# Best local LLMs for 48 GB VRAM | What Can I Run AI? > 511 open models run fully in 48 GB of VRAM at Q4, up to AI21-Jamba2-Mini. See every model that fits, with memory needs. Source: https://whatcanirunai.vercel.app/best-llm-for/48gb-vram/ # Best local LLMs for 48 GB of VRAM Every open model we track that runs fully in 48 GB of GPU memory, biggest first. Checks are at Q4\_K\_M with about 4k context. Run fully at Q4 511 of 656 Run at Q8 too 482 Biggest at Q4 AI21-Jamba2-Mini With 48 GB, the biggest model you can run comfortably is **AI21-Jamba2-Mini** (\~37.0 GB at Q4). If you want higher quality at Q8, **EXAONE-4.5-33B** is the biggest that fits. ## Models that fit in 48 GB Bar: Q4 memory needed vs. 48 GB. The tick marks 90%, our comfort line.FitsTight | Model | Q4 need vs. 48 GB | Q4 / Q8 | | --------------------------------------------------------------------------------------------------------------------- | ------------------ | ----------------- | | [Recursive Flint](https://whatcanirunai.vercel.app/models/flint/)39.8B params, RecursiveSI | **28.7** / 48.0 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** / 48.0 GB | Q4FitsQ8Tight | | [Sage-A1.5](https://whatcanirunai.vercel.app/models/sage-a1.5/)36.0B params, InternScience | **26.1** / 48.0 GB | Q4FitsQ8Tight | | [MemT-1.0-Nano](https://whatcanirunai.vercel.app/models/memt-1.0-nano/)35.1B params, MemTensor | **25.5** / 48.0 GB | Q4FitsQ8Tight | | [Nanbeige4.3-33B](https://whatcanirunai.vercel.app/models/nanbeige4.3-33b/)33.1B params, Nanbeige | **24.1** / 48.0 GB | Q4FitsQ8Fits | | [Qwen2.5-Coder-32B-Instruct](https://whatcanirunai.vercel.app/models/qwen2.5-coder-32b-instruct/)32.8B params, Qwen | **23.8** / 48.0 GB | Q4FitsQ8Fits | | [DeepSeek-R1-Distill-Qwen-32B](https://whatcanirunai.vercel.app/models/deepseek-r1-distill-qwen-32b/)32.8B params, deepseek-ai | **23.8** / 48.0 GB | Q4FitsQ8Fits | | [Qwen3-32B](https://whatcanirunai.vercel.app/models/qwen3-32b/)32.8B params, Qwen | **23.8** / 48.0 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** / 48.0 GB | Q4FitsQ8Fits | | [Gemma 4 31B](https://whatcanirunai.vercel.app/models/gemma-4-31b-it/)31.3B params, google | **22.8** / 48.0 GB | Q4FitsQ8Fits | | [Qwen3-30B-A3B](https://whatcanirunai.vercel.app/models/qwen3-30b-a3b/)30.5B params, Qwen | **22.3** / 48.0 GB | Q4FitsQ8Fits | | [Qwen3-Coder-30B-A3B-Instruct](https://whatcanirunai.vercel.app/models/qwen3-coder-30b-a3b-instruct/)30.5B params, Qwen | **22.3** / 48.0 GB | Q4FitsQ8Fits | | [Hy-Memory-30B-A3B](https://whatcanirunai.vercel.app/models/hy-memory-30b-a3b/)30.1B params, tencent | **22.0** / 48.0 GB | Q4FitsQ8Fits | | [Muse-Glimmer-30B](https://whatcanirunai.vercel.app/models/muse-glimmer-30b/)29.8B params, meta-models | **21.8** / 48.0 GB | Q4FitsQ8Fits | | [Raon-1 mini preview](https://whatcanirunai.vercel.app/models/raon-1-mini-preview/)28.8B params, KRAFTON | **21.1** / 48.0 GB | Q4FitsQ8Fits | | [Qwen3.8-27B](https://whatcanirunai.vercel.app/models/qwen3.8-27b/)27.8B params, Qwen | **20.4** / 48.0 GB | Q4FitsQ8Fits | | [Tess-4.5-27B](https://whatcanirunai.vercel.app/models/tess-4.5-27b/)27.8B params, migtissera | **20.4** / 48.0 GB | Q4FitsQ8Fits | | [gemma-3-27b-it](https://whatcanirunai.vercel.app/models/gemma-3-27b-it/)27.4B params, google | **20.1** / 48.0 GB | Q4FitsQ8Fits | | [Gemma 4 26B A4B](https://whatcanirunai.vercel.app/models/gemma-4-26b-a4b-it/)25.8B params, google | **19.0** / 48.0 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** / 48.0 GB | Q4FitsQ8Fits | | [LFM2.5-24B-A2B](https://whatcanirunai.vercel.app/models/lfm2.5-24b-a2b/)24.0B params, LiquidAI | **17.7** / 48.0 GB | Q4FitsQ8Fits | | [gpt-oss-20b](https://whatcanirunai.vercel.app/models/gpt-oss-20b/)20.9B params, openai | **15.6** / 48.0 GB | Q4FitsQ8Fits | | [Carbon-X (DNA)](https://whatcanirunai.vercel.app/models/carbon-x/)14.8B params, HuggingFaceBio | **11.3** / 48.0 GB | Q4FitsQ8Fits | | [Qwen2.5-Coder-14B-Instruct](https://whatcanirunai.vercel.app/models/qwen2.5-coder-14b-instruct/)14.8B params, Qwen | **11.3** / 48.0 GB | Q4FitsQ8Fits | | [DeepSeek-R1-Distill-Qwen-14B](https://whatcanirunai.vercel.app/models/deepseek-r1-distill-qwen-14b/)14.8B params, deepseek-ai | **11.3** / 48.0 GB | Q4FitsQ8Fits | | [Qwen3-14B](https://whatcanirunai.vercel.app/models/qwen3-14b/)14.8B params, Qwen | **11.3** / 48.0 GB | Q4FitsQ8Fits | | [phi-4](https://whatcanirunai.vercel.app/models/phi-4/)14.7B params, microsoft | **11.2** / 48.0 GB | Q4FitsQ8Fits | | [OLMo-2-1124-13B-Instruct](https://whatcanirunai.vercel.app/models/olmo-2-1124-13b-instruct/)13.7B params, allenai | **10.6** / 48.0 GB | Q4FitsQ8Fits | | [Mistral-Nemo-Instruct-2407](https://whatcanirunai.vercel.app/models/mistral-nemo-instruct-2407/)12.2B params, mistralai | **9.5** / 48.0 GB | Q4FitsQ8Fits | | [gemma-3-12b-it](https://whatcanirunai.vercel.app/models/gemma-3-12b-it/)12.2B params, google | **9.5** / 48.0 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** / 48.0 GB | Q4FitsQ8Fits | | [Gemma 4 12B](https://whatcanirunai.vercel.app/models/gemma-4-12b-it/)12.0B params, google | **9.3** / 48.0 GB | Q4FitsQ8Fits | | [Ornith-1.5-9B](https://whatcanirunai.vercel.app/models/ornith-1.5-9b/)9.7B params, ornith-ai | **7.7** / 48.0 GB | Q4FitsQ8Fits | | [Qwen3-8B](https://whatcanirunai.vercel.app/models/qwen3-8b/)8.2B params, Qwen | **6.7** / 48.0 GB | Q4FitsQ8Fits | | [granite-3.3-8b-instruct](https://whatcanirunai.vercel.app/models/granite-3.3-8b-instruct/)8.2B params, ibm-granite | **6.7** / 48.0 GB | Q4FitsQ8Fits | | [Llama-3.1-8B-Instruct](https://whatcanirunai.vercel.app/models/llama-3.1-8b-instruct/)8.0B params, meta-llama | **6.6** / 48.0 GB | Q4FitsQ8Fits | | [Nemotron 3.5 Bolt E4B](https://whatcanirunai.vercel.app/models/nemotron-3.5-bolt-e4b/)8.0B params, nvidia | **6.6** / 48.0 GB | Q4FitsQ8Fits | | [Gemma 4 E4B](https://whatcanirunai.vercel.app/models/gemma-4-e4b-it/)8.0B params, google | **6.6** / 48.0 GB | Q4FitsQ8Fits | | [Qwen2.5-Coder-7B-Instruct](https://whatcanirunai.vercel.app/models/qwen2.5-coder-7b-instruct/)7.6B params, Qwen | **6.3** / 48.0 GB | Q4FitsQ8Fits | | [DeepSeek-R1-Distill-Qwen-7B](https://whatcanirunai.vercel.app/models/deepseek-r1-distill-qwen-7b/)7.6B params, deepseek-ai | **6.3** / 48.0 GB | Q4FitsQ8Fits | | [Mistral-7B-Instruct-v0.3](https://whatcanirunai.vercel.app/models/mistral-7b-instruct-v0.3/)7.2B params, mistralai | **6.1** / 48.0 GB | Q4FitsQ8Fits | | [Gemma 4 E2B](https://whatcanirunai.vercel.app/models/gemma-4-e2b-it/)5.1B params, google | **4.6** / 48.0 GB | Q4FitsQ8Fits | | [gemma-3-4b-it](https://whatcanirunai.vercel.app/models/gemma-3-4b-it/)4.3B params, google | **4.0** / 48.0 GB | Q4FitsQ8Fits | | [Spark-X2.6-4B](https://whatcanirunai.vercel.app/models/spark-x2.6-4b/)4.1B params, XHToken | **3.9** / 48.0 GB | Q4FitsQ8Fits | | [Qwen3-4B](https://whatcanirunai.vercel.app/models/qwen3-4b/)4.0B params, Qwen | **3.8** / 48.0 GB | Q4FitsQ8Fits | | [Nemotron Labs Dory 1.0 4B](https://whatcanirunai.vercel.app/models/nemotron-labs-dory1.0-4b/)4.0B params, nvidia | **3.8** / 48.0 GB | Q4FitsQ8Fits | | [Phi-4-mini-instruct](https://whatcanirunai.vercel.app/models/phi-4-mini-instruct/)3.8B params, microsoft | **3.7** / 48.0 GB | Q4FitsQ8Fits | | [Llama-3.2-3B-Instruct](https://whatcanirunai.vercel.app/models/llama-3.2-3b-instruct/)3.2B params, meta-llama | **3.2** / 48.0 GB | Q4FitsQ8Fits | | [MiniCPM5-2B](https://whatcanirunai.vercel.app/models/minicpm5-2b/)2.5B params, openbmb | **2.8** / 48.0 GB | Q4FitsQ8Fits | | [Qwen3-1.7B](https://whatcanirunai.vercel.app/models/qwen3-1.7b/)2.0B params, Qwen | **2.4** / 48.0 GB | Q4FitsQ8Fits | | [LFM3-1.7B](https://whatcanirunai.vercel.app/models/lfm3-1.7b/)1.7B params, LiquidAI | **2.2** / 48.0 GB | Q4FitsQ8Fits | | [Llama-3.2-1B-Instruct](https://whatcanirunai.vercel.app/models/llama-3.2-1b-instruct/)1.2B params, meta-llama | **1.9** / 48.0 GB | Q4FitsQ8Fits | | [gemma-3-1b-it](https://whatcanirunai.vercel.app/models/gemma-3-1b-it/)1.0B params, google | **1.7** / 48.0 GB | Q4FitsQ8Fits | | [Qwen3-0.6B](https://whatcanirunai.vercel.app/models/qwen3-0.6b/)0.8B params, Qwen | **1.5** / 48.0 GB | Q4FitsQ8Fits | Showing the 54 most popular of 511 models that fit. [See all 511 in the model directory →](https://whatcanirunai.vercel.app/models/?gb=48) ## Hardware we track with 48–63 GB usable * [Mac mini M5 Pro 64GB](https://whatcanirunai.vercel.app/what-can-i-run/mac-mini-m5-pro-64/)48 GB usable, $3199 * [NVIDIA RTX A6000](https://whatcanirunai.vercel.app/what-can-i-run/rtx-a6000/)48 GB usable, $4649 * [NVIDIA DGX Spark 64GB](https://whatcanirunai.vercel.app/what-can-i-run/dgx-spark-64/)48 GB usable, $4999 * [NVIDIA RTX 6000 Ada 48GB](https://whatcanirunai.vercel.app/what-can-i-run/rtx-6000-ada/)48 GB usable, $6799 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 48 GB of VRAM? AI21-Jamba2-Mini (51.6B parameters) is the biggest model we track that runs comfortably in 48 GB at Q4\_K\_M, needing about 37.0 GB with a \~4k context. At Q8 the biggest is EXAONE-4.5-33B. How many models fit in 48 GB of VRAM? 511 of the 656 open models we track run fully in 48 GB at Q4 with room to spare, 482 also fit at Q8. Can I run a model that needs more than 48 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 48 GB as the comfort line. Which hardware has about 48 GB of usable memory? The cheapest current device we track in this range is the Mac mini M5 Pro 64GB (48 GB usable, $3,199 list price as of our last snapshot). 4 tracked devices have 48–63 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/) · [32 GB](https://whatcanirunai.vercel.app/best-llm-for/32gb-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/