carry a "Built on NVIDIA Cosmos" attribution notice. It was published in December 2025, alongside a larger 8B sibling.
Cosmos Reason2 8B
NVIDIA · 8.8B · runs from 4.1 GB
Cosmos Reason2-8B is NVIDIA's 8.8-billion-parameter open reasoning vision-language model for physical AI, built on a Qwen3-VL-8B-Instruct backbone and tuned to reason step by step about video and images the way a human would when planning actions in the real world. Rather than just labeling objects, it applies physics, spatio-temporal understanding, and common sense to tasks like robot planning, autonomous-vehicle video captioning, and video-analytics annotation, producing structured outputs such as 2D/3D point localization, bounding boxes, trajectory coordinates, and on-screen OCR text. It ships alongside a smaller 2B variant for edge deployment, while the 8B model needs a capable single GPU or more, less once quantized. Context length is roughly 256,000 tokens, up sharply from 16,000 tokens in the original Cosmos Reason 1. It is released under the NVIDIA Open Model License, a custom license that permits commercial use and derivative models but requires attribution ("Built on NVIDIA Cosmos") and prohibits removing its safety guardrails. It was published in December 2025.
Nemotron Labs Diffusion 3B
NVIDIA · 3.8B · runs from 2.1 GB
Nemotron Labs Diffusion 3B is a 3.8B-parameter open language model from NVIDIA in the Nemotron family. It supports a context window of up to 262,144 tokens. See its VRAM requirements by quantization and which GPUs and Macs can run it locally below.
Llama 3.1 Nemotron Nano 8B V1
NVIDIA · 8B · runs from 2.8 GB
Llama 3.1 Nemotron Nano 8B is an 8-billion parameter chat model by NVIDIA, a compact entry in the Nemotron family derived from Meta's Llama 3.1 architecture. It applies NVIDIA's alignment and fine-tuning techniques to deliver improved response quality over the base Llama 3.1 8B Instruct model at the same parameter count. The model runs on consumer GPUs with 8GB or more of VRAM and supports a 128K token context window. Its small footprint and NVIDIA-tuned quality make it a practical option for local inference on mainstream hardware.
NVIDIA Nemotron Parse 2.0
NVIDIA · 903M · runs from 2.0 GB
NVIDIA Nemotron Parse 2.0 is a sub-1-billion-parameter vision-encoder-decoder model purpose-built for document parsing rather than open-ended chat: given a page image, it outputs structured text with layout classes, bounding boxes, and reading order for elements like titles, paragraphs, tables, charts, and footnotes. It pairs a ViT-H vision encoder based on NVIDIA's C-RADIO with a 10-block mBART decoder, and over its predecessor v1.2 it adds roughly 20,000 new vocabulary tokens for more efficient multilingual (especially CJK and Indic-script) OCR, a dedicated chart class for chart-to-table parsing, and stronger table detection and text extraction. At under a billion parameters it runs on a single modest GPU. License is the OpenMDW License Agreement version 1.1, permitting both commercial and non-commercial use; the bundled tokenizer is separately licensed under CC-BY-4.0. It was published in August 2026.
Mistral NeMo Minitron 8B Instruct
NVIDIA · 8.4B · runs from 4.2 GB
Mistral NeMo Minitron 8B Instruct is a 8.4B-parameter open language model from NVIDIA in the Mistral family. It supports a context window of up to 8,192 tokens. See its VRAM requirements by quantization and which GPUs and Macs can run it locally below.
Nemotron Cascade 8B
NVIDIA · 8B · runs from 4 GB
Nemotron Cascade 8B is a 8B-parameter open language model from NVIDIA in the Nemotron family. It supports a context window of up to 32,768 tokens. See its VRAM requirements by quantization and which GPUs and Macs can run it locally below.
Nemotron Labs Diffusion 14B
NVIDIA · 13.5B · runs from 6.5 GB
Nemotron Labs Diffusion 14B is a 13.5B-parameter open language model from NVIDIA in the Nemotron family. It supports a context window of up to 262,144 tokens. See its VRAM requirements by quantization and which GPUs and Macs can run it locally below.
OpenMath Nemotron 1.5B
NVIDIA · 1.5B · runs from 1.0 GB
OpenMath Nemotron 1.5B is a 1.5B-parameter open language model from NVIDIA in the Nemotron family. It supports a context window of up to 131,072 tokens. See its VRAM requirements by quantization and which GPUs and Macs can run it locally below.
Nemotron Research Reasoning Qwen 1.5B
NVIDIA · 1.8B · runs from 1.1 GB
Nemotron Research Reasoning Qwen 1.5B is a 1.8B-parameter open language model from NVIDIA in the Qwen family. It supports a context window of up to 131,072 tokens. See its VRAM requirements by quantization and which GPUs and Macs can run it locally below.
Nemotron Labs Audex 2B
NVIDIA · 2B · runs from 4.4 GB
Nemotron Labs Audex 2B is a 2B-parameter open language model from NVIDIA in the Nemotron family. See its VRAM requirements by quantization and which GPUs and Macs can run it locally below.
Nemotron Terminal 8B
NVIDIA · 8.2B · runs from 4.1 GB
Nemotron Terminal 8B is a 8.2B-parameter open language model from NVIDIA in the Nemotron family. It supports a context window of up to 40,960 tokens. See its VRAM requirements by quantization and which GPUs and Macs can run it locally below.
Nemotron Content Safety Reasoning 4B
NVIDIA · 4.3B · runs from 2.5 GB
Nemotron Content Safety Reasoning 4B is a 4.3B-parameter open language model from NVIDIA in the Nemotron family. It supports a context window of up to 131,072 tokens. See its VRAM requirements by quantization and which GPUs and Macs can run it locally below.
Kimi K2.6 DFlash
NVIDIA · 3.5B · runs from 1.8 GB
Kimi K2.6 DFlash is a 3.5B-parameter open language model from NVIDIA in the Kimi K2 family. It supports a context window of up to 262,144 tokens. See its VRAM requirements by quantization and which GPUs and Macs can run it locally below.
Llama 3.1 Nemotron Safety Guard 8B v3
NVIDIA · 8.0B · runs from 4.0 GB
Llama 3.1 Nemotron Safety Guard 8B v3 is a 8.0B-parameter open language model from NVIDIA in the Llama 3 family. It supports a context window of up to 131,072 tokens. See its VRAM requirements by quantization and which GPUs and Macs can run it locally below.
Kimi K2.7 Code DFlash
NVIDIA · 3.5B · runs from 1.8 GB
Kimi K2.7 Code DFlash is a 3.5B-parameter open language model from NVIDIA in the Kimi K2 family. It supports a context window of up to 262,144 tokens. See its VRAM requirements by quantization and which GPUs and Macs can run it locally below.
Kimi K2.6 Eagle3
NVIDIA · 1.8B · runs from 1.1 GB
Kimi K2.6 Eagle3 is a 1.8B-parameter open language model from NVIDIA in the Kimi K2 family. It supports a context window of up to 262,144 tokens. See its VRAM requirements by quantization and which GPUs and Macs can run it locally below.
Nemotron Terminal 14B
NVIDIA · 14.8B · runs from 6.9 GB
Nemotron Terminal 14B is a 14.8B-parameter open language model from NVIDIA in the Nemotron family. It supports a context window of up to 40,960 tokens. See its VRAM requirements by quantization and which GPUs and Macs can run it locally below.
Nemotron Flash 3B
NVIDIA · 2.7B · runs from 6.0 GB
Nemotron Flash 3B is a 2.7B-parameter open language model from NVIDIA in the Nemotron family. It supports a context window of up to 29,000 tokens. See its VRAM requirements by quantization and which GPUs and Macs can run it locally below.
Riva Translate 4B Instruct
NVIDIA · 4.2B · runs from 2.3 GB
Riva Translate 4B Instruct is a 4.2B-parameter open language model from NVIDIA. It supports a context window of up to 8,192 tokens. See its VRAM requirements by quantization and which GPUs and Macs can run it locally below.