All LLM Models

Browse 1214 LLM models with VRAM requirements, quantization options, and hardware compatibility.

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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.

Granite 3.2 8B Instruct

IBM · 8.2B · runs from 4.1 GB

Granite 3.2 8B Instruct is a 8.2B-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.

Fanar 1 9B Instruct

QCRI · 8.8B · runs from 4.7 GB

Fanar 1 9B Instruct is a 8.8B-parameter open language model from QCRI. 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.

OLMo 2 1124 7B Instruct

Allen AI · 7.3B · runs from 4.5 GB

OLMo 2 1124 7B Instruct is a 7.3B-parameter open language model from Allen AI in the OLMo 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.

Antares 1B

fdtn-ai · 1.8B · runs from 4.0 GB

Antares 1B is a 1.8B-parameter open language model from fdtn-ai. See its VRAM requirements by quantization and which GPUs and Macs can run it locally below.

Qwen3.5 4B Super Coder

jica98 · 4B · runs from 2.2 GB

Qwen3.5 4B Super Coder is a 4B-parameter open language model from jica98 in the Qwen 3.5 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.

BioMistral 7B

BioMistral · 7B · runs from 3.5 GB

BioMistral 7B is a 7B-parameter open language model from BioMistral in the Mistral 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.

Lynx Instruct 30B

bineric · 30.5B · runs from 13.4 GB

Lynx Instruct 30B is a 30.5B-parameter open language model from bineric. 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.

Llama3.1 Typhoon2 8B Instruct

typhoon-ai · 8.0B · runs from 4.0 GB

Llama3.1 Typhoon2 8B Instruct is a 8.0B-parameter open language model from typhoon-ai 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.

Ruadapt Qwen2.5 7B Ext U48 Instruct

RefalMachine · 7.6B · runs from 3.6 GB

Ruadapt Qwen2.5 7B Ext U48 Instruct is a 7.6B-parameter open language model from RefalMachine in the Qwen 2.5 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.

Qwen1.5 MoE A2.7B Chat

Alibaba · 14.3B · runs from 6.8 GB

Qwen1.5 MoE A2.7B Chat is a 14.3B-parameter open language model from Alibaba 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.

EXAONE 3.5 32B Instruct

LGAI-EXAONE · 32.0B · runs from 15.0 GB

EXAONE 3.5 32B Instruct is a 32.0B-parameter open language model from LGAI-EXAONE in the EXAONE 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.

Deepseek Moe 16B Base

DeepSeek · 16.4B · runs from 7.7 GB

Deepseek Moe 16B Base is a 16.4B-parameter open language model from DeepSeek in the DeepSeek 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.

Gemma 4 E4B IT OBLITERATED

OBLITERATUS · 8.0B · runs from 3.9 GB

Gemma 4 E4B IT OBLITERATED is a 8.0B-parameter open language model from OBLITERATUS in the Gemma 4 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.

TIPO 500M

KBlueLeaf · 508M · runs from 0.7 GB

TIPO 500M is a 508M-parameter open language model from KBlueLeaf. 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.

Mythos Nano

squ11z1 · 3.1B · runs from 1.7 GB

Mythos Nano is a 3.1B-parameter open language model from squ11z1. 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.

ChatReasoningMathCode

Deepseek Coder 1.3B Base

DeepSeek · 1.3B · runs from 1.3 GB

Deepseek Coder 1.3B Base is a 1.3B-parameter open language model from DeepSeek in the DeepSeek Coder family. It supports a context window of up to 16,384 tokens. See its VRAM requirements by quantization and which GPUs and Macs can run it locally below.

OneReason 0.8B Pretrain Competition

OpenOneRec · 801M · runs from 0.8 GB

OneReason 0.8B Pretrain Competition is a 801M-parameter open language model from OpenOneRec. 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.

Qwen3.6 27B MTPLX Optimized Speed

Youssofal · 4.7B · runs from 2.7 GB

Qwen3.6 27B MTPLX Optimized Speed is a 4.7B-parameter open language model from Youssofal in the Qwen 3.6 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 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.

MiniCPM 2B Sft BF16

OpenBMB · 2B · runs from 1.9 GB

MiniCPM 2B Sft BF16 is a 2B-parameter open language model from OpenBMB in the MiniCPM 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.

Falcon 7B Instruct

TII UAE · 7.2B · runs from 3.4 GB

Falcon 7B Instruct is the instruction-tuned version of TII's Falcon 7B, fine-tuned on a mix of chat and instruction datasets to follow user prompts more reliably. It was among the early open models to show that a well-tuned 7B model could handle conversational tasks, summarization, and basic reasoning without requiring massive hardware. While newer models have since raised the bar, Falcon 7B Instruct remains a lightweight option for users who want a responsive local assistant with modest resource requirements.

Codegemma 2B

Google · 2.5B · runs from 1.2 GB

Codegemma 2B is a 2.5B-parameter open language model from Google in the Gemma 2 family. See its VRAM requirements by quantization and which GPUs and Macs can run it locally below.

Gemma 7B IT

Google · 8.5B · runs from 18.8 GB

Google Gemma 7B IT is a 7-billion parameter instruction-tuned model from the original Gemma generation. It is fine-tuned for conversational use and general instruction following, running efficiently on consumer GPUs with 8GB or more of VRAM. As a first-generation Gemma model, it has been superseded by Gemma 2 and Gemma 3 models in quality and capability, but it remains well-supported by inference frameworks. Released under the Gemma license.

Llm Jp 3.1 13B Instruct4

llm-jp · 13.7B · runs from 7.8 GB

Llm Jp 3.1 13B Instruct4 is a 13.7B-parameter open language model from llm-jp. 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.

ERNIE 4.5 0.3B PT

Baidu · 361M · runs from 0.5 GB

ERNIE 4.5 0.3B PT is a 361M-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.

Qwen3.6 35B A3B Claude 4.7 Opus Reasoning Distilled

lordx64 · 36.0B · runs from 15.7 GB

Qwen3.6 35B A3B Claude 4.7 Opus Reasoning Distilled is a 36.0B-parameter open language model from lordx64 in the Qwen 3.6 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 2 13B HF

Meta · 13.0B · runs from 6.1 GB

Llama-2-13b-hf is Meta's 13-billion-parameter base (pretrained, not instruction-tuned) language model from the original Llama 2 family, intended as a general-purpose foundation for natural-language generation and further fine-tuning rather than direct assistant-style chat, for which Meta released separate Llama-2-Chat checkpoints. It is an auto-regressive transformer trained on 2 trillion tokens of publicly available data with a September 2022 knowledge cutoff, using a global batch size of 4 million tokens; unlike the 70B model, the 13B size does not use grouped-query attention. It is a historically significant open-weight release rather than a current state-of-the-art model by 2026 standards. At 13 billion parameters, it fits on a single consumer GPU once quantized. Context length is 4,096 tokens. It is released under the Llama 2 Community License, a custom license that is free for most commercial and research use but requires organizations with more than 700 million monthly active users to request separate permission from Meta. It was published in July 2023.

C4ai Command R7b 12 2024

Cohere · 8.0B · runs from 17.7 GB

C4ai Command R7b 12 2024 is a 8.0B-parameter open language model from Cohere in the Command R family. See its VRAM requirements by quantization and which GPUs and Macs can run it locally below.

OCC RAG 1.7B

occ-ai · 1.7B · runs from 1.3 GB

OCC RAG 1.7B is a 1.7B-parameter open language model from occ-ai. 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.

Gemma 4 12B OBLITERATED

OBLITERATUS · 12.0B · runs from 6.1 GB

Gemma 4 12B OBLITERATED is a 12.0B-parameter open language model from OBLITERATUS in the Gemma 4 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.