All LLM Models
Browse 739 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
Functiongemma 270M Ft Mobile Actions
litert-community · 270M · runs from 0.6 GB
194
65Functiongemma 270M Ft Mobile Actions is a 270M-parameter open language model from litert-community in the Gemma family. See its VRAM requirements by quantization and which GPUs and Macs can run it locally below.
CAT Thinking 8B
cyberagent · 8.2B · runs from 4.1 GB
CAT Thinking 8B is a 8.2B-parameter open language model from cyberagent. 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.
Mistral Small 3.2 24B Qiskit
Qiskit · 24.0B · runs from 10.9 GB
Mistral Small 3.2 24B Qiskit is a 24.0B-parameter open language model from Qiskit in the Mistral 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.
GPT OSS 20B RichardErkhov Heresy
MuXodious · 21.5B · runs from 9.5 GB
GPT OSS 20B RichardErkhov Heresy is a 21.5B-parameter open language model from MuXodious in the GPT-OSS 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.
Qwen3.5 4B MiniFantasy MTP
MuXodious · 4.7B · runs from 9.8 GB
Qwen3.5 4B MiniFantasy MTP is a 4.7B-parameter open language model from MuXodious 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.
Gemma 2 Mitra E
buddhist-nlp · 9.2B · runs from 4.8 GB
Gemma 2 Mitra E is a 9.2B-parameter open language model from buddhist-nlp in the Gemma 2 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.
Instella 3B
amd · 3.1B · runs from 7.3 GB
Instella 3B is a 3.1B-parameter open language model from amd. 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.
XiaoHong V1
CongJ-Pan · 8.2B · runs from 4.1 GB
XiaoHong V1 is a 8.2B-parameter open language model from CongJ-Pan. 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.
Distil Qwen3 0.6B Text2sql
distil-labs · 596M · runs from 0.7 GB
Distil Qwen3 0.6B Text2sql is a 596M-parameter open language model from distil-labs 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.
Huihui LFM2.5 8B A1B Abliterated
huihui-ai · 8.5B · runs from 4 GB
Huihui LFM2.5 8B A1B Abliterated is a 8.5B-parameter open language model from huihui-ai. It supports a context window of up to 128,000 tokens. See its VRAM requirements by quantization and which GPUs and Macs can run it locally below.
Sarvam 30B BF16
abhinand · 32.2B · runs from 14 GB
Sarvam 30B BF16 is a 32.2B-parameter open language model from abhinand. 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.
Granite Guardian 3.2 8B Factuality Detection
IBM · 8.2B · runs from 4.1 GB
Granite Guardian 3.2 8B Factuality Detection is a 8.2B-parameter open language model from IBM in the Granite 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.
Llamatron 8B V1
Naphula · 8.0B · runs from 4.0 GB
Llamatron 8B V1 is a 8.0B-parameter open language model from Naphula in the Llama 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.
NousCoder 14B
Nous Research · 14.8B · runs from 6.9 GB
NousCoder 14B is a 14.8B-parameter open language model from Nous Research. It supports a context window of up to 81,920 tokens. See its VRAM requirements by quantization and which GPUs and Macs can run it locally below.
Protgpt2 Distilled Tiny
littleworth · 39M · runs from 0.0 GB
Protgpt2 Distilled Tiny is a 39M-parameter open language model from littleworth. 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.
Nafie 473M
nafie-ai · 473M · runs from 1.0 GB
Nafie 473M is a 473M-parameter open language model from nafie-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.
STEM Oracle 27B
Verdugie · 27B · runs from 12.6 GB
STEM Oracle 27B is a 27B-parameter open language model from Verdugie. See its VRAM requirements by quantization and which GPUs and Macs can run it locally below.
Qwen3.5 27B Claude 4.6 Opus Reasoning Distilled Heretic v2
llmfan46 · 27.4B · runs from 12.4 GB
Qwen3.5 27B Claude 4.6 Opus Reasoning Distilled Heretic v2 is a 27.4B-parameter open language model from llmfan46 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.
Venice Uncensored
AskVenice · 23.6B · runs from 10.7 GB
Venice Uncensored is a 23.6B-parameter open language model from AskVenice. 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.
Qehwa Pashto Llm
junaid008 · 7.6B · runs from 3.6 GB
Qehwa Pashto Llm is a 7.6B-parameter open language model from junaid008. 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 Flash 3B
NVIDIA · 2.7B · runs from 6.0 GB
Nemotron Flash 3B is a 2.7B-parameter open language model from NVIDIA in the Nemotron family. It supports a context window of up to 29,000 tokens. See its VRAM requirements by quantization and which GPUs and Macs can run it locally below.
Apex 1 Instruct 350M
LH-Tech-AI · 350M · runs from 0.8 GB
Apex 1 Instruct 350M is a 350M-parameter open language model from LH-Tech-AI. See its VRAM requirements by quantization and which GPUs and Macs can run it locally below.
Qliphoth 12B V1.2
OccultAI · 12.2B · runs from 5.9 GB
Qliphoth 12B V1.2 is a 12.2B-parameter open language model from OccultAI. It supports a context window of up to 1,024,000 tokens. See its VRAM requirements by quantization and which GPUs and Macs can run it locally below.
Ouro Hybrid 1.4B
chili-lab · 1.5B · runs from 3.7 GB
Ouro Hybrid 1.4B is a 1.5B-parameter open language model from chili-lab. 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.
KernelLLM
Meta · 8.0B · runs from 4.0 GB
KernelLLM is a 8.0B-parameter open language model from Meta. 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.
OpenCodeReasoning Nemotron 1.1 32B
NVIDIA · 32.8B · runs from 14.8 GB
OpenCodeReasoning Nemotron 1.1 32B is a 32.8B-parameter open language model from NVIDIA in the Nemotron 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.
NextCoder 7B
Microsoft · 7.6B · runs from 3.6 GB
NextCoder 7B is a 7.6B-parameter open language model from Microsoft. 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.
Riva Translate 4B Instruct
NVIDIA · 4.2B · runs from 2.3 GB
Riva Translate 4B Instruct is a 4.2B-parameter open language model from NVIDIA. 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.
Meme Trix MoE 14B A8B V1
Naphula · 13.7B · runs from 6.4 GB
Meme Trix MoE 14B A8B V1 is a 13.7B-parameter open language model from Naphula. It supports a context window of up to 1,073,152 tokens. See its VRAM requirements by quantization and which GPUs and Macs can run it locally below.
Supra Mini V6 1M
SupraLabs · 1M · runs from 0.3 GB
Supra Mini V6 1M is a 1M-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.