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
Qwen3 Coder 30B A3B Instruct
Alibaba · 30.5B · runs from 8.8 GB
564.9K
1.3KQwen3 Coder 30B A3B Instruct is a code-specialized Mixture of Experts (MoE) model from Alibaba Cloud's Qwen 3 Coder series, with 30 billion total parameters and approximately 3 billion active parameters per forward pass. The MoE architecture allows it to deliver strong coding performance while keeping per-token compute costs low, making it faster at inference than comparably capable dense models. The model is instruction-tuned for programming assistance, code generation, debugging, and software engineering conversation. It requires VRAM proportional to its total 30B parameter count for loading weights, but benefits from efficient inference throughput due to its low active parameter count. Released under the Apache 2.0 license.
Qwen3.6 35B A3B
Alibaba · 36.0B · runs from 15.7 GB
Qwen3.6 35B A3B pairs a mixture-of-experts design with roughly 35 billion total parameters, only about 3 billion of which are active per token. That small active footprint keeps generation speed close to a 3B-class dense model, even though the full parameter set must still be held in memory, similar to a dense model of that size, meaning local use still calls for a single high-end consumer GPU once quantized. The model also accepts image input alongside text, extending it to visual question answering and image-grounded chat. It supports a 256K token context window for long documents and extended conversations, and is released under the Apache 2.0 license. Published in April 2026, it gives Qwen 3.6 users a faster-inference alternative to the dense Qwen3.6 27B model released the same generation.
Qwen2.5 7B Instruct
Alibaba · 7.6B · runs from 2.7 GB
Qwen2.5 7B Instruct is a 7.6-billion parameter instruction-tuned model from Alibaba Cloud's Qwen 2.5 series. It supports a 128K token context window and is fine-tuned for conversational AI, instruction following, and general assistant tasks. Its efficient size makes it well-suited for local deployment on consumer GPUs with 8GB or more of VRAM. The model delivers strong performance for its parameter class across reasoning, multilingual understanding, and coding tasks. It benefits from the improved pretraining data and techniques of the Qwen 2.5 generation. Released under the Apache 2.0 license and widely supported by inference frameworks such as llama.cpp, vLLM, and Ollama.
Qwen3 14B
Alibaba · 14.8B · runs from 4.7 GB
Qwen3 14B is a 14-billion parameter instruction-tuned model from Alibaba Cloud's Qwen 3 series. It occupies a practical middle ground in the Qwen 3 lineup, offering stronger reasoning and generation quality than the 8B variant while remaining manageable on GPUs with 16GB or more of VRAM in quantized formats. The model supports hybrid thinking mode for flexible reasoning depth. Qwen3 14B is well suited for chat, instruction following, coding assistance, and multilingual tasks. It benefits from the generational improvements of Qwen 3 in pretraining data and alignment techniques, delivering performance that competes with larger models from previous generations. Released under the Apache 2.0 license.
Qwen3 8B
Alibaba · 8.2B · runs from 2.9 GB
Qwen3 8B is an 8.2-billion parameter instruction-tuned model from Alibaba Cloud's Qwen 3 series. It is a general-purpose chat model that delivers strong performance across reasoning, multilingual understanding, and coding tasks while remaining efficient enough to run on consumer GPUs with 8GB or more of VRAM. Like other Qwen 3 models, it supports hybrid thinking mode for flexible reasoning depth. The model benefits from the improved pretraining data and training methodology of the Qwen 3 generation, offering notable quality gains over Qwen 2.5 at the same parameter count. It is widely supported by inference frameworks including llama.cpp, vLLM, and Ollama. Released under the Apache 2.0 license.
Qwen3.8 27B
Alibaba · 27.8B · runs from 12.6 GB
Alibaba's Qwen3.8 27B is a dense, vision-capable model with around 28 billion parameters, continuing the Qwen line into its 3.8 release. It can process images alongside text prompts, supporting tasks like visual question answering and image-grounded chat in addition to general-purpose reasoning and coding assistance. At this size, the model fits on a single high-end consumer GPU once quantized, putting it within reach of enthusiast local setups rather than requiring server-class hardware. It ships with a 256K token context window, enough for long-form documents or extended multi-turn sessions, and is distributed under the Apache 2.0 license. Released in August 2026, it represents one of the more recent entries in Alibaba's Qwen 3.8 family of open-weight models.
Qwen2.5 Coder 7B Instruct
Alibaba · 7.6B · runs from 3.0 GB
Qwen2.5 Coder 7B Instruct is a 7.6-billion parameter code-specialized instruction-tuned model from Alibaba Cloud. It is trained on a large corpus of source code and natural language, fine-tuned for programming assistance tasks such as code generation, completion, debugging, and code explanation. The model supports a 128K token context window and runs efficiently on consumer GPUs with 8GB or more of VRAM. It provides a good balance between coding capability and hardware requirements for developers looking to run a local coding assistant. Released under the Apache 2.0 license.
Gemma 4 E4B IT
Google · 8.0B · runs from 3.2 GB
Gemma 4 E4B IT packs Google's Gemma 4 architecture into a compact, roughly 8-billion-parameter footprint, positioned as the mid-sized option in Gemma 4's efficiency-focused E-series alongside the smaller E2B variant. It is tuned for chat and instruction-following rather than multimodal input, focusing on dialogue quality within a lightweight package. Parameter counts in this range are well suited to local inference on a single consumer GPU, even at higher precision, and comfortably so once quantized. The model provides a 128K token context window, sufficient for most chat and document-assistance use cases without needing the largest Gemma 4 variants. It is released under the Apache 2.0 license, and was published on March 2, 2026, shortly before the larger dense and mixture-of-experts Gemma 4 models that followed.
Qwen2.5 14B Instruct
Alibaba · 14.8B · runs from 5.1 GB
Qwen2.5 14B Instruct is a 14-billion parameter instruction-tuned model from Alibaba Cloud's Qwen 2.5 series. It supports a 128K token context window and provides a balanced tradeoff between quality and hardware requirements, running well on GPUs with 16GB of VRAM in quantized formats. The model is fine-tuned for chat, instruction following, and general-purpose assistant tasks. It performs well across reasoning, coding, and multilingual benchmarks for its size class, making it a practical option for local deployment when larger models are not feasible. Released under the Apache 2.0 license.
Qwen3.5 9B
Alibaba · 9.7B · runs from 3.2 GB
Qwen3.5-9B is Alibaba's dense 9-billion-parameter language model with a built-in vision encoder, part of the Qwen3.5 generation. This is the post-trained, instruction-tuned release, combining Gated DeltaNet and gated-attention layers for efficient long-context inference, and it can take images alongside text for tasks like visual question answering as well as general chat, coding, and tool use. It offers a 262,144-token native context window, extensible up to roughly 1,010,000 tokens, and is released under the Apache 2.0 license. At around 9.7 billion parameters, 4-bit quantization needs only about 5.6GB of memory, so it fits comfortably on a single mid-range consumer GPU.
Gemma 4 31B IT
Google · 31.3B · runs from 10.2 GB
Gemma 4 31B IT is Google's 31-billion-parameter instruction-tuned model in the Gemma 4 lineup, built to handle both text and image input in a single pass. As a vision-capable model, it can describe, compare, or reason about images alongside written prompts, making it suitable for multimodal chat and document-understanding tasks. At this parameter count, local inference calls for quantization and a fairly capable GPU; it fits on a single high-end consumer or workstation card rather than lower-end hardware. The model supports a 256K token context window, enough for long documents, transcripts, or multi-turn conversations without aggressive truncation. It is released under the Apache 2.0 license, allowing unrestricted commercial and research use, and reflects Google's continued push toward mid-sized, multimodal open-weight models following the earlier Gemma generations.
Qwen2.5 32B Instruct
Alibaba · 32.8B · runs from 9.8 GB
Qwen2.5 32B Instruct is a 32-billion parameter instruction-tuned model from Alibaba Cloud's Qwen 2.5 family. It occupies a practical sweet spot between the 14B and 72B variants, offering strong reasoning and multilingual capabilities while remaining feasible to run on a single high-end consumer GPU with 24GB or more of VRAM at reduced precision. The model supports a 128K token context window and is optimized for conversational use, instruction following, and structured output generation. It is a popular choice for local inference when the 72B model is too demanding but users need more capability than the 14B variant. Released under the Apache 2.0 license.
Qwen3 32B
Alibaba · 32.8B · runs from 9.7 GB
Qwen3 32B is the flagship dense model in Alibaba Cloud's Qwen 3 series, with 32 billion parameters. It is instruction-tuned for chat and delivers strong performance across reasoning, coding, mathematics, and multilingual tasks. Qwen3 32B supports a hybrid thinking mode that allows the model to engage in extended chain-of-thought reasoning or respond quickly depending on the task, giving users flexibility between depth and speed. The model 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. It represents a significant generational improvement over Qwen 2.5 in both instruction following and knowledge breadth. Released under the Apache 2.0 license.
Qwen2.5 Coder 32B Instruct
Alibaba · 32.8B · runs from 9.8 GB
Qwen2.5 Coder 32B Instruct is a 32.8-billion parameter code-specialized model from Alibaba Cloud, instruction-tuned for programming assistance and code generation. It is trained on a large corpus of source code alongside natural language data, making it highly capable for tasks such as code completion, debugging, code explanation, and software engineering dialogue. The model supports a 128K token context window and delivers code generation quality competitive with the best open-weight coding models at any scale. It requires a GPU with at least 24GB of VRAM for quantized inference. Released under the Apache 2.0 license.
Gemma 4 26B A4B IT
Google · 25.8B · runs from 11.6 GB
Gemma 4 26B A4B IT applies a mixture-of-experts design to Google's Gemma 4 family, with roughly 26 billion total parameters but only about 4 billion active for any given token. That active-parameter count is what determines inference speed, so despite its total size the model can respond about as quickly as a much smaller dense model, though the full parameter set still needs to be held in memory, putting local use in single high-end consumer GPU territory once quantized. It also accepts image input alongside text, making it usable for multimodal chat and visual question answering. The model offers a 256K token context window, suited to long documents or extended conversations, and is released under the Apache 2.0 license for unrestricted use. Released alongside the dense 31B variant, it gives developers a faster-inference option within the same Gemma 4 generation.
Qwen3.5 9B The Defiant Fable Uncensored Heretic NEO IMATRIX MAX MTP
DavidAU · 9.7B · runs from 3.8 GB
Qwen3.5 9B The Defiant Fable Uncensored Heretic NEO IMATRIX MAX MTP is a 9.7B-parameter open language model from DavidAU in the Qwen 3.5 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 12B IT Qat Q4 0 Unquantized
Google · 12.0B · runs from 6.1 GB
Gemma 4 12B IT Qat Q4 0 Unquantized is a 12.0B-parameter open language model from Google 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.
Llama 3.1 8B Instruct
Meta · 8.0B · runs from 3.6 GB
Meta Llama 3.1 8B Instruct is an 8-billion parameter instruction-tuned language model from Meta. Part of the Llama 3.1 release, it supports a 128K token context window and is fine-tuned for conversational use, tool calling, and general assistant tasks. Its compact size makes it well-suited for local deployment on modern consumer GPUs with 8GB or more of VRAM. Llama 3.1 8B Instruct delivers strong performance for its parameter class across benchmarks in reasoning, coding, and multilingual understanding. It is released under the Llama 3.1 Community License and is widely supported by inference frameworks such as llama.cpp, vLLM, and Ollama.
NVIDIA Nemotron 3 Nano 30B A3B BF16
NVIDIA · 31.6B · runs from 13.8 GB
NVIDIA Nemotron 3 Nano 30B A3B is a mixture-of-experts model with 31.6 billion total parameters but only around 3 billion active per token, giving it the intelligence of a much larger model with the speed of a small one. This BF16 version preserves full precision for maximum output quality. The MoE architecture makes this model especially interesting for local deployment. You get reasoning and instruction-following capabilities that punch well above what a traditional 3B model can deliver, while inference stays fast because only a fraction of the network fires for each token.
Qwen3 4B
Alibaba · 4.0B · runs from 1.6 GB
Qwen3 4B is a compact 4-billion parameter instruction-tuned model from Alibaba Cloud's Qwen 3 family. It is designed for efficient local inference on consumer hardware, supporting chat and general assistant tasks while fitting comfortably on GPUs with 6GB or more of VRAM in quantized formats. The model supports hybrid thinking mode, allowing it to balance reasoning depth and response speed. Despite its small footprint, Qwen3 4B delivers quality competitive with larger models from previous generations, making it a practical choice for lightweight local deployments and resource-constrained environments. Released under the Apache 2.0 license.
Qwen2.5 Coder 14B Instruct
Alibaba · 14.8B · runs from 5.1 GB
Qwen2.5 Coder 14B Instruct is a 14.8B-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.
Qwen3 4B Instruct 2507
Alibaba · 4.0B · runs from 1.9 GB
Qwen3 4B Instruct 2507 is a July 2025 refresh of Alibaba's compact 4-billion-parameter chat model from the Qwen3 family. This updated release brings improved instruction following and conversational quality while remaining lightweight enough to run on most modern GPUs and even some higher-end integrated graphics setups. With its modest size, the 4B Instruct 2507 strikes a practical balance between capability and resource efficiency. It is well suited for everyday chat, summarization, and light assistant tasks on consumer hardware, making it one of the more accessible entry points into the Qwen3 lineup.
LFM2.5 2.6B
Liquid AI · 2.7B · runs from 1.6 GB
LFM2.5-2.6B is Liquid AI's on-device model in the LFM2.5 family, a hybrid architecture mixing gated short-convolution blocks with grouped-query attention layers, at about 2.7 billion parameters. It is post-trained for agentic workloads, tool use, data extraction, retrieval-augmented generation, and long-context tasks, though the publisher does not recommend it for heavy agentic coding or knowledge-intensive question answering. It supports a 131K token context window and is released under Liquid AI's custom LFM1.0 license. At around 2.7 billion parameters, it runs easily on almost any modern laptop or consumer GPU at 4-bit quantization, and the publisher reports it using well under 2.5 GB of memory during on-device inference.
Qwen3 0.6B
Alibaba · 752M · runs from 0.6 GB
Qwen3 0.6B is the smallest instruction-tuned model in Alibaba Cloud's Qwen 3 family, with approximately 752 million parameters. It is designed for ultra-lightweight deployment where minimal hardware resources are available, running comfortably on virtually any modern GPU or CPU-only setups. The model supports hybrid thinking mode despite its tiny footprint. While limited in reasoning depth compared to larger variants, Qwen3 0.6B handles basic chat, simple summarization, and lightweight instruction following. It is primarily useful for edge deployment, rapid prototyping, and experimentation where model size is a critical constraint. Released under the Apache 2.0 license.
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.
Qwen3 VL 30B A3B Instruct
Alibaba · 31.1B · runs from 13.6 GB
Qwen3 VL 30B A3B Instruct is Alibaba's 31-billion-parameter mixture-of-experts vision-language model in the Qwen 3 lineup, with about 3 billion parameters active per token (the A3B in its name). Because only the active experts run for each token, inference is faster than a similarly sized dense model, while all the weights still need to fit in memory. It handles images and video alongside text, supporting visual question answering, document and chart reading, and multi-image reasoning. At this size, local inference calls for quantization and a single high-end consumer GPU. The model supports a 262K token context window, suited to long documents, video transcripts, or extended visual conversations. It is released under the Apache 2.0 license, allowing unrestricted commercial and research use, and was published in September 2025 as part of Qwen's third-generation vision-language family.
Gemma 4 12B IT
Google · 12.0B · runs from 5.1 GB
Sitting between Gemma 4's compact E-series and its larger 31B sibling, Gemma 4 12B IT is a 12-billion-parameter instruction-tuned model built for general chat and dialogue use. It does not take image input, focusing instead on text-based reasoning, coding help, and conversational tasks. At this parameter count, the model fits comfortably on a single consumer GPU without necessarily requiring the heaviest quantization, making it a reasonable middle-ground choice for local setups. The model supports a generous 256K token context window, useful for long documents or extended sessions, and is released under the Apache 2.0 license. It arrived in May 2026, after the initial March 2026 releases of the Gemma 4 E-series and 31B dense model, suggesting continued iteration within the family after the launch wave.
Muse Glimmer 30B
Meta · 29.8B · runs from 8.7 GB
Muse Glimmer 30B is Meta's 30-billion-parameter dense vision-language model, purpose-built for local, always-on AI agents rather than general chat. It pairs a text decoder with a dedicated image encoder for reasoning over screenshots, charts, and documents, and includes native tool-calling with a separate reasoning channel so it can plan multi-step actions and recover from failures. At this size, local inference calls for quantization and a capable GPU; it fits a single high-end 24-32GB-class card, matching Meta's goal of running it entirely on consumer machines. The model supports a 131K token context window, enough for extended agent sessions. It is released under the Apache 2.0 license, allowing unrestricted commercial and research use. Published in August 2026, it is Meta's first open-weight model since Llama 4, and ships without the Llama licenses' monthly-active-user cap.
Llama 3.2 3B Instruct
Meta · 3.2B · runs from 1.0 GB
Meta Llama 3.2 3B Instruct is a 3-billion parameter instruction-tuned model from Meta's Llama 3.2 release, designed for efficient local inference on resource-constrained hardware. It supports a 128K token context window and is optimized for conversational AI, summarization, and general assistant tasks. Despite its small footprint, Llama 3.2 3B Instruct delivers competitive performance for its size class and can run on GPUs with as little as 4GB of VRAM when quantized. It is released under the Llama 3.2 Community License and is a practical choice for edge deployment and lightweight local inference.
Qwen3 30B A3B Instruct 2507
Alibaba · 30.5B · runs from 13.4 GB
Qwen3 30B A3B Instruct 2507 is a July 2025 updated mixture-of-experts model from Alibaba with 30 billion total parameters but only around 3 billion active during inference. This MoE architecture gives it a remarkably small memory and compute footprint relative to its total parameter count, letting users run a model with broad knowledge on mid-range hardware. The 2507 instruct refresh improves alignment and instruction-following quality over the original release. Because only a fraction of the weights are active at any given time, this model can often run on a single consumer GPU with 8 GB or more of VRAM when quantized, making it an excellent choice for users who want strong chat performance without heavyweight hardware.