All LLM Models
Browse 529 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
Mistral 7B Instruct v0.2
Mistral AI · 7.2B · runs from 3.6 GB
1.4M
3.2KMistral 7B Instruct v0.2 is a 7.2B-parameter open language model from Mistral AI in the Mistral 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.
TinyLlama 1.1B Chat v1.0
TinyLlama · 1.1B · runs from 0.8 GB
TinyLlama 1.1B Chat is a 1.1-billion parameter chat model built on the Llama 2 architecture and trained on approximately 3 trillion tokens, an unusually large dataset for a model of its size. The TinyLlama project demonstrated that small models can achieve strong performance when given sufficient training compute, making it a standout in the sub-2B parameter class. The Chat variant is fine-tuned for conversational use and runs on virtually any modern GPU, including entry-level cards with 4GB of VRAM or less. It is a practical choice for lightweight local inference, edge deployment, and experimentation where hardware resources are limited.
Mistral 7B Instruct v0.3
Mistral AI · 7.2B · runs from 2.7 GB
Mistral 7B Instruct v0.3 is the latest instruction-tuned release of Mistral AI's original 7-billion-parameter model, delivering meaningful improvements in instruction following, function calling, and multilingual support over its predecessors. With an extended 32K-token vocabulary and refined chat capabilities, v0.3 remains one of the most capable sub-10B models available. At 7.2 billion parameters it sits comfortably in the sweet spot for local inference, running well on GPUs with 6–8 GB of VRAM at full precision and even on 4 GB cards with 4-bit quantization. It is an excellent default choice for anyone getting started with local LLMs who wants strong conversational performance without heavy hardware.
Mistral Nemo Instruct 2407
Mistral AI · 12.2B · runs from 4.8 GB
Mistral Nemo Instruct 2407 is a 12.2B-parameter open language model from Mistral AI in the Mistral 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.
Phi 4
Microsoft · 14.7B · runs from 5.1 GB
Microsoft Phi 4 is a 14-billion parameter language model from Microsoft Research's Phi series, designed to deliver strong reasoning, mathematical, and coding performance at an efficient size. Phi 4 continues the Phi family's focus on maximizing capability per parameter through high-quality training data curation, achieving benchmark scores that rival much larger models on reasoning and STEM tasks. The model runs well on consumer GPUs with 12-16GB of VRAM in quantized formats. It excels at mathematical problem solving, code generation, and structured reasoning. Released under the MIT license.
Mistral Small 24B Instruct 2501
Mistral AI · 23.6B · runs from 7.8 GB
Mistral Small 24B Instruct is Mistral AI's January 2025 release targeting the mid-range parameter sweet spot. At 24 billion parameters it sits between lightweight 7B models and heavier 70B-class offerings, delivering strong instruction-following, reasoning, and coding performance without demanding top-tier hardware. This model fits comfortably on a single GPU with 16–24 GB of VRAM at common quantization levels, making it an attractive option for users with cards like the RTX 4090 or RTX 3090 who want a noticeable step up from 7B models. It strikes an appealing balance between quality and resource requirements for serious local use.
Gemma 4 E4B IT Qat Q4 0 Unquantized
Google · 7.9B · runs from 3.9 GB
Gemma 4 E4B IT Qat Q4 0 Unquantized is a 7.9B-parameter open language model from Google 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.
DeepSeek R1 Distill Qwen 14B
DeepSeek · 14.8B · runs from 5.1 GB
DeepSeek R1 Distill Qwen 14B sits in a sweet spot between the smaller 7B distill and the more demanding 32B version, offering strong reasoning performance at 14.8 billion parameters on the Qwen 2.5 architecture. It captures a meaningful share of the full R1's chain-of-thought capabilities while keeping resource requirements within the range of mainstream consumer GPUs. Quantized to 4-bit, it fits comfortably on GPUs with 12 GB of VRAM, delivering reliable step-by-step reasoning for math, logic, and analytical problems.
Gemma 3 270M IT
Google · 268M · runs from 0.1 GB
Google Gemma 3 270M IT is a 270-million parameter instruction-tuned model from Google's Gemma 3 family, an experimental release pushing the boundaries of how small an effective chat model can be. The model runs on virtually any hardware, including entry-level GPUs and CPU-only setups, making it useful for experimentation, education, and exploring the limits of small-scale language modeling. Released under the Gemma license.
LFM2.5 8B A1B
LiquidAI · 8.5B · runs from 2.7 GB
LFM2.5 8B A1B is a 8.5B-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.
Gemma 4 E2B IT Qat Q4 0 Unquantized
Google · 5.1B · runs from 2.5 GB
Gemma 4 E2B IT Qat Q4 0 Unquantized is a 5.1B-parameter open language model from Google 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.
SmolLM2 135M Instruct
Hugging Face · 135M · runs from 0.4 GB
SmolLM2 135M Instruct is the instruction-tuned variant of Hugging Face's 135-million-parameter SmolLM2 model. Fine-tuned to follow user prompts and engage in basic conversational exchanges, it delivers surprisingly coherent responses given its minimal size, making it ideal for testing chat interfaces or running on extremely constrained devices. This model is a practical choice when you need an instruction-following model that fits comfortably in under 1 GB of memory. It works well for simple question answering, text reformatting, and lightweight assistant tasks where response quality can be traded for instant inference speed.
Qwen2.5 Coder 3B Instruct
Alibaba · 3.1B · runs from 1.4 GB
Qwen2.5 Coder 3B Instruct is a 3.1B-parameter open language model from Alibaba in the Qwen 2.5 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.
DeepSeek R1 Distill Qwen 1.5B
DeepSeek · 1.8B · runs from 0.8 GB
DeepSeek R1 Distill Qwen 1.5B is the smallest model in the R1 distillation family, packing chain-of-thought reasoning capabilities into just 1.5 billion parameters using the Qwen 2.5 architecture. It represents an ambitious attempt to bring structured reasoning to the smallest practical model size. At this scale, the model can run on virtually any modern GPU and even on CPU-only setups with acceptable speed. While its reasoning depth is naturally limited compared to its larger siblings, it still demonstrates structured thinking patterns that set it apart from generic models of similar size.
GLM 4.6V Flash
zai-org · 10.3B · runs from 3.2 GB
GLM 4.6V Flash is a 10.3B-parameter open language model from zai-org in the GLM 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.
Qwen3 4B Thinking 2507
Alibaba · 4.0B · runs from 1.6 GB
Qwen3 4B Thinking 2507 is the reasoning-optimized variant of Alibaba's compact 4-billion-parameter Qwen3 model, released in the July 2025 update cycle. Despite its small size, this thinking variant is tuned to produce chain-of-thought reasoning and step-by-step problem solving, making it a surprisingly capable lightweight reasoner. This model is ideal for users who want basic reasoning and analytical capabilities on very modest hardware. It can run on most consumer GPUs and even some CPU-only setups when quantized, providing an accessible entry point for experimenting with reasoning-style models without any significant hardware investment.
Gemma 4 E4B IT Ultra Uncensored Heretic
llmfan46 · 8.0B · runs from 3.9 GB
Gemma 4 E4B IT Ultra Uncensored Heretic is a 8.0B-parameter open language model from llmfan46 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.
Phi 4 Mini Reasoning
Microsoft · 3.8B · runs from 1.6 GB
Phi 4 Mini Reasoning is a 3.8B-parameter open language model from Microsoft in the Phi 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.
DeepSeek R1 Distill Qwen 7B
DeepSeek · 7.6B · runs from 3.0 GB
DeepSeek R1 Distill Qwen 7B compresses the reasoning techniques from DeepSeek's full R1 model into a compact 7.6 billion parameter dense model built on the Qwen 2.5 architecture. Despite its small footprint, it demonstrates surprisingly capable step-by-step reasoning on math and logic problems that would stump many models several times its size. This is one of the most accessible reasoning models available for local use, fitting comfortably on GPUs with 6 GB or more of VRAM when quantized. It strikes a practical balance between genuine chain-of-thought reasoning ability and the hardware constraints of a typical consumer setup.
DeepSeek R1 Distill Llama 8B
DeepSeek · 8.0B · runs from 2.8 GB
DeepSeek R1 Distill Llama 8B brings R1's reinforcement-learned reasoning capabilities to the widely supported Llama 3.1 8B architecture. By distilling the full 684.5B R1 model's reasoning patterns into this 8 billion parameter dense model, DeepSeek created a version that benefits from the extensive Llama ecosystem of tools, quantizations, and inference engines. For users who prefer the Llama architecture or already have tooling built around it, this model offers a plug-and-play path to chain-of-thought reasoning. Its hardware requirements are very approachable, running well on consumer GPUs with 8 GB or more of VRAM at common quantization levels.
SmolLM2 1.7B Instruct
Hugging Face · 1.7B · runs from 1.4 GB
SmolLM2 1.7B Instruct is the largest instruction-tuned model in the SmolLM2 family, offering the best balance of capability and efficiency Hugging Face achieved with this generation. At 1.7 billion parameters it produces substantially more coherent and useful responses than its smaller siblings, handling multi-turn conversations, summarization, and simple reasoning tasks with competence. With VRAM requirements well under 4 GB at standard precision, this model runs effortlessly on entry-level GPUs, older laptops, and even some mobile devices. It is an excellent choice for developers building lightweight local assistants or chatbots who want genuine conversational quality without the hardware demands of larger models.
Qwen2.5 Coder 1.5B Instruct
Alibaba · 1.5B · runs from 1.0 GB
Qwen2.5 Coder 1.5B Instruct is a 1.5B-parameter open language model from Alibaba in the Qwen 2.5 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.
Mistral Small 3.2 24B Instruct 2506
Mistral AI · 24.0B · runs from 7.3 GB
Mistral Small 3.2 24B Instruct 2506 is a 24.0B-parameter open language model from Mistral AI in the Mistral 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.
MN 12B Mag Mell R1
inflatebot · 12.2B · runs from 4.1 GB
MN 12B Mag Mell R1 is a 12.2B-parameter open language model from inflatebot. It supports a context window of up to 1,024,000 tokens. See its VRAM requirements by quantization and which GPUs and Macs can run it locally below.
LFM2.5 1.2B Instruct
LiquidAI · 1.2B · runs from 0.8 GB
LFM2.5 1.2B Instruct is an instruction-tuned model from Liquid AI that uses a novel hybrid architecture combining state-space models with attention mechanisms. At just 1.2 billion parameters, it is exceptionally lightweight and can run on virtually any hardware, including laptops and edge devices. Liquid AI's unconventional architecture aims to deliver better efficiency and longer context handling than traditional transformer models at this scale, making it an interesting option for users exploring alternatives to standard transformer-based LLMs.
Qwen3.6 12B IQ Ultra Heretic Uncensored Thinking v2 Hightop
DavidAU · 12.1B · runs from 5.6 GB
Qwen3.6 12B IQ Ultra Heretic Uncensored Thinking v2 Hightop is a 12.1B-parameter open language model from DavidAU in the Qwen 3.6 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.
Mistral 7B Instruct v0.1
Mistral AI · 7B · runs from 3.5 GB
Mistral 7B Instruct v0.1 was the first instruction-tuned variant of the original Mistral 7B, fine-tuned for conversational and instruction-following tasks. While it has since been superseded by v0.2 and v0.3, it remains a solid lightweight chat model and an important milestone in the open-weight model ecosystem. Its hardware requirements are identical to the base Mistral 7B, running smoothly on GPUs with as little as 6 GB of VRAM when quantized. Users seeking the best Mistral 7B experience should generally prefer the newer v0.3 release, but v0.1 is still useful for reproducibility and benchmarking purposes.
Deepseek Coder 6.7B Instruct
DeepSeek · 6.7B · runs from 4.2 GB
DeepSeek Coder 6.7B Instruct is a first-generation code-specialized model trained on a large corpus of source code and programming-related data. At 6.7 billion parameters, it provides solid code completion, generation, and explanation capabilities across popular programming languages while remaining small enough to run on most consumer GPUs. While newer models in the DeepSeek lineup have surpassed it in raw capability, this model remains a practical choice for users who need a lightweight local coding assistant with minimal hardware requirements. It runs well on GPUs with as little as 6 GB of VRAM when quantized.
GLM 4.7 Flash REAP 23B A3B
Cerebras · 23.0B · runs from 7.4 GB
GLM 4.7 Flash REAP 23B A3B is a 23.0B-parameter open language model from Cerebras in the GLM 4 family. It supports a context window of up to 202,752 tokens. See its VRAM requirements by quantization and which GPUs and Macs can run it locally below.
LFM2 24B A2B
LiquidAI · 23.8B · runs from 7.0 GB
LFM2 24B A2B is a 23.8B-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.