All LLM Models
Browse 1214 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
Recursive Language Model 198M
Girinath11 · 198M · runs from 0.4 GB
787
10Recursive Language Model 198M is a 198M-parameter open language model from Girinath11. It supports a context window of up to 512 tokens. See its VRAM requirements by quantization and which GPUs and Macs can run it locally below.
ErniePEUnleashed
Kezmark · 3.4B · runs from 1.9 GB
ErniePEUnleashed is a 3.4B-parameter open language model from Kezmark in the ERNIE 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.
GPT S 5M
AxiomicLabs · 5M · runs from 0.3 GB
GPT S 5M is a 5M-parameter open language model from AxiomicLabs. It supports a context window of up to 512 tokens. See its VRAM requirements by quantization and which GPUs and Macs can run it locally below.
Falcon H1 7B Base
TII UAE · 7.6B · runs from 3.7 GB
Falcon H1 7B Base is a 7.6B-parameter open language model from TII UAE in the Falcon 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.
OmniCoder 9B
Tesslate · 9.4B · runs from 19.4 GB
OmniCoder 9B is a 9.4B-parameter open language model from Tesslate. 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.
XortronCriminalComputingConfig
darkc0de · 23.6B · runs from 10.7 GB
XortronCriminalComputingConfig is a 23.6B-parameter open language model from darkc0de. 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.
OpenReasoning Nemotron 32B
NVIDIA · 32.8B · runs from 14.8 GB
OpenReasoning Nemotron 32B is a 32.8B-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.
Schematron 3B
inference-net · 3.2B · runs from 1.9 GB
Schematron 3B is a 3.2B-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.
Qwen3.6 35B A3b Crown Halo Mtp Dynamic
jcbtc · 35B · runs from 16.4 GB
Qwen3.6 35B A3b Crown Halo Mtp Dynamic is a 35B-parameter open language model from jcbtc in the Qwen 3.6 family. See its VRAM requirements by quantization and which GPUs and Macs can run it locally below.
Moore Lm
ouilyh · 42M · runs from 0.1 GB
Moore Lm is a 42M-parameter open language model from ouilyh. See its VRAM requirements by quantization and which GPUs and Macs can run it locally below.
Stable DiffCoder 8B Instruct
ByteDance-Seed · 8.3B · runs from 17.1 GB
Stable DiffCoder 8B Instruct is a 8.3B-parameter open language model from ByteDance-Seed. 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.
NuExtract
numind · 3.8B · runs from 2.7 GB
NuExtract is a 3.8B-parameter open language model from numind. 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.
SKT ST X 0 3B
sKT-Ai-Labs · 3.4B · runs from 1.8 GB
SKT ST X 0 3B is a 3.4B-parameter open language model from sKT-Ai-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.
Harness 1
pat-jj · 20.9B · runs from 9.3 GB
Harness 1 is a 20.9B-parameter open language model from pat-jj. 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.
Nanowhale 100M
cmpatino · 110M · runs from 0.5 GB
Nanowhale 100M is a 110M-parameter open language model from cmpatino. 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.
Nemotron H 8B Reasoning 128K
NVIDIA · 8.1B · runs from 17.8 GB
Nemotron H 8B Reasoning 128K is a 8.1B-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.
Josiefied Qwen3.5 0.8B Gabliterated V1
Goekdeniz-Guelmez · 853M · runs from 2.1 GB
Josiefied Qwen3.5 0.8B Gabliterated V1 is a 853M-parameter open language model from Goekdeniz-Guelmez 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.
Vicuna 33B V1.3
LMSYS · 33B · runs from 15.4 GB
Vicuna-33B-v1.3 is LMSYS's chat assistant, fine-tuned from Meta's original LLaMA (33B) with supervised instruction tuning on around 125,000 user-shared conversations collected from ShareGPT.com. It was one of the models that popularized using GPT-4 and human preference judging as an evaluation method for open chat models, and is intended primarily for research on large language models and chatbots rather than production deployment. At 33 billion dense parameters it needs a high-end consumer GPU or multi-GPU setup once quantized. Context length is 2,048 tokens, inherited from the original LLaMA base. It is released under a non-commercial license, reflecting both LLaMA's original research-only terms and ShareGPT's usage terms, so it cannot be used commercially. It was published in June 2023; it is an early, now historical Vicuna release, later superseded by Vicuna v1.5 built on Llama 2.
Mellum2 12B A2.5B Thinking SFT
JetBrains · 12.1B · runs from 5.5 GB
Mellum2 12B A2.5B Thinking SFT is a 12.1B-parameter open language model from JetBrains in the Mellum 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.
PrunedHub Qwen3.5 35B A3B 80pct
GOBA-AI-Labs · 35B · runs from 16.4 GB
PrunedHub Qwen3.5 35B A3B 80pct is a 35B-parameter open language model from GOBA-AI-Labs in the Qwen 3.5 family. See its VRAM requirements by quantization and which GPUs and Macs can run it locally below.
Lucie 7B
OpenLLM-France · 6.7B · runs from 3.4 GB
Lucie 7B is a 6.7B-parameter open language model from OpenLLM-France. It supports a context window of up to 32,000 tokens. See its VRAM requirements by quantization and which GPUs and Macs can run it locally below.
Veritas 8B Fact Checker Non Thinking 1.0
resect-ai · 8.2B · runs from 4.1 GB
Veritas 8B Fact Checker Non Thinking 1.0 is a 8.2B-parameter open language model from resect-ai. 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.
Steelman 14B Ada
the-clanker-lover · 14B · runs from 6.5 GB
Steelman 14B Ada is a 14B-parameter open language model from the-clanker-lover. See its VRAM requirements by quantization and which GPUs and Macs can run it locally below.
Granite Switch 4.1 8B Preview
IBM · 9.6B · runs from 19.8 GB
Granite Switch 4.1 8B Preview is a 9.6B-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.
Qwen3.5 9B Gemini 3.1 Pro Reasoning Distill
Jackrong · 9.7B · runs from 4.7 GB
Qwen3.5 9B Gemini 3.1 Pro Reasoning Distill is a 9.7B-parameter open language model from Jackrong 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.
MIST Mini 8B
olaverse · 8.0B · runs from 4.0 GB
MIST Mini 8B is a 8.0B-parameter open language model from olaverse. 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.
Kai 30B Instruct
NoesisLab · 32.8B · runs from 14.8 GB
Kai 30B Instruct is a 32.8B-parameter open language model from NoesisLab. 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.
GPT X2 125M CIx Long Context
reaperdoesntknow · 126M · runs from 0.6 GB
GPT X2 125M CIx Long Context is a 126M-parameter open language model from reaperdoesntknow. 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.
GrepSeek Qwen3.5 9B GRPO
alireza7 · 9.4B · runs from 19.4 GB
GrepSeek Qwen3.5 9B GRPO is a 9.4B-parameter open language model from alireza7 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.
Hunyuan 0.5B Instruct
Tencent · 539M · runs from 0.6 GB
Hunyuan 0.5B Instruct is a 539M-parameter open language model from Tencent in the Hunyuan 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.