# Best local LLMs for 32 GB VRAM | What Can I Run AI? > 504 open models run fully in 32 GB of VRAM at Q4, up to IQuest-Coder-V1-40B-Loop-Instruct. See every model that fits, with memory needs. Source: https://whatcanirunai.vercel.app/best-llm-for/32gb-vram/ # Best local LLMs for 32 GB of VRAM Every open model we track that runs fully in 32 GB of GPU memory, biggest first. Checks are at Q4\_K\_M with about 4k context. Run fully at Q4 504 of 656 Run at Q8 too 398 Biggest at Q4 IQuest-Coder-V1-40B-Loop-Instruct With 32 GB, the biggest model you can run comfortably is **IQuest-Coder-V1-40B-Loop-Instruct** (\~28.7 GB at Q4). If you want higher quality at Q8, **EuroLLM-22B-Instruct-2512** is the biggest that fits. ## Models that fit in 32 GB Bar: Q4 memory needed vs. 32 GB. The tick marks 90%, our comfort line.FitsTight | Model | Q4 need vs. 32 GB | Q4 / Q8 | | --------------------------------------------------------------------------------------------------------------------- | ------------------ | ----------------- | | [Recursive Flint](https://whatcanirunai.vercel.app/models/flint/)39.8B params, RecursiveSI | **28.7** / 32.0 GB | Q4FitsQ8Won’t fit | | [Ornith-1.5-35B-A3B](https://whatcanirunai.vercel.app/models/ornith-1.5-35b-a3b/)36.0B params, ornith-ai | **26.1** / 32.0 GB | Q4FitsQ8Won’t fit | | [Sage-A1.5](https://whatcanirunai.vercel.app/models/sage-a1.5/)36.0B params, InternScience | **26.1** / 32.0 GB | Q4FitsQ8Won’t fit | | [MemT-1.0-Nano](https://whatcanirunai.vercel.app/models/memt-1.0-nano/)35.1B params, MemTensor | **25.5** / 32.0 GB | Q4FitsQ8Won’t fit | | [Nanbeige4.3-33B](https://whatcanirunai.vercel.app/models/nanbeige4.3-33b/)33.1B params, Nanbeige | **24.1** / 32.0 GB | Q4FitsQ8Won’t fit | | [Qwen2.5-Coder-32B-Instruct](https://whatcanirunai.vercel.app/models/qwen2.5-coder-32b-instruct/)32.8B params, Qwen | **23.8** / 32.0 GB | Q4FitsQ8Won’t fit | | [DeepSeek-R1-Distill-Qwen-32B](https://whatcanirunai.vercel.app/models/deepseek-r1-distill-qwen-32b/)32.8B params, deepseek-ai | **23.8** / 32.0 GB | Q4FitsQ8Won’t fit | | [Qwen3-32B](https://whatcanirunai.vercel.app/models/qwen3-32b/)32.8B params, Qwen | **23.8** / 32.0 GB | Q4FitsQ8Won’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** / 32.0 GB | Q4FitsQ8Won’t fit | | [Gemma 4 31B](https://whatcanirunai.vercel.app/models/gemma-4-31b-it/)31.3B params, google | **22.8** / 32.0 GB | Q4FitsQ8Won’t fit | | [Qwen3-30B-A3B](https://whatcanirunai.vercel.app/models/qwen3-30b-a3b/)30.5B params, Qwen | **22.3** / 32.0 GB | Q4FitsQ8Won’t fit | | [Qwen3-Coder-30B-A3B-Instruct](https://whatcanirunai.vercel.app/models/qwen3-coder-30b-a3b-instruct/)30.5B params, Qwen | **22.3** / 32.0 GB | Q4FitsQ8Won’t fit | | [Hy-Memory-30B-A3B](https://whatcanirunai.vercel.app/models/hy-memory-30b-a3b/)30.1B params, tencent | **22.0** / 32.0 GB | Q4FitsQ8Won’t fit | | [Muse-Glimmer-30B](https://whatcanirunai.vercel.app/models/muse-glimmer-30b/)29.8B params, meta-models | **21.8** / 32.0 GB | Q4FitsQ8Won’t fit | | [Raon-1 mini preview](https://whatcanirunai.vercel.app/models/raon-1-mini-preview/)28.8B params, KRAFTON | **21.1** / 32.0 GB | Q4FitsQ8Won’t fit | | [Qwen3.8-27B](https://whatcanirunai.vercel.app/models/qwen3.8-27b/)27.8B params, Qwen | **20.4** / 32.0 GB | Q4FitsQ8Won’t fit | | [Tess-4.5-27B](https://whatcanirunai.vercel.app/models/tess-4.5-27b/)27.8B params, migtissera | **20.4** / 32.0 GB | Q4FitsQ8Won’t fit | | [gemma-3-27b-it](https://whatcanirunai.vercel.app/models/gemma-3-27b-it/)27.4B params, google | **20.1** / 32.0 GB | Q4FitsQ8Won’t fit | | [Gemma 4 26B A4B](https://whatcanirunai.vercel.app/models/gemma-4-26b-a4b-it/)25.8B params, google | **19.0** / 32.0 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** / 32.0 GB | Q4FitsQ8Tight | | [LFM2.5-24B-A2B](https://whatcanirunai.vercel.app/models/lfm2.5-24b-a2b/)24.0B params, LiquidAI | **17.7** / 32.0 GB | Q4FitsQ8Tight | | [gpt-oss-20b](https://whatcanirunai.vercel.app/models/gpt-oss-20b/)20.9B params, openai | **15.6** / 32.0 GB | Q4FitsQ8Fits | | [Carbon-X (DNA)](https://whatcanirunai.vercel.app/models/carbon-x/)14.8B params, HuggingFaceBio | **11.3** / 32.0 GB | Q4FitsQ8Fits | | [Qwen2.5-Coder-14B-Instruct](https://whatcanirunai.vercel.app/models/qwen2.5-coder-14b-instruct/)14.8B params, Qwen | **11.3** / 32.0 GB | Q4FitsQ8Fits | | [DeepSeek-R1-Distill-Qwen-14B](https://whatcanirunai.vercel.app/models/deepseek-r1-distill-qwen-14b/)14.8B params, deepseek-ai | **11.3** / 32.0 GB | Q4FitsQ8Fits | | [Qwen3-14B](https://whatcanirunai.vercel.app/models/qwen3-14b/)14.8B params, Qwen | **11.3** / 32.0 GB | Q4FitsQ8Fits | | [phi-4](https://whatcanirunai.vercel.app/models/phi-4/)14.7B params, microsoft | **11.2** / 32.0 GB | Q4FitsQ8Fits | | [OLMo-2-1124-13B-Instruct](https://whatcanirunai.vercel.app/models/olmo-2-1124-13b-instruct/)13.7B params, allenai | **10.6** / 32.0 GB | Q4FitsQ8Fits | | [Mistral-Nemo-Instruct-2407](https://whatcanirunai.vercel.app/models/mistral-nemo-instruct-2407/)12.2B params, mistralai | **9.5** / 32.0 GB | Q4FitsQ8Fits | | [gemma-3-12b-it](https://whatcanirunai.vercel.app/models/gemma-3-12b-it/)12.2B params, google | **9.5** / 32.0 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** / 32.0 GB | Q4FitsQ8Fits | | [Gemma 4 12B](https://whatcanirunai.vercel.app/models/gemma-4-12b-it/)12.0B params, google | **9.3** / 32.0 GB | Q4FitsQ8Fits | | [Ornith-1.5-9B](https://whatcanirunai.vercel.app/models/ornith-1.5-9b/)9.7B params, ornith-ai | **7.7** / 32.0 GB | Q4FitsQ8Fits | | [Qwen3-8B](https://whatcanirunai.vercel.app/models/qwen3-8b/)8.2B params, Qwen | **6.7** / 32.0 GB | Q4FitsQ8Fits | | [granite-3.3-8b-instruct](https://whatcanirunai.vercel.app/models/granite-3.3-8b-instruct/)8.2B params, ibm-granite | **6.7** / 32.0 GB | Q4FitsQ8Fits | | [Llama-3.1-8B-Instruct](https://whatcanirunai.vercel.app/models/llama-3.1-8b-instruct/)8.0B params, meta-llama | **6.6** / 32.0 GB | Q4FitsQ8Fits | | [Nemotron 3.5 Bolt E4B](https://whatcanirunai.vercel.app/models/nemotron-3.5-bolt-e4b/)8.0B params, nvidia | **6.6** / 32.0 GB | Q4FitsQ8Fits | | [Gemma 4 E4B](https://whatcanirunai.vercel.app/models/gemma-4-e4b-it/)8.0B params, google | **6.6** / 32.0 GB | Q4FitsQ8Fits | | [Qwen2.5-Coder-7B-Instruct](https://whatcanirunai.vercel.app/models/qwen2.5-coder-7b-instruct/)7.6B params, Qwen | **6.3** / 32.0 GB | Q4FitsQ8Fits | | [DeepSeek-R1-Distill-Qwen-7B](https://whatcanirunai.vercel.app/models/deepseek-r1-distill-qwen-7b/)7.6B params, deepseek-ai | **6.3** / 32.0 GB | Q4FitsQ8Fits | | [Mistral-7B-Instruct-v0.3](https://whatcanirunai.vercel.app/models/mistral-7b-instruct-v0.3/)7.2B params, mistralai | **6.1** / 32.0 GB | Q4FitsQ8Fits | | [Gemma 4 E2B](https://whatcanirunai.vercel.app/models/gemma-4-e2b-it/)5.1B params, google | **4.6** / 32.0 GB | Q4FitsQ8Fits | | [gemma-3-4b-it](https://whatcanirunai.vercel.app/models/gemma-3-4b-it/)4.3B params, google | **4.0** / 32.0 GB | Q4FitsQ8Fits | | [Spark-X2.6-4B](https://whatcanirunai.vercel.app/models/spark-x2.6-4b/)4.1B params, XHToken | **3.9** / 32.0 GB | Q4FitsQ8Fits | | [Qwen3-4B](https://whatcanirunai.vercel.app/models/qwen3-4b/)4.0B params, Qwen | **3.8** / 32.0 GB | Q4FitsQ8Fits | | [Nemotron Labs Dory 1.0 4B](https://whatcanirunai.vercel.app/models/nemotron-labs-dory1.0-4b/)4.0B params, nvidia | **3.8** / 32.0 GB | Q4FitsQ8Fits | | [Phi-4-mini-instruct](https://whatcanirunai.vercel.app/models/phi-4-mini-instruct/)3.8B params, microsoft | **3.7** / 32.0 GB | Q4FitsQ8Fits | | [Llama-3.2-3B-Instruct](https://whatcanirunai.vercel.app/models/llama-3.2-3b-instruct/)3.2B params, meta-llama | **3.2** / 32.0 GB | Q4FitsQ8Fits | | [MiniCPM5-2B](https://whatcanirunai.vercel.app/models/minicpm5-2b/)2.5B params, openbmb | **2.8** / 32.0 GB | Q4FitsQ8Fits | | [Qwen3-1.7B](https://whatcanirunai.vercel.app/models/qwen3-1.7b/)2.0B params, Qwen | **2.4** / 32.0 GB | Q4FitsQ8Fits | | [LFM3-1.7B](https://whatcanirunai.vercel.app/models/lfm3-1.7b/)1.7B params, LiquidAI | **2.2** / 32.0 GB | Q4FitsQ8Fits | | [Llama-3.2-1B-Instruct](https://whatcanirunai.vercel.app/models/llama-3.2-1b-instruct/)1.2B params, meta-llama | **1.9** / 32.0 GB | Q4FitsQ8Fits | | [gemma-3-1b-it](https://whatcanirunai.vercel.app/models/gemma-3-1b-it/)1.0B params, google | **1.7** / 32.0 GB | Q4FitsQ8Fits | | [Qwen3-0.6B](https://whatcanirunai.vercel.app/models/qwen3-0.6b/)0.8B params, Qwen | **1.5** / 32.0 GB | Q4FitsQ8Fits | Showing the 54 most popular of 507 models that fit. [See all 507 in the model directory →](https://whatcanirunai.vercel.app/models/?gb=32) ## Hardware we track with 32–47 GB usable * [Intel Arc Pro B70 32GB](https://whatcanirunai.vercel.app/what-can-i-run/arc-pro-b70/)32 GB usable, $949 * [GeForce RTX 5090](https://whatcanirunai.vercel.app/what-can-i-run/rtx-5090/)32 GB usable, $1999 * [NVIDIA Tesla V100 32GB (PCIe)](https://whatcanirunai.vercel.app/what-can-i-run/tesla-v100-32gb/)32 GB usable * [AMD Radeon Instinct MI50 32GB](https://whatcanirunai.vercel.app/what-can-i-run/instinct-mi50-32gb/)32 GB usable * [AMD Radeon Instinct MI60 32GB](https://whatcanirunai.vercel.app/what-can-i-run/instinct-mi60/)32 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 32 GB of VRAM? IQuest-Coder-V1-40B-Loop-Instruct (39.8B parameters) is the biggest model we track that runs comfortably in 32 GB at Q4\_K\_M, needing about 28.7 GB with a \~4k context. At Q8 the biggest is EuroLLM-22B-Instruct-2512. How many models fit in 32 GB of VRAM? 504 of the 656 open models we track run fully in 32 GB at Q4 with room to spare, 398 also fit at Q8, and 3 more fit only with a short context. Can I run a model that needs more than 32 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 32 GB as the comfort line. Which hardware has about 32 GB of usable memory? The cheapest current device we track in this range is the Intel Arc Pro B70 32GB (32 GB usable, $949 list price as of our last snapshot). 5 tracked devices have 32–47 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/) · [24 GB](https://whatcanirunai.vercel.app/best-llm-for/24gb-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/