All LLM Models
Browse 982 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
Kimi K2.6 Eagle3 Mla
lightseekorg · 3.0B · runs from 1.6 GB
38.8K
7Kimi K2.6 Eagle3 Mla is a 3.0B-parameter open language model from lightseekorg 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.
Qwen3Guard Gen 8B
Alibaba · 8.2B · runs from 4.1 GB
Qwen3Guard Gen 8B is a 8.2B-parameter open language model from Alibaba in the Qwen 3 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.
Ling Mini 2.0
Inclusion AI · 16.3B · runs from 7.3 GB
Ling Mini 2.0 is a 16.3B-parameter open language model from Inclusion 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.
Olmo 3 7B Instruct SFT
Allen AI · 7.3B · runs from 4.5 GB
Olmo 3 7B Instruct SFT is a 7.3B-parameter open language model from Allen AI in the OLMo 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.
Tofu Ft Phi 1.5
locuslab · 1.4B · runs from 1.3 GB
Tofu Ft Phi 1.5 is a 1.4B-parameter open language model from locuslab in the Phi 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.
Internlm2 5 7B Chat
InternLM · 7B · runs from 3.5 GB
Internlm2 5 7B Chat is a 7B-parameter open language model from InternLM in the InternLM 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.
Ko PlatYi 6B
kyujinpy · 6B · runs from 3.0 GB
Ko PlatYi 6B is a 6B-parameter open language model from kyujinpy. 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.
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.
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.
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.
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.
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.
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.