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.

Hermes 2 Pro Mistral 7B

Nous Research · 7.2B · runs from 3.6 GB

Hermes 2 Pro Mistral 7B is a 7.2B-parameter open language model from Nous Research 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.

Qwen1.5 0.5B

Alibaba · 620M · runs from 0.8 GB

Qwen1.5 0.5B is a 620M-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.

Qwen 7B Chat

Alibaba · 7.7B · runs from 3.6 GB

Qwen 7B Chat is a 7.7B-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.

Qwen2.5 32B

Alibaba · 32.8B · runs from 14.8 GB

Qwen2.5 32B is a 32.8B-parameter open language model from Alibaba in the Qwen 2.5 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.

GLM Z1 32B 0414

Z.ai · 32.6B · runs from 14.3 GB

GLM Z1 32B 0414 is a 32.6B-parameter open language model from Z.ai in the GLM 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.

Granite 3B Code Base 2k

IBM · 3.5B · runs from 2.5 GB

Granite 3B Code Base 2k is a 3.5B-parameter open language model from IBM in the Granite 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.

Gemma 2 9B

Google · 9.2B · runs from 4.3 GB

Gemma 2 9B is a 9.2B-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.

Nandi Mini 150M

FrontiersMind · 153M · runs from 0.6 GB

Nandi Mini 150M is a 153M-parameter open language model from FrontiersMind. 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 4.0 1B Base

IBM · 1.6B · runs from 1.2 GB

Granite 4.0 1B Base is a 1.6B-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.

Openthaigpt1.5 7B Instruct

openthaigpt · 7.6B · runs from 3.6 GB

Openthaigpt1.5 7B Instruct is a 7.6B-parameter open language model from openthaigpt. 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.

Academic Ds 9B

ByteDance-Seed · 9.4B · runs from 4.5 GB

Academic Ds 9B is a 9.4B-parameter open language model from ByteDance-Seed. 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.

Internlm Chat 7B

InternLM · 7B · runs from 15.4 GB

Internlm Chat 7B is a 7B-parameter open language model from InternLM in the InternLM 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.

Qwen2.5 Coder 0.5B

Alibaba · 494M · runs from 0.5 GB

Qwen2.5 Coder 0.5B is a 494-million parameter code-specialized model from Alibaba Cloud, the smallest in the Qwen 2.5 Coder series. It is designed for ultra-lightweight deployment where code-aware text generation is needed with minimal hardware resources. The model runs on virtually any GPU and even on CPU-only setups. While limited in capability compared to larger coding models, it is useful for basic code completion, prototyping, and experimentation. It supports a 128K token context window. Released under the Apache 2.0 license.

Llama XLAM 2 8B Fc R

Salesforce · 8.0B · runs from 4.0 GB

xLAM 2 8B FC-R is an 8-billion parameter model by Salesforce, specifically optimized for function calling and tool use. Built on the Llama architecture, it is designed to reliably generate structured function call outputs, making it suitable for agentic workflows and applications that require models to interact with external tools and APIs. Unlike general-purpose chat models, xLAM 2 focuses on accurately parsing user intent into structured tool invocations with proper argument formatting. It runs on consumer GPUs with 8GB or more of VRAM and is a strong choice for developers building local AI agent systems that need reliable function-calling capabilities.

Pythia 2.8B

EleutherAI · 2.9B · runs from 1.4 GB

Pythia 2.8B is a 2.9B-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.

LFM2.5 350M Base

Liquid AI · 354M · runs from 0.5 GB

LFM2.5 350M Base is a 354M-parameter open language model from Liquid AI in the LFM2.5 family. 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.

Llava Onevision Qwen2 7B Ov

lmms-lab · 8.0B · runs from 3.8 GB

Llava Onevision Qwen2 7B Ov is a 8.0B-parameter open language model from lmms-lab in the Qwen 2 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.

H2ovl Mississippi 800M

h2oai · 826M · runs from 1.8 GB

H2ovl Mississippi 800M is a 826M-parameter open language model from h2oai. See its VRAM requirements by quantization and which GPUs and Macs can run it locally below.

Smollm2 360M Aac Watch

dcostenco · 360M · runs from 0.2 GB

Smollm2 360M Aac Watch is a 360M-parameter open language model from dcostenco in the SmolLM family. See its VRAM requirements by quantization and which GPUs and Macs can run it locally below.

SOLAR 10.7B Instruct v1.0

Upstage · 10.7B · runs from 5.3 GB

SOLAR 10.7B Instruct v1.0 is a 10.7B-parameter open language model from Upstage in the Solar 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.

Deepseek Coder 1.3B Instruct

DeepSeek · 1.3B · runs from 1.3 GB

DeepSeek Coder 1.3B Instruct is an ultra-compact code model designed for environments where hardware resources are extremely limited. Despite having just 1.3 billion parameters, it can handle basic code completion, simple generation tasks, and code Q&A across common programming languages. This is one of the smallest viable code models available, capable of running on integrated graphics or very low-end dedicated GPUs. It is well suited for edge deployment, embedded development environments, or as a fast local autocomplete engine where response speed matters more than handling complex multi-file reasoning tasks.

Deepseek Llm 7B Base

DeepSeek · 7B · runs from 4.3 GB

Deepseek Llm 7B Base is a 7B-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.

Xing4.0 29B A4B

XingChen-AGI · 31.2B · runs from 14.7 GB

Xing4.0 29B A4B is a 31.2B-parameter open language model from XingChen-AGI. 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 Mla

lightseekorg · 3.0B · runs from 1.6 GB

Kimi 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.