All LLM Models
Browse 671 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
Llama3 8B Chinese Chat
shenzhi-wang · 8.0B · runs from 4.0 GB
8.2K
689Llama3 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.
Yi 1.5 6B Chat
01.AI · 6.1B · runs from 3.0 GB
Yi 1.5 6B Chat is a 6.1B-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.
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.
SmolLM 360M Instruct
Hugging Face · 362M · runs from 0.5 GB
SmolLM 360M Instruct is a 362M-parameter open language model from Hugging Face in the SmolLM 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.
EuroLLM 1.7B Instruct
utter-project · 1.7B · runs from 1.2 GB
EuroLLM 1.7B Instruct is a 1.7B-parameter open language model from utter-project. 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.
Dolphin Mistral 24B Venice Edition
dphn · 24.0B · runs from 10.9 GB
Dolphin Mistral 24B Venice Edition is a 24.0B-parameter open language model from dphn in the Phi 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.
Qwen2.5 Coder 14B
Alibaba · 14.8B · runs from 7.0 GB
Qwen2.5 Coder 14B is a 14.8B-parameter open language model from Alibaba 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.
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.
Goedel Prover v2 32B
Goedel-LM · 32.8B · runs from 14.6 GB
Goedel Prover v2 32B is a 32.8B-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.
Huihui Qwen3.6 27B Abliterated
huihui-ai · 27.8B · runs from 12.6 GB
Huihui Qwen3.6 27B Abliterated is a 27.8B-parameter open language model from huihui-ai 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.
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.
Llama 3 Korean Bllossom 8B
MLP-KTLim · 8.0B · runs from 4.0 GB
Llama 3 Korean Bllossom 8B is a 8.0B-parameter open language model from MLP-KTLim 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.
Qwen3.6 35B A3B Claude 4.7 Opus Reasoning Distilled
lordx64 · 36.0B · runs from 15.7 GB
Qwen3.6 35B A3B Claude 4.7 Opus Reasoning Distilled is a 36.0B-parameter open language model from lordx64 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.
Salamandra 2B Instruct
BSC-LT · 2.3B · runs from 1.7 GB
Salamandra 2B Instruct is a 2.3B-parameter open language model from BSC-LT. 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.
Carbon 3B
HuggingFaceBio · 3.5B · runs from 1.9 GB
Carbon 3B is a 3.5B-parameter open language model from HuggingFaceBio. 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.
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.
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.
Meditron 7B
epfl-llm · 6.7B · runs from 14.8 GB
Meditron 7B is a 6.7B-parameter open language model from epfl-llm. See its VRAM requirements by quantization and which GPUs and Macs can run it locally below.
Qwen3.5 4B PTBR
lucasmg09 · 4B · runs from 1.5 GB
Qwen3.5 4B PTBR is a 4B-parameter open language model from lucasmg09 in the Qwen 3.5 family. See its VRAM requirements by quantization and which GPUs and Macs can run it locally below.
Txgemma 2B Predict
Google · 2.6B · runs from 1.2 GB
Txgemma 2B Predict 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.
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.
BitCPM CANN 8B
openbmb · 8B · runs from 3.8 GB
BitCPM CANN 8B is a 8B-parameter open language model from openbmb. 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.
SILMA 9B Instruct v1.0
silma-ai · 9.2B · runs from 4.8 GB
SILMA 9B Instruct v1.0 is a 9.2B-parameter open language model from silma-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.
Qwen3.6 27B MTPLX Optimized
Youssofal · 26.9B · runs from 12.2 GB
Qwen3.6 27B MTPLX Optimized is a 26.9B-parameter open language model from Youssofal 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.
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.
Sarashina2.2 3B Instruct v0.1
sbintuitions · 3.4B · runs from 2.1 GB
Sarashina2.2 3B Instruct v0.1 is a 3.4B-parameter open language model from sbintuitions. 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.