All LLM Models

Browse 982 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.

Mistral 7B v0.2

mistral-community · 7.2B · runs from 3.6 GB

Mistral 7B v0.2 is a 7.2B-parameter open language model from mistral-community 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.

Josiefied Qwen3 8B Abliterated V1

Goekdeniz-Guelmez · 8.2B · runs from 4.1 GB

Josiefied Qwen3 8B Abliterated V1 is a 8.2B-parameter open language model from Goekdeniz-Guelmez in the Qwen 3 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.

PLLuM 12B Chat

CYFRAGOVPL · 12.2B · runs from 5.9 GB

PLLuM 12B Chat is a 12.2B-parameter open language model from CYFRAGOVPL. 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

Liquid AI · 8.5B · runs from 4 GB

LFM2.5 8B A1B Base is a 8.5B-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.

Dolphin 2.9 Llama3 8B

dphn · 8.0B · runs from 4.0 GB

Dolphin 2.9 Llama3 8B is a 8.0B-parameter open language model from dphn 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.

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.

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.

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.

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.

Humanizer Gemma 4 E4b

jialinyyzz · 7.9B · runs from 3.9 GB

Humanizer Gemma 4 E4b is a 7.9B-parameter open language model from jialinyyzz 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.

MiniCPM5 2B Midtrain

OpenBMB · 2.5B · runs from 1.5 GB

MiniCPM5 2B Midtrain is a 2.5B-parameter open language model from OpenBMB in the MiniCPM 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.

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.

Ssiat 1.0

MOGODIK · 255M · runs from 0.5 GB

Ssiat 1.0 is a 255M-parameter open language model from MOGODIK. 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.

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.

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.

G9v3 3B

ai9stars · 3.0B · runs from 1.7 GB

G9v3 3B is a 3.0B-parameter open language model from ai9stars. 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.

Instella MoE 16B A3B Think

amd · 15.9B · runs from 7.5 GB

Instella MoE 16B A3B Think is a 15.9B-parameter open language model from amd. 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.

YuE2 3B OrbitQuant W4A4

WaveCut · 2.2B · runs from 5.0 GB

YuE2 3B OrbitQuant W4A4 is a 2.2B-parameter open language model from WaveCut. It supports a context window of up to 24,576 tokens. See its VRAM requirements by quantization and which GPUs and Macs can run it locally below.

BitCPM CANN 8B

OpenBMB · 8B · runs from 3.8 GB

BitCPM-CANN-8B is OpenBMB's 8-billion-parameter ternary (1.58-bit) quantization-aware variant of its MiniCPM4-8B model, produced by the first publicly reported end-to-end 1.58-bit training system built natively for Huawei's Ascend NPU stack, integrating quantization-aware training into Megatron-LM with MindSpeed acceleration. Against its full-precision MiniCPM4-8B counterpart across 11 benchmarks it retains about 95.7% of performance, and the ternary training approach itself adds only a few percent training overhead on Ascend 910B hardware. This particular checkpoint ships in a "pseudo-quantized" format, with ternary values stored as ordinary floating-point weights, so despite the ternary training it loads and runs exactly like the full-precision 8B model rather than at reduced memory, and needs the same hardware as a dense 8B model to run locally. Context length is 32,768 tokens. It is released under the Apache 2.0 license, permitting unrestricted commercial and research use, and was published in May 2026, alongside smaller 0.5B, 1B, and 3B siblings in the same BitCPM-CANN family.

BAAR2 150M

aixk · 168M · runs from 0.4 GB

BAAR2 150M is a 168M-parameter open language model from aixk. 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.

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.

Qwen3 VL 8B Heretic 1.3.0

DreamFast · 8.8B · runs from 4.3 GB

Qwen3 VL 8B Heretic 1.3.0 is a 8.8B-parameter open language model from DreamFast 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.

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.

Mistral Nemo 2407 12B Thinking Claude Gemini GPT5.2 Uncensored HERETIC

DavidAU · 12.2B · runs from 5.9 GB

Mistral Nemo 2407 12B Thinking Claude Gemini GPT5.2 Uncensored HERETIC is a 12.2B-parameter open language model from DavidAU in the Mistral family. It supports a context window of up to 1,024,000 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.