All LLM Models
Browse 1242 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
Qwen Base Invoicev1.01 1.5B
LaaP-ai · 1.5B · runs from 1.0 GB
87.5KQwen Base Invoicev1.01 1.5B is a 1.5B-parameter open language model from LaaP-ai in the Qwen 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.
Chandra
datalab-to · 8.8B · runs from 4.3 GB
532Chandra is Datalab's 8.8-billion-parameter vision-language OCR model, built on a Qwen3VL backbone, that converts document images and PDFs into markdown, HTML, or JSON while preserving layout, tables, math, and forms with checkboxes. It handles handwriting, multi-column and complex layouts, and extracts images and diagrams with captions and structured data across more than 40 languages, aiming at document digitization rather than general chat. On the olmOCR benchmark Chandra scored highest overall among compared models, ahead of Datalab's own Marker pipeline, Mistral's OCR API, DeepSeek-OCR, and anchored GPT-4o and Gemini Flash 2 baselines. At under 9 billion parameters it runs on a single consumer GPU. Context length is 262,144 tokens. It is released under an OpenRAIL license, a responsible-AI license that permits broad use but restricts certain harmful applications. It was published in October 2025 and has since been superseded by a newer Chandra OCR 2 model.
Nemotron Labs Diffusion 8B Base
NVIDIA · 8.5B · runs from 17.6 GB
Nemotron Labs Diffusion 8B Base is a 8.5B-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.
MobileLLaMA 1.4B Chat
mtgv · 1.4B · runs from 1.3 GB
MobileLLaMA 1.4B Chat is a 1.4B-parameter open language model from mtgv in the Llama family. It supports a context window of up to 2,048 tokens. See its VRAM requirements by quantization and which GPUs and Macs can run it locally below.
TinyLLama V0
Maykeye · 5M · runs from 0.0 GB
TinyLLama V0 is a 5M-parameter open language model from Maykeye in the TinyLlama family. It supports a context window of up to 2,048 tokens. See its VRAM requirements by quantization and which GPUs and Macs can run it locally below.
Llama2 0B Unit Test
MaxJeblick · 770940 · runs from 0.3 GB
Llama2 0B Unit Test is a 770940-parameter open language model from MaxJeblick in the Llama 2 family. It supports a context window of up to 1,024 tokens. See its VRAM requirements by quantization and which GPUs and Macs can run it locally below.
Nl2sh 1.5B Q4 K M
ThorOdinson246 · 1.5B · runs from 0.7 GB
Nl2sh 1.5B Q4 K M is a 1.5B-parameter open language model from ThorOdinson246. See its VRAM requirements by quantization and which GPUs and Macs can run it locally below.
Apertus 70B 2509
swiss-ai · 70.6B · runs from 31.0 GB
Apertus 70B 2509 is a 70.6B-parameter open language model from swiss-ai in the Apertus family. It supports a context window of up to 65,536 tokens. See its VRAM requirements by quantization and which GPUs and Macs can run it locally below.
Moonlight 16B A3B
Moonshot AI · 16.0B · runs from 7.5 GB
Moonlight 16B A3B is a compact Mixture-of-Experts model from Moonshot AI that packs 16 billion total parameters while activating only around 3 billion per token. This efficient sparse design lets it punch well above its active parameter count, delivering surprisingly strong chat performance for its effective inference cost. The small active parameter count means Moonlight runs briskly on modest hardware, fitting comfortably on GPUs with 8–12 GB of VRAM at common quantization levels. It is an appealing choice for users who want MoE-level performance diversity without the heavy memory footprint typically associated with mixture models.
Qwen3 30B A3B.w8a8
nytopop · 30.6B · runs from 13.4 GB
Qwen3 30B A3B.w8a8 is a 30.6B-parameter open language model from nytopop in the Qwen 3 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.
Transformer 1.3B 100B
fla-hub · 1.4B · runs from 3 GB
Transformer 1.3B 100B is a 1.4B-parameter open language model from fla-hub. It supports a context window of up to 2,048 tokens. See its VRAM requirements by quantization and which GPUs and Macs can run it locally below.
Nemotron Labs Diffusion 8B
NVIDIA · 8.5B · runs from 17.6 GB
Nemotron Labs Diffusion 8B is a 8.5B-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.
Phi 3 Vision 128k Instruct
Microsoft · 4.1B · runs from 9.4 GB
Phi 3 Vision 128k Instruct is a 4.1B-parameter open language model from Microsoft in the Phi 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.
Qwen3.8 27B Uncensored
orcarouter · 27.8B · runs from 13.0 GB
Qwen3.8 27B Uncensored is a 27.8B-parameter open language model from orcarouter in the Qwen 3.8 family. See its VRAM requirements by quantization and which GPUs and Macs can run it locally below.
Falcon Mamba Tiny Dev
TII UAE · 9M · runs from 0.0 GB
Falcon Mamba Tiny Dev is a 9M-parameter open language model from TII UAE in the Falcon family. See its VRAM requirements by quantization and which GPUs and Macs can run it locally below.
Nemotron H 8B Base 8K
NVIDIA · 8.1B · runs from 17.8 GB
Nemotron H 8B Base 8K is a 8.1B-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.
Opt 6.7B
Meta · 6.7B · runs from 14.7 GB
Opt 6.7B is a 6.7B-parameter open language model from Meta. It supports a context window of up to 2,048 tokens. See its VRAM requirements by quantization and which GPUs and Macs can run it locally below.
K2 Horizon 7B Uno
IFM · 7B · runs from 3.3 GB
K2 Horizon 7B Uno is a 7B-parameter open language model from IFM. See its VRAM requirements by quantization and which GPUs and Macs can run it locally below.
Distil Lfm25 Shellper
distil-labs · 354M · runs from 0.5 GB
Distil Lfm25 Shellper is a 354M-parameter open language model from distil-labs. It supports a context window of up to 128,000 tokens. See its VRAM requirements by quantization and which GPUs and Macs can run it locally below.
XCurOS0.1 8B Instruct
XCurOS · 7.6B · runs from 15.7 GB
XCurOS0.1 8B Instruct is a 7.6B-parameter open language model from XCurOS. 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.
NVIDIA Nemotron 3 Nano 30B A3B Base BF16
NVIDIA · 31.6B · runs from 14.8 GB
NVIDIA Nemotron 3 Nano 30B A3B Base BF16 is the foundation model version of the Nemotron 3 Nano 30B, offered in full BF16 precision. Unlike the chat-tuned variants, this base model hasn't been instruction-tuned, making it suitable for fine-tuning, research, or custom alignment workflows. At 31.6 billion total parameters with a mixture-of-experts architecture, the base model gives developers and researchers a strong starting point for building specialized applications. It retains all the architectural benefits of the MoE design while leaving the behavioral layer open for customization.
Qwen3.8 27B EfficientThink Uncensored K3 Opus5 Grok4.6 GPT5.6Sol SFT SimPO DFlash2
nerkyor · 27B · runs from 12.6 GB
Qwen3.8 27B EfficientThink Uncensored K3 Opus5 Grok4.6 GPT5.6Sol SFT SimPO DFlash2 is a 27B-parameter open language model from nerkyor in the Qwen 3.8 family. See its VRAM requirements by quantization and which GPUs and Macs can run it locally below.
Granite 4.2 3B
IBM · 3.7B · runs from 2.0 GB
Granite 4.2 3B is a 3.7B-parameter open language model from IBM in the Granite 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.
DialoGPT Small
Microsoft · 176M · runs from 0.1 GB
DialoGPT Small is a 176M-parameter open language model from Microsoft. It supports a context window of up to 1,024 tokens. See its VRAM requirements by quantization and which GPUs and Macs can run it locally below.
GLM 5.3 Flash DFlash2
incoai · 1.2B · runs from 0.8 GB
GLM 5.3 Flash DFlash2 is a 1.2B-parameter open language model from incoai in the GLM 5 family. It supports a context window of up to 1,048,576 tokens. See its VRAM requirements by quantization and which GPUs and Macs can run it locally below.
GPT Neo 1.3B
EleutherAI · 1.4B · runs from 3 GB
GPT Neo 1.3B is a 1.4B-parameter open language model from EleutherAI. It supports a context window of up to 2,048 tokens. See its VRAM requirements by quantization and which GPUs and Macs can run it locally below.
ERNIE 4.5 21B A3B Thinking
Baidu · 21.8B · runs from 9.7 GB
ERNIE 4.5 21B A3B Thinking is a 21.8B-parameter open language model from Baidu in the ERNIE 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.
Llama Guard 3 8B
Meta · 8.0B · runs from 17.7 GB
Meta Llama Guard 3 8B is an 8-billion parameter safety classifier model built on the Llama 3.1 architecture. Unlike general-purpose chat models, Llama Guard is specifically designed to classify whether prompts or responses contain unsafe content across categories such as violence, sexual content, criminal planning, and other policy violations. The model is intended to be used as a moderation layer in LLM-based applications, providing input and output safety filtering. It follows a taxonomy-based classification approach and can be customized for different safety policies. Released under the Llama 3.1 Community License.
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.
GPT Bigcode Santacoder
BigCode · 1.1B · runs from 0.5 GB
GPT Bigcode Santacoder is a 1.1B-parameter open language model from BigCode. It supports a context window of up to 2,048 tokens. See its VRAM requirements by quantization and which GPUs and Macs can run it locally below.