All LLM Models

Browse 856 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.

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.

DeepSeek V3.2 Speciale

DeepSeek · 685.4B · runs from 295.2 GB

DeepSeek V3.2 Speciale is a 685.4B-parameter open language model from DeepSeek in the DeepSeek V3 family. 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.

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.

Bloom

BigScience · 176.2B · runs from 82.4 GB

Bloom is a 176.2B-parameter open language model from BigScience. See its VRAM requirements by quantization and which GPUs and Macs can run it locally below.

Tri 21B Think

trillionlabs · 20.7B · runs from 42.2 GB

Tri 21B Think is a 20.7B-parameter open language model from trillionlabs. 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.

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.

Meditron3 70B

EPFLiGHT · 70.6B · runs from 155.2 GB

Meditron3 70B is a 70.6B-parameter open language model from EPFLiGHT. See its VRAM requirements by quantization and which GPUs and Macs can run it locally below.

DeepSeek v2

DeepSeek · 235.7B · runs from 103.0 GB

DeepSeek v2 is a 235.7B-parameter open language model from DeepSeek in the DeepSeek V2 family. 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.

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.

Llama 3 1 Nemotron Ultra 253B V1

NVIDIA · 253.4B · runs from 557.5 GB

Llama 3 1 Nemotron Ultra 253B V1 is a 253.4B-parameter open language model from NVIDIA in the Llama 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.

TinyLlama 1.1B Chat V0.6

TinyLlama · 1.1B · runs from 0.8 GB

TinyLlama 1.1B Chat V0.6 is a 1.1B-parameter open language model from TinyLlama in the TinyLlama 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.

Apertus 70B Instruct 2509

swiss-ai · 70B · runs from 30.7 GB

Apertus 70B Instruct 2509 is a 70B-parameter open language model from swiss-ai in the Apertus family. 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.

Quasar 10B

silx-ai · 8.6B · runs from 17.8 GB

Quasar 10B is a 8.6B-parameter open language model from silx-ai. It supports a context window of up to 2,097,152 tokens. See its VRAM requirements by quantization and which GPUs and Macs can run it locally below.