All LLM Models
Browse 51 LLM models with VRAM requirements, quantization options, and hardware compatibility.
Model List
Mistral NeMo Minitron 8B Instruct
NVIDIA · 8.4B · runs from 6.7 GB at Q4
46.6K
86Mistral NeMo Minitron 8B Instruct is a 8.4B-parameter open language model from NVIDIA in the Mistral 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.
NVIDIA Nemotron 3 Nano 30B A3B Base BF16
NVIDIA · 31.6B · runs from 24.6 GB at Q4
NVIDIA Nemotron 3 Nano 30B A3B Base BF16 is the foundation model version of the Nemotron 3 Nano 30B, offered in full BF16 precision. Unlike the chat-tuned variants, this base model hasn't been instruction-tuned, making it suitable for fine-tuning, research, or custom alignment workflows. At 31.6 billion total parameters with a mixture-of-experts architecture, the base model gives developers and researchers a strong starting point for building specialized applications. It retains all the architectural benefits of the MoE design while leaving the behavioral layer open for customization.
Nemotron Cascade 8B
NVIDIA · 8B · runs from 7.5 GB at Q4
Nemotron Cascade 8B is a 8B-parameter open language model from NVIDIA in the Nemotron 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.
Nemotron Labs Diffusion 14B
NVIDIA · 13.5B · runs from 11.8 GB at Q4
Nemotron Labs Diffusion 14B is a 13.5B-parameter open language model from NVIDIA in the Nemotron 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.
OpenMath Nemotron 1.5B
NVIDIA · 1.5B · runs from 1.7 GB at Q4
OpenMath Nemotron 1.5B is a 1.5B-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.
Nemotron Research Reasoning Qwen 1.5B
NVIDIA · 1.8B · runs from 1.8 GB at Q4
Nemotron Research Reasoning Qwen 1.5B is a 1.8B-parameter open language model from NVIDIA in the Qwen 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.
Nemotron Labs Audex 2B
NVIDIA · 2B · runs from 3.1 GB at Q4
Nemotron Labs Audex 2B is a 2B-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.
Nemotron Terminal 8B
NVIDIA · 8.2B · runs from 7.6 GB at Q4
Nemotron Terminal 8B is a 8.2B-parameter open language model from NVIDIA in the Nemotron 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.
Nemotron Content Safety Reasoning 4B
NVIDIA · 4.3B · runs from 3.6 GB at Q4
Nemotron Content Safety Reasoning 4B is a 4.3B-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.
Kimi K2.6 DFlash
NVIDIA · 3.5B · runs from 2.7 GB at Q4
Kimi K2.6 DFlash is a 3.5B-parameter open language model from NVIDIA in the Kimi K2 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.
Llama 3.1 Nemotron Safety Guard 8B v3
NVIDIA · 8.0B · runs from 7.3 GB at Q4
Llama 3.1 Nemotron Safety Guard 8B v3 is a 8.0B-parameter open language model from NVIDIA in the Llama 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.
Kimi K2.7 Code DFlash
NVIDIA · 3.5B · runs from 2.7 GB at Q4
Kimi K2.7 Code DFlash is a 3.5B-parameter open language model from NVIDIA in the Kimi K2 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.
Nemotron Terminal 32B
NVIDIA · 32.8B · runs from 22.6 GB at Q4
Nemotron Terminal 32B is a 32.8B-parameter open language model from NVIDIA in the Nemotron 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.
OpenReasoning Nemotron 32B
NVIDIA · 32.8B · runs from 24.3 GB at Q4
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.
Nemotron H 8B Reasoning 128K
NVIDIA · 8.1B · runs from 7.9 GB at Q4
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.
Kimi K2.6 Eagle3
NVIDIA · 1.8B · runs from 1.9 GB at Q4
Kimi K2.6 Eagle3 is a 1.8B-parameter open language model from NVIDIA in the Kimi K2 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.
Nemotron Terminal 14B
NVIDIA · 14.8B · runs from 11.8 GB at Q4
Nemotron Terminal 14B is a 14.8B-parameter open language model from NVIDIA in the Nemotron 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.
Nemotron Flash 3B
NVIDIA · 2.7B · runs from 3.7 GB at Q4
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.
OpenCodeReasoning Nemotron 1.1 32B
NVIDIA · 32.8B · runs from 24.3 GB at Q4
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.
Riva Translate 4B Instruct
NVIDIA · 4.2B · runs from 3.7 GB at Q4
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.
Llama 3.3 Nemotron 70B Reward
NVIDIA · 70.6B · runs from 48 GB at Q4
Llama 3.3 Nemotron 70B Reward is a 70.6B-parameter open language model from NVIDIA in the Llama 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.
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.