All LLM Models
Browse 1138 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
QU SSM 130M MoE
Prannesshkva · 135M · runs from 0.3 GB
2.6K
2QU SSM 130M MoE is a 135M-parameter open language model from Prannesshkva. See its VRAM requirements by quantization and which GPUs and Macs can run it locally below.
Claude OSS
squ11z1 · 9.0B · runs from 4.4 GB
Claude OSS is a 9.0B-parameter open language model from squ11z1. 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.
Nemotron Research Reasoning Qwen 1.5B
NVIDIA · 1.8B · runs from 1.1 GB
Nemotron Research Reasoning Qwen 1.5B is a 1.8B-parameter open language model from NVIDIA in the Qwen 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.
Llama3 OpenBioLLM 8B
aaditya · 8B · runs from 4.0 GB
Llama3 OpenBioLLM 8B is a 8B-parameter open language model from aaditya 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.
Internlm 7B
InternLM · 7B · runs from 15.4 GB
InternLM-7B is InternLM's open base pretrained language model, not instruction-tuned, built at 7 billion parameters and trained on trillions of high-quality tokens to serve as a general-purpose knowledge foundation for downstream fine-tuning. On the OpenCompass evaluation suite it outperformed same-size peers such as LLaMA-7B and Baichuan-7B across disciplinary, language, knowledge, reasoning, and comprehension benchmarks. A matching InternLM-Chat-7B instruction-tuned version was released alongside it. Its 7B size fits on a single consumer GPU. Context length is 2,048 tokens. The code is released under Apache 2.0, while the model weights are free for academic research and free for commercial use only after completing InternLM's application form. It was published in July 2023, as InternLM's first open base model; larger InternLM2 and later families followed.
MeoinGTS1.5 1.5B
ali-arshiya · 494M · runs from 0.5 GB
MeoinGTS1.5 1.5B is a 494M-parameter open language model from ali-arshiya. 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.
Maple Preview
deepgrove · 20.2B · runs from 9.0 GB
Maple Preview is a 20.2B-parameter open language model from deepgrove. 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.
OpenGuardrails Text 2510
openguardrails · 14.8B · runs from 6.9 GB
OpenGuardrails Text 2510 is a 14.8B-parameter open language model from openguardrails. 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.
Elbaz NVIDIA Nemotron 3 Nano 30B A3B PRISM
Ex0bit · 30B · runs from 14.0 GB
Elbaz NVIDIA Nemotron 3 Nano 30B A3B PRISM is a 30B-parameter open language model from Ex0bit in the Nemotron family. See its VRAM requirements by quantization and which GPUs and Macs can run it locally below.
Huihui Ornith 1.5 9B Abliterated
huihui-ai · 9.4B · runs from 4.6 GB
Huihui Ornith 1.5 9B Abliterated is a 9.4B-parameter open language model from huihui-ai in the Ornith 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 Claude Opus Fable5 v2 Thinking Heretic
saidutta69 · 1.1B · runs from 0.8 GB
MiniCPM5 1B Claude Opus Fable5 v2 Thinking Heretic is a 1.1B-parameter open language model from saidutta69 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.
Nemotron Labs Audex 2B
NVIDIA · 2B · runs from 4.4 GB
Nemotron Labs Audex 2B is a 2B-parameter open language model from NVIDIA in the Nemotron family. See its VRAM requirements by quantization and which GPUs and Macs can run it locally below.
Nemotron Terminal 8B
NVIDIA · 8.2B · runs from 4.1 GB
Nemotron Terminal 8B is a 8.2B-parameter open language model from NVIDIA in the Nemotron 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.
Nemotron Content Safety Reasoning 4B
NVIDIA · 4.3B · runs from 2.5 GB
Nemotron Content Safety Reasoning 4B is a 4.3B-parameter open language model from NVIDIA in the Nemotron 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.
Qwen3.6 27B MTP TQ3 4S
YTan2000 · 27B · runs from 12.6 GB
Qwen3.6 27B MTP TQ3 4S is a 27B-parameter open language model from YTan2000 in the Qwen 3.6 family. See its VRAM requirements by quantization and which GPUs and Macs can run it locally below.
MustaqiLLM
NeuronUz · 5.2B · runs from 10.8 GB
MustaqiLLM is a 5.2B-parameter open language model from NeuronUz. 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.
Qwen3.8 27B ASCII Condensed
bsaleh03 · 27B · runs from 12.6 GB
Qwen3.8 27B ASCII Condensed is a 27B-parameter open language model from bsaleh03 in the Qwen 3.8 family. See its VRAM requirements by quantization and which GPUs and Macs can run it locally below.
ZGCM 1 7B
zgcagi · 7.4B · runs from 15.4 GB
ZGCM 1 7B is a 7.4B-parameter open language model from zgcagi. 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.
Emo 1b14b 1T
Allen AI · 13.6B · runs from 6.3 GB
Emo 1b14b 1T is a 13.6B-parameter open language model from Allen 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.
Supra 1.5 50M Instruct Exp
SupraLabs · 52M · runs from 0.3 GB
Supra 1.5 50M Instruct Exp is a 52M-parameter open language model from SupraLabs. It supports a context window of up to 5,120 tokens. See its VRAM requirements by quantization and which GPUs and Macs can run it locally below.
Veyra 30M Base
veyra-ai · 35M · runs from 0.3 GB
Veyra 30M Base is a 35M-parameter open language model from veyra-ai. 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.
Kimi K2.6 DFlash
NVIDIA · 3.5B · runs from 1.8 GB
Kimi K2.6 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.
Qwen2.5 Coder 7B Bird Cot
jk200201 · 7.6B · runs from 3.6 GB
Qwen2.5 Coder 7B Bird Cot is a 7.6B-parameter open language model from jk200201 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.
Lumma 0.6B Base
FrontiersMind · 649M · runs from 1.8 GB
Lumma 0.6B Base is a 649M-parameter open language model from FrontiersMind. It supports a context window of up to 12,288 tokens. See its VRAM requirements by quantization and which GPUs and Macs can run it locally below.
Qwen3.8 Flash Next Tq4a Tq2e G64
manjunathshiva · 16.6B · runs from 7.4 GB
Qwen3.8 Flash Next Tq4a Tq2e G64 is a 16.6B-parameter open language model from manjunathshiva 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.
Finance Llama3 8B
instruction-pretrain · 8.0B · runs from 4.0 GB
Finance Llama3 8B is a 8.0B-parameter open language model from instruction-pretrain 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.
Huihui Qwen3 4B Abliterated v2
huihui-ai · 4.0B · runs from 2.2 GB
Huihui Qwen3 4B Abliterated v2 is a 4.0B-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.
UserLM 8B
Microsoft · 8.0B · runs from 4.0 GB
UserLM 8B is a 8.0B-parameter open language model from Microsoft. 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.
MediPhi Instruct
Microsoft · 3.8B · runs from 2.7 GB
MediPhi Instruct is a 3.8B-parameter open language model from Microsoft 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.
Carbon 8B
HuggingFaceBio · 8.3B · runs from 4.1 GB
Carbon 8B is a 8.3B-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.