All LLM Models

Browse 1475 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.

Param 1 5B

bharatgenai · 5B · runs from 10.4 GB

Param 1 5B is a 5B-parameter open language model from bharatgenai. 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.

LFM2.5 8B A1B Opus Distil

reaperdoesntknow · 8.5B · runs from 4 GB

LFM2.5 8B A1B Opus Distil is a 8.5B-parameter open language model from reaperdoesntknow 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.

GigaChat3 10B A1.8B Base

ai-sage · 11.5B · runs from 5.5 GB

GigaChat3 10B A1.8B Base is a 11.5B-parameter open language model from ai-sage. 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.

MAI DS R1

Microsoft · 671.0B · runs from 289.1 GB

MAI DS R1 is a 671.0B-parameter open language model from Microsoft. It supports a context window of up to 163,840 tokens. See its VRAM requirements by quantization and which GPUs and Macs can run it locally below.

YanoljaNEXT EEVE Instruct 2.8B

yanolja · 2.8B · runs from 2.2 GB

YanoljaNEXT EEVE Instruct 2.8B is a 2.8B-parameter open language model from yanolja. 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 4 E4B Luchador

rpDungeon · 8.0B · runs from 16.5 GB

Gemma 4 E4B Luchador is a 8.0B-parameter open language model from rpDungeon 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.

Internlm Chat 20B

InternLM · 20B · runs from 11.3 GB

InternLM-Chat-20B is a 20-billion-parameter chat model from a collaboration between the Shanghai AI Laboratory, SenseTime, CUHK, and Fudan University, built by applying supervised fine-tuning and RLHF on top of the InternLM-20B base model. InternLM-20B used a deliberately deep 60-layer architecture, versus the 32-40 layers typical of 7B/13B models of its era, trained on over 2.3 trillion tokens of English, Chinese, and code data, giving it stronger reasoning, math, and coding scores than contemporaries like Llama 2 13B and Baichuan2-13B. As a 20-billion-parameter dense model, it fits on a single consumer GPU once quantized. Context length is 4,096 tokens natively, extendable to about 16,384 tokens through inference-time extrapolation, per the model card. It is released under the Apache 2.0 license for the code, with model weights free for academic research and available for free commercial use after applying for a license from the developers. It was published in September 2023, predating the InternLM2 generation.

Mellum2 12B A2.5B Instruct SFT

JetBrains · 12.1B · runs from 5.5 GB

Mellum2 12B A2.5B Instruct 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.

MIST Mini 8B Thinking

olaverse · 8.0B · runs from 4.0 GB

MIST Mini 8B Thinking 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.

SmolLM2 70M

codelion · 69M · runs from 0.4 GB

SmolLM2 70M is a 69M-parameter open language model from codelion in the SmolLM 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.

StorySupra 10M

SupraLabs · 13M · runs from 0.3 GB

StorySupra 10M is a 13M-parameter open language model from SupraLabs. It supports a context window of up to 256 tokens. See its VRAM requirements by quantization and which GPUs and Macs can run it locally below.

CAT Thinking 8B

cyberagent · 8.2B · runs from 4.1 GB

CAT Thinking 8B is a 8.2B-parameter open language model from cyberagent. 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.

Mistral Small 3.2 24B Qiskit

Qiskit · 24.0B · runs from 10.9 GB

Mistral Small 3.2 24B Qiskit is a 24.0B-parameter open language model from Qiskit in the Mistral 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.

Qwen3.5 4B MiniFantasy MTP

MuXodious · 4.7B · runs from 9.8 GB

Qwen3.5 4B MiniFantasy MTP is a 4.7B-parameter open language model from MuXodious 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.

Gemma 2 Mitra E

buddhist-nlp · 9.2B · runs from 4.8 GB

Gemma 2 Mitra E is a 9.2B-parameter open language model from buddhist-nlp 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.

Instella 3B

amd · 3.1B · runs from 7.3 GB

Instella 3B is a 3.1B-parameter open language model from amd. 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.

XiaoHong V1

CongJ-Pan · 8.2B · runs from 4.1 GB

XiaoHong V1 is a 8.2B-parameter open language model from CongJ-Pan. 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.

Distil Qwen3 0.6B Text2sql

distil-labs · 596M · runs from 0.7 GB

Distil Qwen3 0.6B Text2sql is a 596M-parameter open language model from distil-labs 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.

GLM 4.5 Air Derestricted

ArliAI · 110.5B · runs from 47.4 GB

GLM 4.5 Air Derestricted is a 110.5B-parameter open language model from ArliAI in the GLM 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.

Step 3.5 Flash Base Midtrain

StepFun · 197.8B · runs from 92.5 GB

Step 3.5 Flash Base Midtrain is a 197.8B-parameter open language model from StepFun in the Step 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.

Text2cypher Gemma 2 9B IT Finetuned 2024v1

neo4j · 9B · runs from 4.2 GB

Text2cypher Gemma 2 9B IT Finetuned 2024v1 is a 9B-parameter open language model from neo4j in the Gemma 2 family. See its VRAM requirements by quantization and which GPUs and Macs can run it locally below.

Huihui LFM2.5 8B A1B Abliterated

huihui-ai · 8.5B · runs from 4 GB

Huihui LFM2.5 8B A1B Abliterated is a 8.5B-parameter open language model from huihui-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.

Sarvam 30B BF16

abhinand · 32.2B · runs from 14 GB

Sarvam 30B BF16 is a 32.2B-parameter open language model from abhinand. 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.

Granite Guardian 3.2 8B Factuality Detection

IBM · 8.2B · runs from 4.1 GB

Granite Guardian 3.2 8B Factuality Detection 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.

Llamatron 8B V1

Naphula · 8.0B · runs from 4.0 GB

Llamatron 8B V1 is a 8.0B-parameter open language model from Naphula in the Llama 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.

Protgpt2 Distilled Tiny

littleworth · 39M · runs from 0.0 GB

Protgpt2 Distilled Tiny is a 39M-parameter open language model from littleworth. 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.

Nafie 473M

nafie-ai · 473M · runs from 1.0 GB

Nafie 473M is a 473M-parameter open language model from nafie-ai. 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.

STEM Oracle 27B

Verdugie · 27B · runs from 12.6 GB

STEM Oracle 27B is a 27B-parameter open language model from Verdugie. See its VRAM requirements by quantization and which GPUs and Macs can run it locally below.

NVIDIA Nemotron 3 Ultra 550B A55B GenRM

NVIDIA · 560.5B · runs from 262.1 GB

NVIDIA Nemotron 3 Ultra 550B A55B GenRM is a 560.5B-parameter open language model from NVIDIA in the Nemotron 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.

Qwen3.5 27B Claude 4.6 Opus Reasoning Distilled Heretic v2

llmfan46 · 27.4B · runs from 12.4 GB

Qwen3.5 27B Claude 4.6 Opus Reasoning Distilled Heretic v2 is a 27.4B-parameter open language model from llmfan46 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.