# Best local LLMs for 10 GB VRAM | What Can I Run AI? > 340 open models run fully in 10 GB of VRAM at Q4, up to HyperCLOVAX-SEED-Omni-8B. See every model that fits, with memory needs. Source: https://whatcanirunai.vercel.app/best-llm-for/10gb-vram/ # Best local LLMs for 10 GB of VRAM Every open model we track that runs fully in 10 GB of GPU memory, biggest first. Checks are at Q4\_K\_M with about 4k context. Run fully at Q4 340 of 656 Run at Q8 too 198 Biggest at Q4 HyperCLOVAX-SEED-Omni-8B With 10 GB, the biggest model you can run comfortably is **HyperCLOVAX-SEED-Omni-8B** (\~8.5 GB at Q4). If you want higher quality at Q8, **YuE-s1-7B-anneal-en-cot** is the biggest that fits. ## Models that fit in 10 GB Bar: Q4 memory needed vs. 10 GB. The tick marks 90%, our comfort line.FitsTight | Model | Q4 need vs. 10 GB | Q4 / Q8 | | ----------------------------------------------------------------------------------------------- | ----------------- | ------------------ | | [Ornith-1.5-9B](https://whatcanirunai.vercel.app/models/ornith-1.5-9b/)9.7B params, ornith-ai | **7.7** / 10.0 GB | Q4FitsQ8Won’t fit | | [Qwen3.5-9B](https://whatcanirunai.vercel.app/models/qwen3.5-9b/)9.7B params, Qwen | **7.7** / 10.0 GB | Q4FitsQ8Won’t fit | | [Qwen3-VL-8B-Instruct](https://whatcanirunai.vercel.app/models/qwen3-vl-8b-instruct/)8.8B params, Qwen | **7.1** / 10.0 GB | Q4FitsQ8Won’t fit | | [Qwen2.5-VL-7B-Instruct](https://whatcanirunai.vercel.app/models/qwen2.5-vl-7b-instruct/)8.3B params, Qwen | **6.8** / 10.0 GB | Q4FitsQ8Won’t fit | | [Qwen3-8B](https://whatcanirunai.vercel.app/models/qwen3-8b/)8.2B params, Qwen | **6.7** / 10.0 GB | Q4FitsQ8Won’t fit | | [granite-3.3-8b-instruct](https://whatcanirunai.vercel.app/models/granite-3.3-8b-instruct/)8.2B params, ibm-granite | **6.7** / 10.0 GB | Q4FitsQ8Won’t fit | | [Llama-3.1-8B-Instruct](https://whatcanirunai.vercel.app/models/llama-3.1-8b-instruct/)8.0B params, meta-llama | **6.6** / 10.0 GB | Q4FitsQ8Won’t fit | | [Llama-3.1-8B](https://whatcanirunai.vercel.app/models/llama-3.1-8b/)8.0B params, meta-llama | **6.6** / 10.0 GB | Q4FitsQ8Won’t fit | | [Nemotron 3.5 Bolt E4B](https://whatcanirunai.vercel.app/models/nemotron-3.5-bolt-e4b/)8.0B params, nvidia | **6.6** / 10.0 GB | Q4FitsQ8Won’t fit | | [Gemma 4 E4B](https://whatcanirunai.vercel.app/models/gemma-4-e4b-it/)8.0B params, google | **6.6** / 10.0 GB | Q4FitsQ8Won’t fit | | [Qwen2.5-Coder-7B-Instruct](https://whatcanirunai.vercel.app/models/qwen2.5-coder-7b-instruct/)7.6B params, Qwen | **6.3** / 10.0 GB | Q4FitsQ8Won’t fit | | [DeepSeek-R1-Distill-Qwen-7B](https://whatcanirunai.vercel.app/models/deepseek-r1-distill-qwen-7b/)7.6B params, deepseek-ai | **6.3** / 10.0 GB | Q4FitsQ8Won’t fit | | [Qwen2.5-7B-Instruct](https://whatcanirunai.vercel.app/models/qwen2.5-7b-instruct/)7.6B params, Qwen | **6.3** / 10.0 GB | Q4FitsQ8Won’t fit | | [Mistral-7B-Instruct-v0.3](https://whatcanirunai.vercel.app/models/mistral-7b-instruct-v0.3/)7.2B params, mistralai | **6.1** / 10.0 GB | Q4FitsQ8Tight | | [Gemma 4 E2B](https://whatcanirunai.vercel.app/models/gemma-4-e2b-it/)5.1B params, google | **4.6** / 10.0 GB | Q4FitsQ8Fits | | [Qwen3.5-4B](https://whatcanirunai.vercel.app/models/qwen3.5-4b/)4.7B params, Qwen | **4.2** / 10.0 GB | Q4FitsQ8Fits | | [Qwen3-VL-4B-Instruct](https://whatcanirunai.vercel.app/models/qwen3-vl-4b-instruct/)4.4B params, Qwen | **4.1** / 10.0 GB | Q4FitsQ8Fits | | [gemma-3-4b-it](https://whatcanirunai.vercel.app/models/gemma-3-4b-it/)4.3B params, google | **4.0** / 10.0 GB | Q4FitsQ8Fits | | [Spark-X2.6-4B](https://whatcanirunai.vercel.app/models/spark-x2.6-4b/)4.1B params, XHToken | **3.9** / 10.0 GB | Q4FitsQ8Fits | | [Qwen3-4B](https://whatcanirunai.vercel.app/models/qwen3-4b/)4.0B params, Qwen | **3.8** / 10.0 GB | Q4FitsQ8Fits | | [Qwen3-4B-Instruct-2507](https://whatcanirunai.vercel.app/models/qwen3-4b-instruct-2507/)4.0B params, Qwen | **3.8** / 10.0 GB | Q4FitsQ8Fits | | [Nemotron Labs Dory 1.0 4B](https://whatcanirunai.vercel.app/models/nemotron-labs-dory1.0-4b/)4.0B params, nvidia | **3.8** / 10.0 GB | Q4FitsQ8Fits | | [Phi-4-mini-instruct](https://whatcanirunai.vercel.app/models/phi-4-mini-instruct/)3.8B params, microsoft | **3.7** / 10.0 GB | Q4FitsQ8Fits | | [Llama-3.2-3B-Instruct](https://whatcanirunai.vercel.app/models/llama-3.2-3b-instruct/)3.2B params, meta-llama | **3.2** / 10.0 GB | Q4FitsQ8Fits | | [Qwen2.5-3B-Instruct](https://whatcanirunai.vercel.app/models/qwen2.5-3b-instruct/)3.1B params, Qwen | **3.2** / 10.0 GB | Q4FitsQ8Fits | | [MiniCPM5-2B](https://whatcanirunai.vercel.app/models/minicpm5-2b/)2.5B params, openbmb | **2.8** / 10.0 GB | Q4FitsQ8Fits | | [Qwen3.5-2B](https://whatcanirunai.vercel.app/models/qwen3.5-2b/)2.3B params, Qwen | **2.6** / 10.0 GB | Q4FitsQ8Fits | | [Qwen3-1.7B](https://whatcanirunai.vercel.app/models/qwen3-1.7b/)2.0B params, Qwen | **2.4** / 10.0 GB | Q4FitsQ8Fits | | [DeepSeek-R1-Distill-Qwen-1.5B](https://whatcanirunai.vercel.app/models/deepseek-r1-distill-qwen-1.5b/)1.8B params, deepseek-ai | **2.2** / 10.0 GB | Q4FitsQ8Fits | | [LFM3-1.7B](https://whatcanirunai.vercel.app/models/lfm3-1.7b/)1.7B params, LiquidAI | **2.2** / 10.0 GB | Q4FitsQ8Fits | | [Qwen2.5-1.5B-Instruct](https://whatcanirunai.vercel.app/models/qwen2.5-1.5b-instruct/)1.5B params, Qwen | **2.1** / 10.0 GB | Q4FitsQ8Fits | | [Llama-3.2-1B-Instruct](https://whatcanirunai.vercel.app/models/llama-3.2-1b-instruct/)1.2B params, meta-llama | **1.9** / 10.0 GB | Q4FitsQ8Fits | | [gemma-3-1b-it](https://whatcanirunai.vercel.app/models/gemma-3-1b-it/)1.0B params, google | **1.7** / 10.0 GB | Q4FitsQ8Fits | | [Qwen3.5-0.8B](https://whatcanirunai.vercel.app/models/qwen3.5-0.8b/)0.9B params, Qwen | **1.6** / 10.0 GB | Q4FitsQ8Fits | | [Qwen3-0.6B](https://whatcanirunai.vercel.app/models/qwen3-0.6b/)0.8B params, Qwen | **1.5** / 10.0 GB | Q4FitsQ8Fits | | [Qwen2.5-0.5B-Instruct](https://whatcanirunai.vercel.app/models/qwen2.5-0.5b-instruct/)0.5B params, Qwen | **1.3** / 10.0 GB | Q4FitsQ8Fits | | [Mistral-Nemo-Instruct-2407](https://whatcanirunai.vercel.app/models/mistral-nemo-instruct-2407/)12.2B params, mistralai | **9.5** / 10.0 GB | Q4TightQ8Won’t fit | | [gemma-3-12b-it](https://whatcanirunai.vercel.app/models/gemma-3-12b-it/)12.2B params, google | **9.5** / 10.0 GB | Q4TightQ8Won’t fit | | [Mellum2.1-12B-A2.5B-Thinking](https://whatcanirunai.vercel.app/models/mellum2.1-12b-a2.5b-thinking/)12.1B params, JetBrains | **9.5** / 10.0 GB | Q4TightQ8Won’t fit | | [Gemma 4 12B](https://whatcanirunai.vercel.app/models/gemma-4-12b-it/)12.0B params, google | **9.3** / 10.0 GB | Q4TightQ8Won’t fit | Showing the 40 most popular of 349 models that fit. [See all 349 in the model directory →](https://whatcanirunai.vercel.app/models/?gb=10) ## Hardware we track with 10–11 GB usable * [Intel Arc B570](https://whatcanirunai.vercel.app/what-can-i-run/arc-b570/)10 GB usable, $219 * [GeForce RTX 3080 10GB](https://whatcanirunai.vercel.app/what-can-i-run/rtx-3080-10gb/)10 GB usable, $699 * [GeForce RTX 2080 Ti](https://whatcanirunai.vercel.app/what-can-i-run/rtx-2080-ti/)11 GB usable, $999 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 10 GB of VRAM? HyperCLOVAX-SEED-Omni-8B (10.7B parameters) is the biggest model we track that runs comfortably in 10 GB at Q4\_K\_M, needing about 8.5 GB with a \~4k context. At Q8 the biggest is YuE-s1-7B-anneal-en-cot. How many models fit in 10 GB of VRAM? 340 of the 656 open models we track run fully in 10 GB at Q4 with room to spare, 198 also fit at Q8, and 9 more fit only with a short context. Can I run a model that needs more than 10 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 10 GB as the comfort line. Which hardware has about 10 GB of usable memory? The cheapest current device we track in this range is the Intel Arc B570 (10 GB usable, $219 list price as of our last snapshot). 3 tracked devices have 10–11 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/) · [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/) · [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/