All LLM Models
Browse 880 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
BananaMind 2 Pro
BananaMind · 160M · runs from 0.7 GB
1.5K
32BananaMind 2 Pro is a 160M-parameter open language model from BananaMind. It supports a context window of up to 3,072 tokens. See its VRAM requirements by quantization and which GPUs and Macs can run it locally below.
MiniCPM MoE 8x2B
OpenBMB · 8x2B · runs from 7.8 GB
MiniCPM MoE 8x2B is a 8x2B-parameter open language model from OpenBMB in the MiniCPM 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.
EuroMoE 2.6B A0.6B 2512
utter-project · 2.6B · runs from 1.5 GB
EuroMoE 2.6B A0.6B 2512 is a 2.6B-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.
SemanticRepair 270M
Gramscii-IT · 268M · runs from 0.4 GB
SemanticRepair 270M is a 268M-parameter open language model from Gramscii-IT. 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.
MedPsy 4B
qvac · 4.4B · runs from 2.4 GB
MedPsy 4B is a 4.4B-parameter open language model from qvac. 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.
MicroLlama v2
ViorikaAI-org · 45M · runs from 0.3 GB
MicroLlama v2 is a 45M-parameter open language model from ViorikaAI-org in the Llama 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.
Pythagoras Prover 4B
Pythagoras-LM · 4.4B · runs from 2.4 GB
Pythagoras Prover 4B is a 4.4B-parameter open language model from Pythagoras-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.
Llama 3.2 1B MathCodeInstruct 5k
OliverSundaram · 1.2B · runs from 0.9 GB
Llama 3.2 1B MathCodeInstruct 5k is a 1.2B-parameter open language model from OliverSundaram 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.
Turkish Gemma 9B T1
ytu-ce-cosmos · 9.2B · runs from 4.8 GB
Turkish Gemma 9B T1 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.
Parable Qwen3 4B Claude Fable 5
AnkitAI · 4.0B · runs from 2.2 GB
Parable Qwen3 4B Claude Fable 5 is a 4.0B-parameter open language model from AnkitAI 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.
Param 1 2.9B Instruct
bharatgenai · 2.9B · runs from 6.3 GB
Param 1 2.9B Instruct is a 2.9B-parameter open language model from bharatgenai. 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.
Open 1B Base
Gensyn · 1.6B · runs from 3.6 GB
Open 1B Base is a 1.6B-parameter open language model from Gensyn. 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.
SupraNeo 4M
SupraLabs · 4M · runs from 0.3 GB
SupraNeo 4M is a 4M-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.
Speck1 140M Instruct
specklabs · 141M · runs from 0.6 GB
Speck1 140M Instruct is a 141M-parameter open language model from specklabs. 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.
NuExtract 1.5
numind · 3.8B · runs from 2.7 GB
NuExtract 1.5 is a 3.8B-parameter open language model from numind. 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.
Vicuna 13B V1.3
LMSYS · 13B · runs from 6.1 GB
Vicuna-13B v1.3 is LMSYS's 13-billion-parameter chat assistant, fine-tuned from the original LLaMA base model on roughly 125,000 user-shared conversations collected from ShareGPT via supervised instruction fine-tuning. It was one of the earliest widely used open chat models and helped popularize LLM-as-a-judge and human-preference evaluation methodology, being assessed with standard benchmarks, human preference, and the Chatbot Arena leaderboard. LMSYS positions it as a research and hobbyist tool rather than a production assistant. At 13B parameters it needs a capable consumer GPU at full precision, considerably less once quantized. Context length is 2,048 tokens, inherited from the original LLaMA base model. It carries a non-commercial license, consistent with LLaMA's original research-only weights. It was published in June 2023.
CRia LM 75M Instruct
sz14 · 76M · runs from 0.5 GB
CRia LM 75M Instruct is a 76M-parameter open language model from sz14. 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.
Schematron 8B
inference-net · 8B · runs from 4.0 GB
Schematron 8B is a 8B-parameter open language model from inference-net. 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.
Petitgpt
yqi0 · 125M · runs from 0.3 GB
Petitgpt is a 125M-parameter open language model from yqi0. 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.
Huihui Spark X2.5 4B Abliterated
huihui-ai · 4.1B · runs from 2.2 GB
Huihui Spark X2.5 4B Abliterated is a 4.1B-parameter open language model from huihui-ai. It supports a context window of up to 1,048,576 tokens. See its VRAM requirements by quantization and which GPUs and Macs can run it locally below.
Kimi K2.7 Code DFlash
NVIDIA · 3.5B · runs from 1.8 GB
Kimi K2.7 Code DFlash is a 3.5B-parameter open language model from NVIDIA in the Kimi K2 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.
Manaca 1B Instruct
menezesbruno · 1.7B · runs from 1.1 GB
Manaca 1B Instruct is a 1.7B-parameter open language model from menezesbruno. 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.
Tiny Aya Water
Cohere · 3.3B · runs from 7.4 GB
Tiny Aya Water is a 3.3B-parameter open language model from Cohere in the Aya family. See its VRAM requirements by quantization and which GPUs and Macs can run it locally below.
Qwen3.5 9B Abliterated
lukey03 · 9.0B · runs from 4.4 GB
Qwen3.5 9B Abliterated is a 9.0B-parameter open language model from lukey03 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.
Falcon H1 0.5B Instruct
TII UAE · 521M · runs from 0.6 GB
Falcon H1 0.5B Instruct is a 521M-parameter open language model from TII UAE in the Falcon family. 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.
Cagliostro v3
bench-labs · 146M · runs from 0.7 GB
Cagliostro v3 is a 146M-parameter open language model from bench-labs. 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.
BananaMind 2 Mini
BananaMind · 28M · runs from 0.4 GB
BananaMind 2 Mini is a 28M-parameter open language model from BananaMind. 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.
Overfitter 1.0
BananaMind · 50M · runs from 0.4 GB
Overfitter 1.0 is a 50M-parameter open language model from BananaMind. 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.
Blaze SFT
SurjoLabs · 48M · runs from 0.4 GB
Blaze SFT is a 48M-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.
Gemma 3 1B IT Heretic Extreme Uncensored Abliterated
DavidAU · 1000M · runs from 0.8 GB
Gemma 3 1B IT Heretic Extreme Uncensored Abliterated is a 1000M-parameter open language model from DavidAU in the Gemma 3 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.