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
Gemma 7B
Google · 8.5B · runs from 18.8 GB
28.1K
3.4KGoogle Gemma 7B is a 7-billion parameter base (pretrained) model from the original Gemma generation, Google's first openly available family of language models. It represents Google's initial entry into the open-weight LLM space. While superseded by Gemma 2 and Gemma 3 in terms of benchmark performance, the original Gemma 7B remains a solid foundation model and a useful reference point in the evolution of Google's open models. Released under the Gemma license.
CodeLlama 34B HF
Meta · 33.7B · runs from 15.0 GB
CodeLlama 34B HF is a 33.7B-parameter open language model from Meta in the Code Llama 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.
Gemma 2B IT
Google · 2.5B · runs from 1.2 GB
Gemma 2B IT is a 2.5B-parameter open language model from Google in the Gemma 2 family. See its VRAM requirements by quantization and which GPUs and Macs can run it locally below.
Baichuan 7B
baichuan-inc · 7B · runs from 15.4 GB
Baichuan-7B is Baichuan Intelligent Technology's open-source base pretrained language model, not instruction-tuned, with 7 billion parameters trained on around 1.2 trillion bilingual Chinese-English tokens. It follows a LLaMA-like Transformer design with rotary position embeddings, SwiGLU feedforward layers, and RMSNorm pre-normalization, and was positioned at release as state of the art among models of its size on the Chinese C-Eval and English MMLU benchmarks. Its 7B size makes it easy to run on a single consumer GPU. Context length is 4,096 tokens. It is released under the custom Baichuan-7B license, which is more permissive than LLaMA's original license and explicitly allows commercial use. It was published in June 2023, as one of the earliest fully open Chinese-English base models; Baichuan later released larger and instruction-tuned successors.
Ouro 1.4B
ByteDance · 1.4B · runs from 3.6 GB
Ouro 1.4B is a 1.4B-parameter open language model from ByteDance. 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.
Huginn 0125
tomg-group-umd · 3.9B · runs from 8.6 GB
Huginn 0125 is a 3.9B-parameter open language model from tomg-group-umd. See its VRAM requirements by quantization and which GPUs and Macs can run it locally below.
Granite 4.2 30B
IBM · 29.3B · runs from 13.3 GB
Granite 4.2 30B is a 29.3B-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.
Deepseek Moe 16B Chat
DeepSeek · 16.4B · runs from 7.7 GB
Deepseek Moe 16B Chat is a 16.4B-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.
Olmo Hybrid 7B
Allen AI · 7B · runs from 15.3 GB
Olmo Hybrid 7B is a 7B-parameter open language model from Allen AI in the OLMo 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.
MiniMax M3 EAGLE3
Inferact · 3.3B · runs from 1.7 GB
MiniMax 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.