# Best local LLMs for 64 GB VRAM | What Can I Run AI? > 525 open models run fully in 64 GB of VRAM at Q4, up to Hunyuan-A13B-Instruct. See every model that fits, with memory needs. Source: https://whatcanirunai.vercel.app/best-llm-for/64gb-vram/ # Best local LLMs for 64 GB of VRAM Every open model we track that runs fully in 64 GB of GPU memory, biggest first. Checks are at Q4\_K\_M with about 4k context. Run fully at Q4 525 of 656 Run at Q8 too 507 Biggest at Q4 Hunyuan-A13B-Instruct With 64 GB, the biggest model you can run comfortably is **Hunyuan-A13B-Instruct** (\~57.0 GB at Q4). If you want higher quality at Q8, **Phi-3.5-MoE-instruct** is the biggest that fits. ## Models that fit in 64 GB Bar: Q4 memory needed vs. 64 GB. The tick marks 90%, our comfort line.FitsTight | Model | Q4 need vs. 64 GB | Q4 / Q8 | | --------------------------------------------------------------------------------------------------------------------- | ------------------ | ----------------- | | [Kolibri-1](https://whatcanirunai.vercel.app/models/kolibri-1/)78.1B params, Aleph-Alpha | **55.5** / 64.0 GB | Q4FitsQ8Won’t fit | | [Qwen2.5-72B-Instruct](https://whatcanirunai.vercel.app/models/qwen2.5-72b-instruct/)72.7B params, Qwen | **51.7** / 64.0 GB | Q4FitsQ8Won’t fit | | [Llama-3.3-70B-Instruct](https://whatcanirunai.vercel.app/models/llama-3.3-70b-instruct/)70.6B params, meta-llama | **50.2** / 64.0 GB | Q4FitsQ8Won’t fit | | [DeepSeek-R1-Distill-Llama-70B](https://whatcanirunai.vercel.app/models/deepseek-r1-distill-llama-70b/)70.6B params, deepseek-ai | **50.2** / 64.0 GB | Q4FitsQ8Won’t fit | | [Recursive Flint](https://whatcanirunai.vercel.app/models/flint/)39.8B params, RecursiveSI | **28.7** / 64.0 GB | Q4FitsQ8Fits | | [Ornith-1.5-35B-A3B](https://whatcanirunai.vercel.app/models/ornith-1.5-35b-a3b/)36.0B params, ornith-ai | **26.1** / 64.0 GB | Q4FitsQ8Fits | | [Sage-A1.5](https://whatcanirunai.vercel.app/models/sage-a1.5/)36.0B params, InternScience | **26.1** / 64.0 GB | Q4FitsQ8Fits | | [MemT-1.0-Nano](https://whatcanirunai.vercel.app/models/memt-1.0-nano/)35.1B params, MemTensor | **25.5** / 64.0 GB | Q4FitsQ8Fits | | [Nanbeige4.3-33B](https://whatcanirunai.vercel.app/models/nanbeige4.3-33b/)33.1B params, Nanbeige | **24.1** / 64.0 GB | Q4FitsQ8Fits | | [Qwen2.5-Coder-32B-Instruct](https://whatcanirunai.vercel.app/models/qwen2.5-coder-32b-instruct/)32.8B params, Qwen | **23.8** / 64.0 GB | Q4FitsQ8Fits | | [DeepSeek-R1-Distill-Qwen-32B](https://whatcanirunai.vercel.app/models/deepseek-r1-distill-qwen-32b/)32.8B params, deepseek-ai | **23.8** / 64.0 GB | Q4FitsQ8Fits | | [Qwen3-32B](https://whatcanirunai.vercel.app/models/qwen3-32b/)32.8B params, Qwen | **23.8** / 64.0 GB | Q4FitsQ8Fits | | [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** / 64.0 GB | Q4FitsQ8Fits | | [Gemma 4 31B](https://whatcanirunai.vercel.app/models/gemma-4-31b-it/)31.3B params, google | **22.8** / 64.0 GB | Q4FitsQ8Fits | | [Qwen3-30B-A3B](https://whatcanirunai.vercel.app/models/qwen3-30b-a3b/)30.5B params, Qwen | **22.3** / 64.0 GB | Q4FitsQ8Fits | | [Qwen3-Coder-30B-A3B-Instruct](https://whatcanirunai.vercel.app/models/qwen3-coder-30b-a3b-instruct/)30.5B params, Qwen | **22.3** / 64.0 GB | Q4FitsQ8Fits | | [Hy-Memory-30B-A3B](https://whatcanirunai.vercel.app/models/hy-memory-30b-a3b/)30.1B params, tencent | **22.0** / 64.0 GB | Q4FitsQ8Fits | | [Muse-Glimmer-30B](https://whatcanirunai.vercel.app/models/muse-glimmer-30b/)29.8B params, meta-models | **21.8** / 64.0 GB | Q4FitsQ8Fits | | [Raon-1 mini preview](https://whatcanirunai.vercel.app/models/raon-1-mini-preview/)28.8B params, KRAFTON | **21.1** / 64.0 GB | Q4FitsQ8Fits | | [Qwen3.8-27B](https://whatcanirunai.vercel.app/models/qwen3.8-27b/)27.8B params, Qwen | **20.4** / 64.0 GB | Q4FitsQ8Fits | | [Tess-4.5-27B](https://whatcanirunai.vercel.app/models/tess-4.5-27b/)27.8B params, migtissera | **20.4** / 64.0 GB | Q4FitsQ8Fits | | [gemma-3-27b-it](https://whatcanirunai.vercel.app/models/gemma-3-27b-it/)27.4B params, google | **20.1** / 64.0 GB | Q4FitsQ8Fits | | [Gemma 4 26B A4B](https://whatcanirunai.vercel.app/models/gemma-4-26b-a4b-it/)25.8B params, google | **19.0** / 64.0 GB | Q4FitsQ8Fits | | [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** / 64.0 GB | Q4FitsQ8Fits | | [LFM2.5-24B-A2B](https://whatcanirunai.vercel.app/models/lfm2.5-24b-a2b/)24.0B params, LiquidAI | **17.7** / 64.0 GB | Q4FitsQ8Fits | | [gpt-oss-20b](https://whatcanirunai.vercel.app/models/gpt-oss-20b/)20.9B params, openai | **15.6** / 64.0 GB | Q4FitsQ8Fits | | [Carbon-X (DNA)](https://whatcanirunai.vercel.app/models/carbon-x/)14.8B params, HuggingFaceBio | **11.3** / 64.0 GB | Q4FitsQ8Fits | | [Qwen2.5-Coder-14B-Instruct](https://whatcanirunai.vercel.app/models/qwen2.5-coder-14b-instruct/)14.8B params, Qwen | **11.3** / 64.0 GB | Q4FitsQ8Fits | | [DeepSeek-R1-Distill-Qwen-14B](https://whatcanirunai.vercel.app/models/deepseek-r1-distill-qwen-14b/)14.8B params, deepseek-ai | **11.3** / 64.0 GB | Q4FitsQ8Fits | | [Qwen3-14B](https://whatcanirunai.vercel.app/models/qwen3-14b/)14.8B params, Qwen | **11.3** / 64.0 GB | Q4FitsQ8Fits | | [phi-4](https://whatcanirunai.vercel.app/models/phi-4/)14.7B params, microsoft | **11.2** / 64.0 GB | Q4FitsQ8Fits | | [OLMo-2-1124-13B-Instruct](https://whatcanirunai.vercel.app/models/olmo-2-1124-13b-instruct/)13.7B params, allenai | **10.6** / 64.0 GB | Q4FitsQ8Fits | | [Mistral-Nemo-Instruct-2407](https://whatcanirunai.vercel.app/models/mistral-nemo-instruct-2407/)12.2B params, mistralai | **9.5** / 64.0 GB | Q4FitsQ8Fits | | [gemma-3-12b-it](https://whatcanirunai.vercel.app/models/gemma-3-12b-it/)12.2B params, google | **9.5** / 64.0 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** / 64.0 GB | Q4FitsQ8Fits | | [Gemma 4 12B](https://whatcanirunai.vercel.app/models/gemma-4-12b-it/)12.0B params, google | **9.3** / 64.0 GB | Q4FitsQ8Fits | | [Ornith-1.5-9B](https://whatcanirunai.vercel.app/models/ornith-1.5-9b/)9.7B params, ornith-ai | **7.7** / 64.0 GB | Q4FitsQ8Fits | | [Qwen3-8B](https://whatcanirunai.vercel.app/models/qwen3-8b/)8.2B params, Qwen | **6.7** / 64.0 GB | Q4FitsQ8Fits | | [granite-3.3-8b-instruct](https://whatcanirunai.vercel.app/models/granite-3.3-8b-instruct/)8.2B params, ibm-granite | **6.7** / 64.0 GB | Q4FitsQ8Fits | | [Llama-3.1-8B-Instruct](https://whatcanirunai.vercel.app/models/llama-3.1-8b-instruct/)8.0B params, meta-llama | **6.6** / 64.0 GB | Q4FitsQ8Fits | | [Nemotron 3.5 Bolt E4B](https://whatcanirunai.vercel.app/models/nemotron-3.5-bolt-e4b/)8.0B params, nvidia | **6.6** / 64.0 GB | Q4FitsQ8Fits | | [Gemma 4 E4B](https://whatcanirunai.vercel.app/models/gemma-4-e4b-it/)8.0B params, google | **6.6** / 64.0 GB | Q4FitsQ8Fits | | [Qwen2.5-Coder-7B-Instruct](https://whatcanirunai.vercel.app/models/qwen2.5-coder-7b-instruct/)7.6B params, Qwen | **6.3** / 64.0 GB | Q4FitsQ8Fits | | [DeepSeek-R1-Distill-Qwen-7B](https://whatcanirunai.vercel.app/models/deepseek-r1-distill-qwen-7b/)7.6B params, deepseek-ai | **6.3** / 64.0 GB | Q4FitsQ8Fits | | [Mistral-7B-Instruct-v0.3](https://whatcanirunai.vercel.app/models/mistral-7b-instruct-v0.3/)7.2B params, mistralai | **6.1** / 64.0 GB | Q4FitsQ8Fits | | [Gemma 4 E2B](https://whatcanirunai.vercel.app/models/gemma-4-e2b-it/)5.1B params, google | **4.6** / 64.0 GB | Q4FitsQ8Fits | | [gemma-3-4b-it](https://whatcanirunai.vercel.app/models/gemma-3-4b-it/)4.3B params, google | **4.0** / 64.0 GB | Q4FitsQ8Fits | | [Spark-X2.6-4B](https://whatcanirunai.vercel.app/models/spark-x2.6-4b/)4.1B params, XHToken | **3.9** / 64.0 GB | Q4FitsQ8Fits | | [Qwen3-4B](https://whatcanirunai.vercel.app/models/qwen3-4b/)4.0B params, Qwen | **3.8** / 64.0 GB | Q4FitsQ8Fits | | [Nemotron Labs Dory 1.0 4B](https://whatcanirunai.vercel.app/models/nemotron-labs-dory1.0-4b/)4.0B params, nvidia | **3.8** / 64.0 GB | Q4FitsQ8Fits | | [Phi-4-mini-instruct](https://whatcanirunai.vercel.app/models/phi-4-mini-instruct/)3.8B params, microsoft | **3.7** / 64.0 GB | Q4FitsQ8Fits | | [Llama-3.2-3B-Instruct](https://whatcanirunai.vercel.app/models/llama-3.2-3b-instruct/)3.2B params, meta-llama | **3.2** / 64.0 GB | Q4FitsQ8Fits | | [MiniCPM5-2B](https://whatcanirunai.vercel.app/models/minicpm5-2b/)2.5B params, openbmb | **2.8** / 64.0 GB | Q4FitsQ8Fits | | [Qwen3-1.7B](https://whatcanirunai.vercel.app/models/qwen3-1.7b/)2.0B params, Qwen | **2.4** / 64.0 GB | Q4FitsQ8Fits | | [LFM3-1.7B](https://whatcanirunai.vercel.app/models/lfm3-1.7b/)1.7B params, LiquidAI | **2.2** / 64.0 GB | Q4FitsQ8Fits | | [Llama-3.2-1B-Instruct](https://whatcanirunai.vercel.app/models/llama-3.2-1b-instruct/)1.2B params, meta-llama | **1.9** / 64.0 GB | Q4FitsQ8Fits | | [gemma-3-1b-it](https://whatcanirunai.vercel.app/models/gemma-3-1b-it/)1.0B params, google | **1.7** / 64.0 GB | Q4FitsQ8Fits | | [Qwen3-0.6B](https://whatcanirunai.vercel.app/models/qwen3-0.6b/)0.8B params, Qwen | **1.5** / 64.0 GB | Q4FitsQ8Fits | Showing the 58 most popular of 528 models that fit. [See all 528 in the model directory →](https://whatcanirunai.vercel.app/models/?gb=64) ## Quick answers What is the biggest LLM I can run with 64 GB of VRAM? Hunyuan-A13B-Instruct (80.4B parameters) is the biggest model we track that runs comfortably in 64 GB at Q4\_K\_M, needing about 57.0 GB with a \~4k context. At Q8 the biggest is Phi-3.5-MoE-instruct. How many models fit in 64 GB of VRAM? 525 of the 656 open models we track run fully in 64 GB at Q4 with room to spare, 507 also fit at Q8, and 3 more fit only with a short context. Can I run a model that needs more than 64 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 64 GB as the comfort line. ## 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/) · [32 GB](https://whatcanirunai.vercel.app/best-llm-for/32gb-vram/) · [48 GB](https://whatcanirunai.vercel.app/best-llm-for/48gb-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/