All LLM Models

Browse 719 LLM models with VRAM requirements, quantization options, and hardware compatibility.

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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.

Ternary Bonsai 4B Unpacked

prism-ml · 4.0B · runs from 2.2 GB

Ternary Bonsai 4B Unpacked is a 4.0B-parameter open language model from prism-ml. 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 1.7B Base

Alibaba · 1.7B · runs from 1.0 GB

Qwen3 1.7B Base is a 1.7-billion parameter pretrained foundation model from Alibaba Cloud's Qwen 3 family. It is a compact base model designed for fine-tuning, research, and custom applications rather than direct conversational use. Its small size makes it accessible for resource-constrained fine-tuning and rapid experimentation. The model can run on virtually any modern GPU and benefits from the improved pretraining data of the Qwen 3 generation. It is suitable as a lightweight foundation for domain-specific fine-tunes and student models in distillation pipelines. Released under the Apache 2.0 license.

NVIDIA Nemotron Nano 9B v2

NVIDIA · 8.9B · runs from 4.5 GB

NVIDIA Nemotron Nano 9B v2 is a compact yet capable chat model from NVIDIA, packing 8.9 billion parameters into a size that runs comfortably on a wide range of consumer GPUs. Built on NVIDIA's Nemotron architecture, it delivers strong instruction-following and conversational performance while keeping VRAM requirements modest. This second-generation Nano model reflects NVIDIA's push to make high-quality language models accessible on local hardware. It's an excellent starting point for users who want a responsive, general-purpose assistant without needing top-tier GPU memory.

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.

Falcon 40B Instruct

TII UAE · 40B · runs from 12.1 GB

Falcon 40B Instruct is a 40B-parameter open language model from TII UAE in the Falcon family. See its VRAM requirements by quantization and which GPUs and Macs can run it locally below.

Yi 6B

01.AI · 6.1B · runs from 2.9 GB

Yi 6B is a 6.1B-parameter open language model from 01.AI in the Yi 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.

TinyLlama 1.1B Intermediate Step 1431k 3T

TinyLlama · 1.1B · runs from 0.8 GB

TinyLlama 1.1B Intermediate Step 1431k 3T is a 1.1B-parameter open language model from TinyLlama in the TinyLlama family. 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.

Gemma 2B

Google · 2.5B · runs from 1.2 GB

Gemma 2B 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.

Llama 3 3 Nemotron Super 49B V1 5

NVIDIA · 49.9B · runs from 15.1 GB

Llama 3.3 Nemotron Super 49B is a 49.9-billion parameter chat model by NVIDIA, built on a modified Llama 3.3 architecture. It occupies a unique size point between the common 70B and 8B tiers, offering strong reasoning and conversational ability while requiring less VRAM than full 70B models. NVIDIA's Nemotron Super training pipeline applies extensive alignment tuning to optimize helpfulness and factual accuracy. The model typically needs 32GB or more of VRAM for local inference at reduced precision, placing it within reach of high-end consumer GPUs like the RTX 4090 or professional workstation cards.

DeepSeek v2 Lite Chat

DeepSeek · 15.7B · runs from 5.1 GB

DeepSeek v2 Lite Chat is a 15.7B-parameter open language model from DeepSeek in the DeepSeek V2 family. It supports a context window of up to 163,840 tokens. See its VRAM requirements by quantization and which GPUs and Macs can run it locally below.

Gemma 2 9B

Google · 9.2B · runs from 4.2 GB

Gemma 2 9B is a 9.2B-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.

Qwen2.5 7B

Alibaba · 7.6B · runs from 3.6 GB

Qwen2.5 7B is a 7.6B-parameter open language model from Alibaba in the Qwen 2.5 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.

EXAONE 4.0 1.2B

LGAI-EXAONE · 1.3B · runs from 1.0 GB

EXAONE 4.0 1.2B is a 1.3B-parameter open language model from LGAI-EXAONE in the EXAONE 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.

LFM2 1.2B RAG

LiquidAI · 1.2B · runs from 0.9 GB

LFM2 1.2B RAG is a 1.2B-parameter open language model from LiquidAI. 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.

Yi 34B

01.AI · 34.4B · runs from 15.0 GB

Yi 34B is a 34.4B-parameter open language model from 01.AI in the Yi 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.

Racka 4B

elte-nlp · 4.0B · runs from 1.9 GB

Racka 4B is a 4.0B-parameter open language model from elte-nlp. See its VRAM requirements by quantization and which GPUs and Macs can run it locally below.

SmolLM 135M

Hugging Face · 135M · runs from 0.4 GB

SmolLM 135M is the original first-generation small language model from Hugging Face, designed to push the boundaries of what is achievable at extremely low parameter counts. With just 135 million parameters, it was a pioneering effort in making capable language models accessible on the most resource-constrained hardware. While the SmolLM2 and SmolLM3 families have since surpassed it in quality, the original SmolLM 135M remains a useful reference point for research and a practical option for ultra-lightweight deployment scenarios where every megabyte of memory counts.

Supra 50M Reasoning

SupraLabs · 52M · runs from 0.3 GB

Supra 50M Reasoning is a 52M-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.

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.

Yi 1.5 34B Chat

01.AI · 34.4B · runs from 12.4 GB

Yi 1.5 34B Chat is a 34.4-billion parameter instruction-tuned model by 01.AI, the Chinese AI lab founded by Kai-Fu Lee. It is a bilingual model with strong performance in both English and Chinese, making it particularly well suited for users who need high-quality generation in either language. Yi 1.5 represents an improved iteration of the Yi model family with enhanced reasoning and coding ability. The 34B size requires a GPU with at least 24GB of VRAM for quantized inference, placing it within reach of high-end consumer cards like the RTX 4090. Released under the Yi License.

Qwen2.5 14B

Alibaba · 14.8B · runs from 6.8 GB

Qwen2.5 14B is a 14.8B-parameter open language model from Alibaba in the Qwen 2.5 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 4B Base

Alibaba · 4.0B · runs from 2.2 GB

Qwen3 4B Base is a 4.0B-parameter open language model from Alibaba in the Qwen 3 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.

Gemma 4 E2B IT Qat Q4 0 Unquantized Heretic

coder3101 · 5.1B · runs from 2.5 GB

Gemma 4 E2B IT Qat Q4 0 Unquantized Heretic is a 5.1B-parameter open language model from coder3101 in the Gemma 4 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.

Qwen2.5 32B

Alibaba · 32.8B · runs from 14.3 GB

Qwen2.5 32B is a 32.8B-parameter open language model from Alibaba in the Qwen 2.5 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.

Eurus 2 7B PRIME

PRIME-RL · 7.6B · runs from 3.0 GB

Eurus 2 7B PRIME is a 7.6B-parameter open language model from PRIME-RL. 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.

Saiga Llama3 8B

IlyaGusev · 8.0B · runs from 4.0 GB

Saiga Llama3 8B is a 8.0B-parameter open language model from IlyaGusev 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.

Jan v3 4B Base Instruct

janhq · 4.4B · runs from 2.0 GB

Jan v3 4B Base Instruct is a 4.4B-parameter open language model from janhq. 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.

Sqlcoder 7B 2

defog · 6.7B · runs from 4.2 GB

SQLCoder 7B 2 is a 6.7-billion-parameter model from Defog, purpose-built for converting natural-language questions into SQL queries. Fine-tuned specifically on text-to-SQL tasks, it consistently outperforms much larger general-purpose models when the job is generating accurate, executable SQL against real database schemas. For developers and data analysts who regularly query databases, running SQLCoder locally means fast, private SQL generation without sending proprietary schema details to an external API. It works best when provided with table definitions as context and is particularly strong on PostgreSQL, MySQL, and SQLite dialects.

Llama 2 7B Chat HF

Meta · 6.7B · runs from 3.1 GB

Meta Llama 2 7B Chat is a 7-billion parameter instruction-tuned model from Meta's Llama 2 family, optimized for dialogue use cases. It was fine-tuned using supervised fine-tuning and RLHF on top of the Llama 2 7B base model, with a 4K token context window. This model is suitable for basic conversational AI tasks and runs efficiently on consumer GPUs. While newer Llama generations offer improved performance, Llama 2 7B Chat remains a well-understood and widely-supported option for local inference. Released under the Llama 2 Community License.

Yi 6B Chat

01.AI · 6.1B · runs from 2.9 GB

Yi 6B Chat is a 6.1B-parameter open language model from 01.AI in the Yi 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.