All LLM Models

Browse 739 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.

II Medical 8B

Intelligent-Internet · 8.2B · runs from 4.1 GB

II Medical 8B is a 8.2B-parameter open language model from Intelligent-Internet. 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.

Polyglot Ko 1.3B

EleutherAI · 1.4B · runs from 0.7 GB

Polyglot Ko 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.

Schematron 3B

inference-net · 3B · runs from 1.8 GB

Schematron 3B is a 3B-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.

MythoMax L2 13B

Gryphe · 13B · runs from 7.5 GB

MythoMax L2 13B is a 13B-parameter open language model from Gryphe. 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.

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.

Gemma 3n E2B IT Litert Lm

Google · 2B · runs from 0.9 GB

Gemma 3n E2B IT Litert Lm is a 2B-parameter open language model from Google in the Gemma 3 family. See its VRAM requirements by quantization and which GPUs and Macs can run it locally below.

Nemotron Orchestrator 8B

NVIDIA · 8.2B · runs from 4.1 GB

Nemotron Orchestrator 8B is a 8.2B-parameter open language model from NVIDIA in the Nemotron 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.

Bella Bartender 8B Llama3.1

juiceb0xc0de · 8.0B · runs from 3.0 GB

Bella Bartender 8B Llama3.1 is a 8.0B-parameter open language model from juiceb0xc0de 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.

KONI Llama3.1 8B Instruct 20241024

KISTI-KONI · 8.0B · runs from 4.0 GB

KONI Llama3.1 8B Instruct 20241024 is a 8.0B-parameter open language model from KISTI-KONI 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.

Saul 7B Instruct V1

Equall · 7.2B · runs from 3.6 GB

Saul 7B Instruct V1 is a 7.2B-parameter open language model from Equall. 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.

Cali 0.1B

Sandroeth · 124M · runs from 0.3 GB

Cali 0.1B is a 124M-parameter open language model from Sandroeth. See its VRAM requirements by quantization and which GPUs and Macs can run it locally below.

DeepSeek V4 Flash JANG CRACK

dealignai · 33.5B · runs from 14.6 GB

DeepSeek V4 Flash JANG CRACK is a 33.5B-parameter open language model from dealignai in the DeepSeek V4 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.

Qwen3 4B Gemini 3.1 Pro Reasoning Distilled

khazarai · 4B · runs from 2.2 GB

Qwen3 4B Gemini 3.1 Pro Reasoning Distilled is a 4B-parameter open language model from khazarai in the Qwen 3 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.

OpenMath Nemotron 1.5B

NVIDIA · 1.5B · runs from 1.0 GB

OpenMath Nemotron 1.5B is a 1.5B-parameter open language model from NVIDIA in the Nemotron 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 4B Instruct 2507 Heretic

p-e-w · 4.0B · runs from 2.2 GB

Qwen3 4B Instruct 2507 Heretic is a 4.0B-parameter open language model from p-e-w in the Qwen 3 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.

Llama3 OpenBioLLM 70B

aaditya · 70B · runs from 30.7 GB

Llama3 OpenBioLLM 70B is a 70B-parameter open language model from aaditya in the Llama 3 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.

Bielik 4.5B V3.0 Instruct

speakleash · 4.8B · runs from 10.5 GB

Bielik 4.5B V3.0 Instruct is a 4.8B-parameter open language model from speakleash. See its VRAM requirements by quantization and which GPUs and Macs can run it locally below.

TildeOpen 30B

TildeAI · 30.7B · runs from 13.8 GB

TildeOpen 30B is a 30.7B-parameter open language model from TildeAI. 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 8B

tencent · 8.7B · runs from 17.9 GB

Penguin VL 8B is a 8.7B-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.

MiroThinker 1.7 Mini

miromind-ai · 30.5B · runs from 13.4 GB

MiroThinker 1.7 Mini is a 30.5B-parameter open language model from miromind-ai. 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.

Gemma 3n E4B IT Litert Lm

Google · 4B · runs from 1.9 GB

Gemma 3n E4B IT Litert Lm is a 4B-parameter open language model from Google in the Gemma 3 family. See its VRAM requirements by quantization and which GPUs and Macs can run it locally below.

AI21 Jamba Reasoning 3B

AI21 Labs · 3.2B · runs from 1.7 GB

AI21 Jamba Reasoning 3B is a 3.2B-parameter open language model from AI21 Labs in the Jamba 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 Base

JetBrains · 12.1B · runs from 24.7 GB

Mellum2 12B A2.5B Base 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.

LFM2.5 8B A1B Base

LiquidAI · 8.5B · runs from 4 GB

LFM2.5 8B A1B Base is a 8.5B-parameter open language model from LiquidAI. 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.

OpenThinker3 1.5B

open-thoughts · 1.5B · runs from 1.0 GB

OpenThinker3 1.5B is a 1.5B-parameter open language model from open-thoughts. 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.

SpatialLM1.1 Qwen 0.5B

manycore-research · 604M · runs from 1.5 GB

SpatialLM1.1 Qwen 0.5B is a 604M-parameter open language model from manycore-research 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.

Qwen3.5 2B Claude 4.6 Opus Reasoning Distilled

Jackrong · 2.3B · runs from 1.4 GB

Qwen3.5 2B Claude 4.6 Opus Reasoning Distilled is a 2.3B-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.

Thinkless 1.5B RL DeepScaleR

Vinnnf · 1.8B · runs from 1.1 GB

Thinkless 1.5B RL DeepScaleR is a 1.8B-parameter open language model from Vinnnf. 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.

Zeta 2.1

zed-industries · 8.3B · runs from 4.1 GB

Zeta 2.1 is a 8.3B-parameter open language model from zed-industries. 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.

Qwen3.5 4B Safety Thinking

MerlinSafety · 4.2B · runs from 2.3 GB

Qwen3.5 4B Safety Thinking is a 4.2B-parameter open language model from MerlinSafety 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.