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
Parable Granite 4.1 8B Claude Fable 5
AnkitAI · 8.4B · runs from 4.2 GB
1.2K
2Parable Granite 4.1 8B Claude Fable 5 is a 8.4B-parameter open language model from AnkitAI 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.
GPT OSS 20B Heretic Ara v3
p-e-w · 21.5B · runs from 9.5 GB
GPT OSS 20B Heretic Ara v3 is a 21.5B-parameter open language model from p-e-w in the GPT-OSS 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.
Hunyuan 0.5B Pretrain
Tencent · 539M · runs from 0.6 GB
Hunyuan 0.5B Pretrain 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.
Orca Mini 3B
pankajmathur · 3.4B · runs from 1.6 GB
Orca Mini 3B is a 3.4B-parameter open language model from pankajmathur in the Orca family. 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.
WebWorld 8B
Alibaba · 8.2B · runs from 4.1 GB
WebWorld 8B is a 8.2B-parameter open language model from Alibaba. 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.
ToolWeave Stage3
muradil211 · 4.4B · runs from 2.4 GB
ToolWeave Stage3 is a 4.4B-parameter open language model from muradil211. 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.
Minicpm5 2B Distilled Reasoning
jigs97022 · 2.5B · runs from 1.5 GB
Minicpm5 2B Distilled Reasoning is a 2.5B-parameter open language model from jigs97022 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.
LFM2 350M Extract
Liquid AI · 354M · runs from 0.5 GB
LFM2 350M Extract is a 354M-parameter open language model from Liquid AI in the LFM2 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.
Qwen3.8 27B DSpark Agentic
tiyuvta · 27B · runs from 11.8 GB
Qwen3.8 27B DSpark Agentic is a 27B-parameter open language model from tiyuvta 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.
Ternary Bonsai 2 27B Vllm
fraserprice · 3.5B · runs from 7.8 GB
Ternary Bonsai 2 27B Vllm is a 3.5B-parameter open language model from fraserprice. 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.
Phi 4 Mini Flash Reasoning
Microsoft · 3.9B · runs from 2.3 GB
Phi 4 Mini Flash Reasoning is a 3.9B-parameter open language model from Microsoft in the Phi 4 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.
PicoMistral 23M
PicoKittens · 24M · runs from 0.3 GB
PicoMistral 23M is a 24M-parameter open language model from PicoKittens in the Mistral family. 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.
LFM2.5 230M Chess
mlabonne · 231M · runs from 0.5 GB
LFM2.5 230M Chess is a 231M-parameter open language model from mlabonne 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.
Cagliostro V1
bench-labs · 157M · runs from 0.5 GB
Cagliostro V1 is a 157M-parameter open language model from bench-labs. 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.
Qwen3.6 27B M
trymirai · 14.1B · runs from 6.6 GB
Qwen3.6 27B M is a 14.1B-parameter open language model from trymirai in the Qwen 3.6 family. See its VRAM requirements by quantization and which GPUs and Macs can run it locally below.
Qwen3.5 4B EmperoAI Qwen3.8 Distill Heretic Abliterated
insraq · 4.5B · runs from 9.6 GB
Qwen3.5 4B EmperoAI Qwen3.8 Distill Heretic Abliterated is a 4.5B-parameter open language model from insraq 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.
MiniCPM5 1B CoreAI
mlboydaisuke · 1B · runs from 2.2 GB
MiniCPM5 1B CoreAI is a 1B-parameter open language model from mlboydaisuke in the MiniCPM family. See its VRAM requirements by quantization and which GPUs and Macs can run it locally below.
Ice AI
darkps · 8.2B · runs from 4.1 GB
Ice AI is a 8.2B-parameter open language model from darkps. 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.
Gemma 4 12B Agentic Fable5 Composer2.5 v2 3.5x Tau2
yuxinlu1 · 12.0B · runs from 6.1 GB
Gemma 4 12B Agentic Fable5 Composer2.5 v2 3.5x Tau2 is a 12.0B-parameter open language model from yuxinlu1 in the Gemma 4 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.
Kumru 2B
vngrs-ai · 2.4B · runs from 1.4 GB
Kumru 2B is a 2.4B-parameter open language model from vngrs-ai. 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.
Kumru 2B Base
vngrs-ai · 2.4B · runs from 1.4 GB
Kumru 2B Base is a 2.4B-parameter open language model from vngrs-ai. 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.
Turkish Gemma 9B v0.1
ytu-ce-cosmos · 9.2B · runs from 4.8 GB
Turkish Gemma 9B v0.1 is a 9.2B-parameter open language model from ytu-ce-cosmos in the Gemma 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.
Surjo 50M
SurjoLabs · 54M · runs from 0.4 GB
Surjo 50M is a 54M-parameter open language model from SurjoLabs. 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.
PicoLM 80M Instruct
aethertp · 90M · runs from 0.4 GB
PicoLM 80M Instruct is a 90M-parameter open language model from aethertp. 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.
Supra 50M Base
SupraLabs · 52M · runs from 0.3 GB
Supra 50M Base 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.
T5gemma L L Ul2 IT
Google · 1.2B · runs from 2.7 GB
T5gemma L L Ul2 IT is a 1.2B-parameter open language model from Google in the Gemma family. See its VRAM requirements by quantization and which GPUs and Macs can run it locally below.
Zagreus 0.4B Ita
mii-llm · 438M · runs from 0.6 GB
Zagreus 0.4B Ita is a 438M-parameter open language model from mii-llm. 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.
Xgen 7B 8k Base
Salesforce · 7B · runs from 3.3 GB
XGen-7B-8K-Base is Salesforce AI Research's 7-billion-parameter pretrained base language model, introduced in the 2023 paper "Long Sequence Modeling with XGen: A 7B LLM Trained on 8K Input Sequence Length" as one of the earlier open 7B models built specifically for longer input sequences. It is not instruction-tuned; a separate XGen-7B-8K-Inst checkpoint, released for research purposes only, adds supervised instruction fine-tuning on top of the same base, and a sibling XGen-7B-4K-Base uses a shorter 4K training sequence length. It uses OpenAI's Tiktoken tokenizer rather than a custom vocabulary. At 7 billion parameters it runs easily on a single consumer GPU. Context length is 8,192 tokens, the model's namesake feature. It is released under the Apache 2.0 license, permitting unrestricted commercial and research use, and was published in June 2023, predating the wave of 7B open models that followed later that year such as Mistral 7B.
Supergemma4 E4b Abliterated
Jiunsong · 7.5B · runs from 3.7 GB
Supergemma4 E4b Abliterated is a 7.5B-parameter open language model from Jiunsong 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.
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.