All LLM Models
Browse 982 LLM models with VRAM requirements, quantization options, and hardware compatibility.
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.
Model List
T5gemma 2B 2B Ul2
Google · 5.6B · runs from 2.6 GB
10.1K
25T5gemma 2B 2B Ul2 is a 5.6B-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.
Ouro 1.4B Thinking
ByteDance · 1.4B · runs from 3.6 GB
Ouro 1.4B Thinking is a 1.4B-parameter open language model from ByteDance. 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.
Qwen3.8 2B Distill
empero-ai · 2.3B · runs from 1.4 GB
Qwen3.8 2B Distill is a 2.3B-parameter open language model from empero-ai in the Qwen 3.8 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.
Sarvam 1
sarvamai · 2.5B · runs from 1.6 GB
Sarvam 1 is a 2.5B-parameter open language model from sarvamai. 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.
Granite 3.1 2B Instruct
IBM · 2.5B · runs from 1.5 GB
Granite 3.1 2B Instruct is IBM's 2.5-billion-parameter dense instruction-tuned model, fine-tuned from Granite-3.1-2B-Base on permissively licensed open instruction datasets plus internally generated synthetic data aimed at long-context problems. It targets business-assistant work such as summarization, classification, extraction, question answering, retrieval-augmented generation, code tasks and function calling, and supports twelve languages including English, German, Spanish, French, Japanese, Arabic and Chinese. At this size it runs on almost any consumer GPU, and on a laptop CPU once quantized. Context length is 131,072 tokens. It is released under the Apache 2.0 license, permitting unrestricted commercial and research use, and was published in December 2024. IBM has since superseded it with Granite 3.3 2B Instruct, which keeps the same size class.
Supra 50M Instruct
SupraLabs · 52M · runs from 0.3 GB
Supra 50M Instruct 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.
Meta Llama 3 8B Instruct Abliterated v3
failspy · 8.0B · runs from 4.0 GB
Meta Llama 3 8B Instruct Abliterated v3 is a 8.0B-parameter open language model from failspy 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.
VibeThinker 3B
WeiboAI · 3.1B · runs from 1.7 GB
VibeThinker 3B is a 3.1B-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.
Ouro 2.6B
ByteDance · 2.7B · runs from 6.4 GB
Ouro 2.6B is a 2.7B-parameter open language model from ByteDance. 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.
Title
desert-ant-labs · 352M · runs from 0.5 GB
Title is a 352M-parameter open language model from desert-ant-labs. 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.
ALLaM 7B Instruct Preview
humain-ai · 7.0B · runs from 4.3 GB
ALLaM 7B Instruct Preview is a 7.0B-parameter open language model from humain-ai. 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.
Huihui Qwen3 8B Abliterated v2
huihui-ai · 8.2B · runs from 4.1 GB
Huihui Qwen3 8B Abliterated v2 is a 8.2B-parameter open language model from huihui-ai 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.
Granite Switch 4.1 3B Preview
IBM · 4.1B · runs from 8.8 GB
Granite Switch 4.1 3B Preview is a 4.1B-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.
Goedel Prover v2 8B
Goedel-LM · 8.2B · runs from 4.1 GB
Goedel Prover v2 8B is a 8.2B-parameter open language model from Goedel-LM. 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.
Mamba 1.4B HF
State Spaces · 1.4B · runs from 0.6 GB
Mamba 1.4B HF is a 1.4B-parameter open language model from State Spaces. See its VRAM requirements by quantization and which GPUs and Macs can run it locally below.
Llama3 8B Chinese Chat
shenzhi-wang · 8.0B · runs from 4.0 GB
Llama3 8B Chinese Chat is a 8.0B-parameter open language model from shenzhi-wang 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.
MiniCPM5 1B Base
OpenBMB · 1.1B · runs from 0.8 GB
MiniCPM5 1B Base is a 1.1B-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.
Mamba 2.8B HF
State Spaces · 2.8B · runs from 1.3 GB
Mamba 2.8B HF is a 2.8B-parameter open language model from State Spaces. See its VRAM requirements by quantization and which GPUs and Macs can run it locally below.
MiniCPM5 2B Base
OpenBMB · 2.5B · runs from 1.5 GB
MiniCPM5 2B Base is a 2.5B-parameter open language model from OpenBMB in the MiniCPM family. It supports a context window of up to 524,288 tokens. See its VRAM requirements by quantization and which GPUs and Macs can run it locally below.
Gemma 2 2B Jpn IT
Google · 2.6B · runs from 5.8 GB
Gemma 2 2B Jpn IT is a 2.6B-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.
TIPO V2.1 1B A200M
KBlueLeaf · 991M · runs from 0.7 GB
TIPO V2.1 1B A200M is a 991M-parameter open language model from KBlueLeaf. 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.
Qwen2.5 Coder 3B Claude Opus 4.6 Distilled
ryzdfm · 3.1B · runs from 1.7 GB
Qwen2.5 Coder 3B Claude Opus 4.6 Distilled is a 3.1B-parameter open language model from ryzdfm in the Qwen 2.5 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.
ShellWhisperer 1.5B
fableforge-ai · 1.5B · runs from 0.8 GB
ShellWhisperer 1.5B is a 1.5B-parameter open language model from fableforge-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.
DFM Mimir
danish-foundation-models · 1.8B · runs from 1.3 GB
DFM Mimir is a 1.8B-parameter open language model from danish-foundation-models. 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.
Tower Plus 9B
Unbabel · 9.2B · runs from 4.8 GB
Tower Plus 9B is a 9.2B-parameter open language model from Unbabel. 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.
Foundation Sec 8B
fdtn-ai · 8.0B · runs from 4.0 GB
Foundation Sec 8B is a 8.0B-parameter open language model from fdtn-ai. 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.
Nemotron Labs Diffusion 14B
NVIDIA · 13.5B · runs from 6.5 GB
Nemotron Labs Diffusion 14B is a 13.5B-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.
Llm Jp 3.1 1.8B Instruct4
llm-jp · 1.9B · runs from 1.5 GB
Llm Jp 3.1 1.8B Instruct4 is a 1.9B-parameter open language model from llm-jp. 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.
EuroLLM 9B Instruct 2512
utter-project · 9.2B · runs from 4.5 GB
EuroLLM 9B Instruct 2512 is a 9.2B-parameter open language model from utter-project. 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.
Quasar 3B A1B Preview
silx-ai · 2.9B · runs from 6.5 GB
Quasar 3B A1B Preview is a 2.9B-parameter open language model from silx-ai. 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.