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
MiniMax M3 EAGLE3
Inferact · 3.3B · runs from 1.7 GB
23.5K
14MiniMax M3 EAGLE3 is a 3.3B-parameter open language model from Inferact in the MiniMax family. 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.
Functiongemma 270M IT
Google · 268M · runs from 0.6 GB
Functiongemma 270M IT is a 268M-parameter open language model from Google in the Gemma family. See its VRAM requirements by quantization and which GPUs and Macs can run it locally below.
Deepseek Llm 7B Chat
DeepSeek · 7B · runs from 4.3 GB
Deepseek Llm 7B Chat is a 7B-parameter open language model from DeepSeek in the DeepSeek 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.
Phi 3 Small 8k Instruct
Microsoft · 7.4B · runs from 15.3 GB
Phi-3-Small-8K-Instruct is Microsoft's 7-billion-parameter instruction-tuned chat model from the Phi-3 family, trained on a mix of synthetic data and heavily filtered public web text chosen for high reasoning density, then post-trained with supervised fine-tuning and direct preference optimization. It targets memory- and compute-constrained, latency-sensitive deployments while still emphasizing strong code, math, and logical reasoning, and Microsoft reports it holds up well against same-size and next-size-up models on common-sense, language, math, code, and long-context benchmarks. It sits alongside Mini and Medium-sized Phi-3 variants, plus a 128K-context Small sibling. At around 7 billion parameters it runs comfortably on a single consumer GPU. Context length is 8,192 tokens, as the name indicates. It is released under the MIT license, permitting unrestricted commercial and research use, and was published in May 2024.
Starcoder
BigCode · 15.8B · runs from 7.4 GB
Starcoder is a 15.8B-parameter open language model from BigCode in the StarCoder family. See its VRAM requirements by quantization and which GPUs and Macs can run it locally below.
Tongyi DeepResearch 30B A3B
Alibaba-NLP · 30.5B · runs from 13.4 GB
Tongyi DeepResearch 30B A3B is a 30.5B-parameter open language model from Alibaba-NLP. 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.
LFM2 350M
Liquid AI · 354M · runs from 0.5 GB
LFM2 350M is a 354M-parameter open language model from Liquid AI in the LFM2 family. 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.
K2 Horizon 0.9B
IFM · 1.1B · runs from 2.5 GB
K2 Horizon 0.9B is a 1.1B-parameter open language model from IFM. 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.
LFM2.5 1.2B Base
Liquid AI · 1.2B · runs from 0.9 GB
LFM2.5 1.2B Base is a 1.2B-parameter open language model from Liquid AI in the LFM2.5 family. 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.
MoziAI 35B A3B MOE MTP
chenyumo · 35B · runs from 15.3 GB
MoziAI 35B A3B MOE MTP is a 35B-parameter open language model from chenyumo. 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.
Qwen1.5 1.8B
Alibaba · 1.8B · runs from 1.5 GB
Qwen1.5 1.8B is a 1.8B-parameter open language model from Alibaba in the Qwen 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.
Qwen3.8 9B
empero-ai · 9.7B · runs from 4.7 GB
Qwen3.8 9B is a 9.7B-parameter open language model from empero-ai 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.
TinyDolphin 2.8 1.1B
QuixiAI · 1.1B · runs from 0.8 GB
TinyDolphin 2.8 1.1B is a 1.1B-parameter open language model from QuixiAI in the Phi 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.
Bitnet B1.58 2B 4T
Microsoft · 850M · runs from 2.2 GB
Bitnet B1.58 2B 4T is a 850M-parameter open language model from Microsoft. 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.
Foundation Sec 8B Reasoning
fdtn-ai · 8.0B · runs from 4.0 GB
Foundation Sec 8B Reasoning 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.
HRM Text 1B
sapientinc · 1.2B · runs from 2.9 GB
HRM Text 1B is a 1.2B-parameter open language model from sapientinc. 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.
Gemma 3 12B IT Heretic v2
DreamFast · 12.2B · runs from 6.2 GB
Gemma 3 12B IT Heretic v2 is a 12.2B-parameter open language model from DreamFast in the Gemma 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.
Qwythos 9B Claude Mythos 5 1M
empero-ai · 9.4B · runs from 4.6 GB
Qwythos 9B Claude Mythos 5 1M is a 9.4B-parameter open language model from empero-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.
GPT Neo 2.7B
EleutherAI · 2.7B · runs from 6.0 GB
GPT Neo 2.7B is a 2.7B-parameter open language model from EleutherAI. 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.
KAT Coder V2.5 Dev 35B A3B ABLITERATED UNCENSORED PHILADELPHIA CLASS
KridgeDookie · 34.7B · runs from 15.1 GB
KAT Coder V2.5 Dev 35B A3B ABLITERATED UNCENSORED PHILADELPHIA CLASS is a 34.7B-parameter open language model from KridgeDookie in the Phi 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.
YanoljaNEXT EEVE Instruct 10.8B
yanolja · 10.8B · runs from 5.3 GB
YanoljaNEXT EEVE Instruct 10.8B is a 10.8B-parameter open language model from yanolja. 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.
MagicPrompt Stable Diffusion
Gustavosta · 137M · runs from 0.1 GB
MagicPrompt Stable Diffusion is a 137M-parameter open language model from Gustavosta. 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.
Qwen1.5 4B
Alibaba · 4.0B · runs from 2.8 GB
Qwen1.5 4B is a 4.0B-parameter open language model from Alibaba in the Qwen 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.
Zeta 2.1
zed-industries · 8.3B · runs from 4.1 GB
Zeta 2.1 is a 8.3B-parameter open language model from zed-industries. 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.
Gemma 4 26B A4B IT DFlash
z-lab · 26B · runs from 11.4 GB
Gemma 4 26B A4B IT DFlash is a 26B-parameter open language model from z-lab in the Gemma 4 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 4 31B IT DFlash
z-lab · 31B · runs from 13.5 GB
Gemma 4 31B IT DFlash is a 31B-parameter open language model from z-lab in the Gemma 4 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.
Qwen3 4B Z Image Engineer V4
BennyDaBall · 4B · runs from 1.9 GB
Qwen3 4B Z Image Engineer V4 is a 4B-parameter open language model from BennyDaBall in the Qwen 3 family. See its VRAM requirements by quantization and which GPUs and Macs can run it locally below.
Unsloth Ornith 1.5 35B A3B
peculiar-ragdoll · 35B · runs from 14.9 GB
Unsloth Ornith 1.5 35B A3B is a 35B-parameter open language model from peculiar-ragdoll in the Ornith family. See its VRAM requirements by quantization and which GPUs and Macs can run it locally below.
Qwen3.8 9B Distill
empero-ai · 9.7B · runs from 4.7 GB
Qwen3.8 9B Distill is a 9.7B-parameter open language model from empero-ai 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.
LocoOperator 4B
LocoreMind · 4.0B · runs from 2.2 GB
LocoOperator 4B is a 4.0B-parameter open language model from LocoreMind. 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.