All LLM Models

Browse 880 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.

Sweep Next Edit v2 7B

sweepai · 7.6B · runs from 3.6 GB

Sweep Next Edit v2 7B is a 7.6B-parameter open language model from sweepai. 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.

Functiongemma 270M Ft Mobile Actions

litert-community · 270M · runs from 0.6 GB

Functiongemma 270M Ft Mobile Actions is a 270M-parameter open language model from litert-community in the Gemma family. See its VRAM requirements by quantization and which GPUs and Macs can run it locally below.

RedPajama INCITE 7B Base

togethercomputer · 7B · runs from 3.3 GB

RedPajama-INCITE-7B-Base is Together Computer's open base pretrained language model, not instruction-tuned, with roughly 6.9 billion parameters trained on the RedPajama-Data-1T dataset, an open reproduction of the corpus used to train Meta's original LLaMA. It was developed with a consortium including Ontocord.ai, ETH DS3Lab, Stanford CRFM and Hazy Research, and LAION, using compute awarded through the 2023 INCITE program. Instruction-tuned and chat variants, RedPajama-INCITE-7B-Instruct and RedPajama-INCITE-7B-Chat, were released alongside it. At under 7 billion parameters it runs on a single consumer GPU. Context length is 2,048 tokens. It is released under the Apache 2.0 license, permitting unrestricted commercial and research use. It was published in May 2023, as one of the first fully open, commercially usable base models trained on openly licensed data.

Nidum Gemma 2B Uncensored

VibeStudio · 2.5B · runs from 1.4 GB

Nidum Gemma 2B Uncensored is a 2.5B-parameter open language model from VibeStudio in the Gemma 2 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 X2 125M

AxiomicLabs · 144M · runs from 0.6 GB

GPT X2 125M is a 144M-parameter open language model from AxiomicLabs. 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.

VibeThinker 1.5B

WeiboAI · 1.8B · runs from 1.1 GB

VibeThinker 1.5B is a 1.8B-parameter open language model from WeiboAI. 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.

CyberSecQwen 4B

lablab-ai-amd-developer-hackathon · 4.0B · runs from 2.2 GB

CyberSecQwen 4B is a 4.0B-parameter open language model from lablab-ai-amd-developer-hackathon in the Qwen 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.

Recursive Language Model 198M

Girinath11 · 198M · runs from 0.4 GB

Recursive Language Model 198M is a 198M-parameter open language model from Girinath11. It supports a context window of up to 512 tokens. See its VRAM requirements by quantization and which GPUs and Macs can run it locally below.

ErniePEUnleashed

Kezmark · 3.4B · runs from 1.9 GB

ErniePEUnleashed is a 3.4B-parameter open language model from Kezmark in the ERNIE 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.

GPT S 5M

AxiomicLabs · 5M · runs from 0.3 GB

GPT S 5M is a 5M-parameter open language model from AxiomicLabs. It supports a context window of up to 512 tokens. See its VRAM requirements by quantization and which GPUs and Macs can run it locally below.

Falcon H1 7B Base

TII UAE · 7.6B · runs from 3.7 GB

Falcon H1 7B Base is a 7.6B-parameter open language model from TII UAE in the Falcon 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.

Schematron 3B

inference-net · 3.2B · runs from 1.9 GB

Schematron 3B is a 3.2B-parameter open language model from inference-net. 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.

Moore Lm

ouilyh · 42M · runs from 0.1 GB

Moore Lm is a 42M-parameter open language model from ouilyh. See its VRAM requirements by quantization and which GPUs and Macs can run it locally below.

NuExtract

numind · 3.8B · runs from 2.7 GB

NuExtract is a 3.8B-parameter open language model from numind. 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.

SKT ST X 0 3B

sKT-Ai-Labs · 3.4B · runs from 1.8 GB

SKT ST X 0 3B is a 3.4B-parameter open language model from sKT-Ai-Labs. 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.

Nanowhale 100M

cmpatino · 110M · runs from 0.5 GB

Nanowhale 100M is a 110M-parameter open language model from cmpatino. 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.

Josiefied Qwen3.5 0.8B Gabliterated V1

Goekdeniz-Guelmez · 853M · runs from 2.1 GB

Josiefied Qwen3.5 0.8B Gabliterated V1 is a 853M-parameter open language model from Goekdeniz-Guelmez 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.

Mellum2 12B A2.5B Thinking SFT

JetBrains · 12.1B · runs from 5.5 GB

Mellum2 12B A2.5B Thinking SFT is a 12.1B-parameter open language model from JetBrains in the Mellum 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.

Lucie 7B

OpenLLM-France · 6.7B · runs from 3.4 GB

Lucie 7B is a 6.7B-parameter open language model from OpenLLM-France. It supports a context window of up to 32,000 tokens. See its VRAM requirements by quantization and which GPUs and Macs can run it locally below.

Veritas 8B Fact Checker Non Thinking 1.0

resect-ai · 8.2B · runs from 4.1 GB

Veritas 8B Fact Checker Non Thinking 1.0 is a 8.2B-parameter open language model from resect-ai. 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.

Steelman 14B Ada

the-clanker-lover · 14B · runs from 6.5 GB

Steelman 14B Ada is a 14B-parameter open language model from the-clanker-lover. See its VRAM requirements by quantization and which GPUs and Macs can run it locally below.

Qwen3.5 9B Gemini 3.1 Pro Reasoning Distill

Jackrong · 9.7B · runs from 4.7 GB

Qwen3.5 9B Gemini 3.1 Pro Reasoning Distill is a 9.7B-parameter open language model from Jackrong 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.

MIST Mini 8B

olaverse · 8.0B · runs from 4.0 GB

MIST Mini 8B is a 8.0B-parameter open language model from olaverse. 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.

GPT X2 125M CIx Long Context

reaperdoesntknow · 126M · runs from 0.6 GB

GPT X2 125M CIx Long Context is a 126M-parameter open language model from reaperdoesntknow. 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.

Hunyuan 0.5B Instruct

Tencent · 539M · runs from 0.6 GB

Hunyuan 0.5B Instruct is a 539M-parameter open language model from Tencent in the Hunyuan 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.

Supra 50M Reasoning

SupraLabs · 52M · runs from 0.3 GB

Supra 50M Reasoning is a 52M-parameter open language model from SupraLabs. 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.

Phi 4 Quantized.w8a8

RedHatAI · 14.7B · runs from 7.0 GB

Phi 4 Quantized.w8a8 is a 14.7B-parameter open language model from RedHatAI in the Phi 4 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.

Dolphin X1 Trinity Nano

dphn · 6.1B · runs from 3.0 GB

Dolphin X1 Trinity Nano is a 6.1B-parameter open language model from dphn in the Phi 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.

Apertus 8B MeditronFO

EPFLiGHT · 8.1B · runs from 4.0 GB

Apertus 8B MeditronFO is a 8.1B-parameter open language model from EPFLiGHT 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.

Penguin VL 2B

Tencent · 2.2B · runs from 4.9 GB

Penguin VL 2B is a 2.2B-parameter open language model from Tencent. 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.