# Best local LLMs for 24 GB VRAM | What Can I Run AI? > 423 open models run fully in 24 GB of VRAM at Q4, up to ERNIE-4.5-VL-28B-A3B-PT. See every model that fits, with memory needs. Source: https://whatcanirunai.vercel.app/best-llm-for/24gb-vram/ # Best local LLMs for 24 GB of VRAM Every open model we track that runs fully in 24 GB of GPU memory, biggest first. Checks are at Q4\_K\_M with about 4k context. Run fully at Q4 423 of 656 Run at Q8 too 388 Biggest at Q4 ERNIE-4.5-VL-28B-A3B-PT With 24 GB, the biggest model you can run comfortably is **ERNIE-4.5-VL-28B-A3B-PT** (\~21.5 GB at Q4). If you want higher quality at Q8, **Ling-Coder-lite** is the biggest that fits. ## Models that fit in 24 GB Bar: Q4 memory needed vs. 24 GB. The tick marks 90%, our comfort line.FitsTight | Model | Q4 need vs. 24 GB | Q4 / Q8 | | --------------------------------------------------------------------------------------------------------------------- | ------------------ | ------------------ | | [Raon-1 mini preview](https://whatcanirunai.vercel.app/models/raon-1-mini-preview/)28.8B params, KRAFTON | **21.1** / 24.0 GB | Q4FitsQ8Won’t fit | | [Qwen3.8-27B](https://whatcanirunai.vercel.app/models/qwen3.8-27b/)27.8B params, Qwen | **20.4** / 24.0 GB | Q4FitsQ8Won’t fit | | [Tess-4.5-27B](https://whatcanirunai.vercel.app/models/tess-4.5-27b/)27.8B params, migtissera | **20.4** / 24.0 GB | Q4FitsQ8Won’t fit | | [gemma-3-27b-it](https://whatcanirunai.vercel.app/models/gemma-3-27b-it/)27.4B params, google | **20.1** / 24.0 GB | Q4FitsQ8Won’t fit | | [Gemma 4 26B A4B](https://whatcanirunai.vercel.app/models/gemma-4-26b-a4b-it/)25.8B params, google | **19.0** / 24.0 GB | Q4FitsQ8Won’t fit | | [Mistral-Small-3.2-24B-Instruct-2506](https://whatcanirunai.vercel.app/models/mistral-small-3.2-24b-instruct-2506/)24.0B params, mistralai | **17.7** / 24.0 GB | Q4FitsQ8Won’t fit | | [LFM2.5-24B-A2B](https://whatcanirunai.vercel.app/models/lfm2.5-24b-a2b/)24.0B params, LiquidAI | **17.7** / 24.0 GB | Q4FitsQ8Won’t fit | | [gpt-oss-20b](https://whatcanirunai.vercel.app/models/gpt-oss-20b/)20.9B params, openai | **15.6** / 24.0 GB | Q4FitsQ8Won’t fit | | [Carbon-X (DNA)](https://whatcanirunai.vercel.app/models/carbon-x/)14.8B params, HuggingFaceBio | **11.3** / 24.0 GB | Q4FitsQ8Fits | | [Qwen2.5-Coder-14B-Instruct](https://whatcanirunai.vercel.app/models/qwen2.5-coder-14b-instruct/)14.8B params, Qwen | **11.3** / 24.0 GB | Q4FitsQ8Fits | | [DeepSeek-R1-Distill-Qwen-14B](https://whatcanirunai.vercel.app/models/deepseek-r1-distill-qwen-14b/)14.8B params, deepseek-ai | **11.3** / 24.0 GB | Q4FitsQ8Fits | | [Qwen3-14B](https://whatcanirunai.vercel.app/models/qwen3-14b/)14.8B params, Qwen | **11.3** / 24.0 GB | Q4FitsQ8Fits | | [phi-4](https://whatcanirunai.vercel.app/models/phi-4/)14.7B params, microsoft | **11.2** / 24.0 GB | Q4FitsQ8Fits | | [OLMo-2-1124-13B-Instruct](https://whatcanirunai.vercel.app/models/olmo-2-1124-13b-instruct/)13.7B params, allenai | **10.6** / 24.0 GB | Q4FitsQ8Fits | | [Mistral-Nemo-Instruct-2407](https://whatcanirunai.vercel.app/models/mistral-nemo-instruct-2407/)12.2B params, mistralai | **9.5** / 24.0 GB | Q4FitsQ8Fits | | [gemma-3-12b-it](https://whatcanirunai.vercel.app/models/gemma-3-12b-it/)12.2B params, google | **9.5** / 24.0 GB | Q4FitsQ8Fits | | [Mellum2.1-12B-A2.5B-Thinking](https://whatcanirunai.vercel.app/models/mellum2.1-12b-a2.5b-thinking/)12.1B params, JetBrains | **9.5** / 24.0 GB | Q4FitsQ8Fits | | [Gemma 4 12B](https://whatcanirunai.vercel.app/models/gemma-4-12b-it/)12.0B params, google | **9.3** / 24.0 GB | Q4FitsQ8Fits | | [Ornith-1.5-9B](https://whatcanirunai.vercel.app/models/ornith-1.5-9b/)9.7B params, ornith-ai | **7.7** / 24.0 GB | Q4FitsQ8Fits | | [Qwen3-8B](https://whatcanirunai.vercel.app/models/qwen3-8b/)8.2B params, Qwen | **6.7** / 24.0 GB | Q4FitsQ8Fits | | [granite-3.3-8b-instruct](https://whatcanirunai.vercel.app/models/granite-3.3-8b-instruct/)8.2B params, ibm-granite | **6.7** / 24.0 GB | Q4FitsQ8Fits | | [Llama-3.1-8B-Instruct](https://whatcanirunai.vercel.app/models/llama-3.1-8b-instruct/)8.0B params, meta-llama | **6.6** / 24.0 GB | Q4FitsQ8Fits | | [Nemotron 3.5 Bolt E4B](https://whatcanirunai.vercel.app/models/nemotron-3.5-bolt-e4b/)8.0B params, nvidia | **6.6** / 24.0 GB | Q4FitsQ8Fits | | [Gemma 4 E4B](https://whatcanirunai.vercel.app/models/gemma-4-e4b-it/)8.0B params, google | **6.6** / 24.0 GB | Q4FitsQ8Fits | | [Qwen2.5-Coder-7B-Instruct](https://whatcanirunai.vercel.app/models/qwen2.5-coder-7b-instruct/)7.6B params, Qwen | **6.3** / 24.0 GB | Q4FitsQ8Fits | | [DeepSeek-R1-Distill-Qwen-7B](https://whatcanirunai.vercel.app/models/deepseek-r1-distill-qwen-7b/)7.6B params, deepseek-ai | **6.3** / 24.0 GB | Q4FitsQ8Fits | | [Mistral-7B-Instruct-v0.3](https://whatcanirunai.vercel.app/models/mistral-7b-instruct-v0.3/)7.2B params, mistralai | **6.1** / 24.0 GB | Q4FitsQ8Fits | | [Gemma 4 E2B](https://whatcanirunai.vercel.app/models/gemma-4-e2b-it/)5.1B params, google | **4.6** / 24.0 GB | Q4FitsQ8Fits | | [gemma-3-4b-it](https://whatcanirunai.vercel.app/models/gemma-3-4b-it/)4.3B params, google | **4.0** / 24.0 GB | Q4FitsQ8Fits | | [Spark-X2.6-4B](https://whatcanirunai.vercel.app/models/spark-x2.6-4b/)4.1B params, XHToken | **3.9** / 24.0 GB | Q4FitsQ8Fits | | [Qwen3-4B](https://whatcanirunai.vercel.app/models/qwen3-4b/)4.0B params, Qwen | **3.8** / 24.0 GB | Q4FitsQ8Fits | | [Nemotron Labs Dory 1.0 4B](https://whatcanirunai.vercel.app/models/nemotron-labs-dory1.0-4b/)4.0B params, nvidia | **3.8** / 24.0 GB | Q4FitsQ8Fits | | [Phi-4-mini-instruct](https://whatcanirunai.vercel.app/models/phi-4-mini-instruct/)3.8B params, microsoft | **3.7** / 24.0 GB | Q4FitsQ8Fits | | [Llama-3.2-3B-Instruct](https://whatcanirunai.vercel.app/models/llama-3.2-3b-instruct/)3.2B params, meta-llama | **3.2** / 24.0 GB | Q4FitsQ8Fits | | [MiniCPM5-2B](https://whatcanirunai.vercel.app/models/minicpm5-2b/)2.5B params, openbmb | **2.8** / 24.0 GB | Q4FitsQ8Fits | | [Qwen3-1.7B](https://whatcanirunai.vercel.app/models/qwen3-1.7b/)2.0B params, Qwen | **2.4** / 24.0 GB | Q4FitsQ8Fits | | [LFM3-1.7B](https://whatcanirunai.vercel.app/models/lfm3-1.7b/)1.7B params, LiquidAI | **2.2** / 24.0 GB | Q4FitsQ8Fits | | [Llama-3.2-1B-Instruct](https://whatcanirunai.vercel.app/models/llama-3.2-1b-instruct/)1.2B params, meta-llama | **1.9** / 24.0 GB | Q4FitsQ8Fits | | [gemma-3-1b-it](https://whatcanirunai.vercel.app/models/gemma-3-1b-it/)1.0B params, google | **1.7** / 24.0 GB | Q4FitsQ8Fits | | [Qwen3-0.6B](https://whatcanirunai.vercel.app/models/qwen3-0.6b/)0.8B params, Qwen | **1.5** / 24.0 GB | Q4FitsQ8Fits | | [Qwen2.5-Coder-32B-Instruct](https://whatcanirunai.vercel.app/models/qwen2.5-coder-32b-instruct/)32.8B params, Qwen | **23.8** / 24.0 GB | Q4TightQ8Won’t fit | | [DeepSeek-R1-Distill-Qwen-32B](https://whatcanirunai.vercel.app/models/deepseek-r1-distill-qwen-32b/)32.8B params, deepseek-ai | **23.8** / 24.0 GB | Q4TightQ8Won’t fit | | [Qwen3-32B](https://whatcanirunai.vercel.app/models/qwen3-32b/)32.8B params, Qwen | **23.8** / 24.0 GB | Q4TightQ8Won’t fit | | [NVIDIA-Nemotron-3.5-Lightning-30B-A3B-BF16](https://whatcanirunai.vercel.app/models/nvidia-nemotron-3.5-lightning-30b-a3b-bf16/)31.6B params, nvidia | **23.0** / 24.0 GB | Q4TightQ8Won’t fit | | [Gemma 4 31B](https://whatcanirunai.vercel.app/models/gemma-4-31b-it/)31.3B params, google | **22.8** / 24.0 GB | Q4TightQ8Won’t fit | | [Qwen3-30B-A3B](https://whatcanirunai.vercel.app/models/qwen3-30b-a3b/)30.5B params, Qwen | **22.3** / 24.0 GB | Q4TightQ8Won’t fit | | [Qwen3-Coder-30B-A3B-Instruct](https://whatcanirunai.vercel.app/models/qwen3-coder-30b-a3b-instruct/)30.5B params, Qwen | **22.3** / 24.0 GB | Q4TightQ8Won’t fit | | [Hy-Memory-30B-A3B](https://whatcanirunai.vercel.app/models/hy-memory-30b-a3b/)30.1B params, tencent | **22.0** / 24.0 GB | Q4TightQ8Won’t fit | | [Muse-Glimmer-30B](https://whatcanirunai.vercel.app/models/muse-glimmer-30b/)29.8B params, meta-models | **21.8** / 24.0 GB | Q4TightQ8Won’t fit | Showing the 49 most popular of 472 models that fit. [See all 472 in the model directory →](https://whatcanirunai.vercel.app/models/?gb=24) ## Hardware we track with 24–31 GB usable * [Radeon RX 7900 XTX](https://whatcanirunai.vercel.app/what-can-i-run/rx-7900-xtx/)24 GB usable, $999 * [GeForce RTX 3090](https://whatcanirunai.vercel.app/what-can-i-run/rtx-3090/)24 GB usable, $1499 * [GeForce RTX 4090](https://whatcanirunai.vercel.app/what-can-i-run/rtx-4090/)24 GB usable, $1599 * [Mac mini M6 32GB](https://whatcanirunai.vercel.app/what-can-i-run/mac-mini-m6-32/)24 GB usable, $1799 * [GeForce RTX 3090 Ti](https://whatcanirunai.vercel.app/what-can-i-run/rtx-3090-ti/)24 GB usable, $1999 * [NVIDIA RTX A5000](https://whatcanirunai.vercel.app/what-can-i-run/rtx-a5000/)24 GB usable, $2250 * [MacBook Pro M6 32GB](https://whatcanirunai.vercel.app/what-can-i-run/macbook-pro-m6-32/)24 GB usable, $2399 * [NVIDIA Titan RTX](https://whatcanirunai.vercel.app/what-can-i-run/titan-rtx/)24 GB usable, $2499 * [NVIDIA Tesla P40 24GB](https://whatcanirunai.vercel.app/what-can-i-run/tesla-p40/)24 GB usable Macs and unified-memory mini-PCs count about 75% of memory as usable ([how we calculate](https://whatcanirunai.vercel.app/method/)). ## Quick answers What is the biggest LLM I can run with 24 GB of VRAM? ERNIE-4.5-VL-28B-A3B-PT (29.4B parameters) is the biggest model we track that runs comfortably in 24 GB at Q4\_K\_M, needing about 21.5 GB with a \~4k context. At Q8 the biggest is Ling-Coder-lite. How many models fit in 24 GB of VRAM? 423 of the 656 open models we track run fully in 24 GB at Q4 with room to spare, 388 also fit at Q8, and 49 more fit only with a short context. Can I run a model that needs more than 24 GB? Yes, with llama.cpp or Ollama you can offload some layers to system RAM, but speed drops sharply. For full-speed local inference the whole model plus its context cache should fit in GPU memory; we use 90% of 24 GB as the comfort line. Which hardware has about 24 GB of usable memory? The cheapest current device we track in this range is the Radeon RX 7900 XTX (24 GB usable, $999 list price as of our last snapshot). 9 tracked devices have 24–31 GB usable. ## Other memory sizes [4 GB](https://whatcanirunai.vercel.app/best-llm-for/4gb-vram/) · [6 GB](https://whatcanirunai.vercel.app/best-llm-for/6gb-vram/) · [8 GB](https://whatcanirunai.vercel.app/best-llm-for/8gb-vram/) · [10 GB](https://whatcanirunai.vercel.app/best-llm-for/10gb-vram/) · [12 GB](https://whatcanirunai.vercel.app/best-llm-for/12gb-vram/) · [16 GB](https://whatcanirunai.vercel.app/best-llm-for/16gb-vram/) · [20 GB](https://whatcanirunai.vercel.app/best-llm-for/20gb-vram/) · [32 GB](https://whatcanirunai.vercel.app/best-llm-for/32gb-vram/) · [48 GB](https://whatcanirunai.vercel.app/best-llm-for/48gb-vram/) · [64 GB](https://whatcanirunai.vercel.app/best-llm-for/64gb-vram/) · [96 GB](https://whatcanirunai.vercel.app/best-llm-for/96gb-vram/) · [128 GB](https://whatcanirunai.vercel.app/best-llm-for/128gb-vram/) --- Agent guide: https://whatcanirunai.vercel.app/llms.txt · JSON API: https://whatcanirunai.vercel.app/developers/