All LLM Models
Browse 1214 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
Yi 1.5 9B
01.AI · 8.8B · runs from 4.3 GB
13.4K
53Yi 1.5 9B is a 8.8B-parameter open language model from 01.AI in the Yi 1.5 family. 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.
Mathstral 7B v0.1
Mistral AI · 7.2B · runs from 3.6 GB
Mathstral 7B v0.1 is a 7.2B-parameter open language model from Mistral 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.
Qwen3.6 27B Uncensored HauhauCS Aggressive MTP
AIOpsInSpace · 27B · runs from 10.0 GB
Qwen3.6 27B Uncensored HauhauCS Aggressive MTP is a 27B-parameter open language model from AIOpsInSpace in the Qwen 3.6 family. See its VRAM requirements by quantization and which GPUs and Macs can run it locally below.
K2 Horizon 3.7B
IFM · 5.1B · runs from 10.6 GB
K2 Horizon 3.7B is a 5.1B-parameter open language model from IFM. 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.
Lfm2.5 2.6B Fable5 Coding Agent Heretic
saidutta69 · 2.7B · runs from 1.6 GB
Lfm2.5 2.6B Fable5 Coding Agent Heretic is a 2.7B-parameter open language model from saidutta69 in the LFM2.5 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.
TinyStories 33M
roneneldan · 33M · runs from 0.1 GB
TinyStories 33M is a 33M-parameter open language model from roneneldan. 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 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.
SOLAR 10.7B v1.0
Upstage · 10.7B · runs from 5.3 GB
SOLAR 10.7B v1.0 is a 10.7B-parameter open language model from Upstage in the Solar family. 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.
Qwable 9B Claude Fable 5
empero-ai · 9.4B · runs from 19.4 GB
Qwable 9B Claude Fable 5 is a 9.4B-parameter open language model from empero-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.
Qwen3.8 2B
empero-ai · 2.3B · runs from 1.4 GB
Qwen3.8 2B 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.
NuExtract 1.5 Tiny
numind · 494M · runs from 0.5 GB
NuExtract 1.5 Tiny is a 494M-parameter open language model from numind. 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.
Phi 3 Medium 4k Instruct
Microsoft · 14.0B · runs from 6.7 GB
Phi 3 Medium 4k Instruct is a 14.0B-parameter open language model from Microsoft in the Phi 3 family. 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.
Internlm2 5 20B Chat
InternLM · 19.9B · runs from 9.1 GB
Internlm2 5 20B Chat is a 19.9B-parameter open language model from InternLM in the InternLM 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.
ThinkingCap Qwen3.6 27B
bottlecapai · 27.4B · runs from 12.4 GB
ThinkingCap Qwen3.6 27B is a 27.4B-parameter open language model from bottlecapai in the Qwen 3.6 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.
Rwkv 4 169M Pile
RWKV · 169M · runs from 0.1 GB
Rwkv 4 169M Pile is a 169M-parameter open language model from RWKV. See its VRAM requirements by quantization and which GPUs and Macs can run it locally below.
Phi 1
Microsoft · 1.4B · runs from 0.7 GB
Phi 1 is a 1.4B-parameter open language model from Microsoft in the Phi 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.
Llama 3 ELYZA JP 8B
elyza · 8.0B · runs from 4.0 GB
Llama 3 ELYZA JP 8B is a 8.0B-parameter open language model from elyza 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.
PapersRAG 1.5B
metaresearch · 1.5B · runs from 1.0 GB
PapersRAG 1.5B is a 1.5B-parameter open language model from metaresearch. 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.
T5gemma 2B 2B Ul2
Google · 5.6B · runs from 2.6 GB
T5gemma 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.