# Best local LLMs for 8 GB VRAM | What Can I Run AI? > 315 open models run fully in 8 GB of VRAM at Q4, up to NVIDIA-Nemotron-Nano-9B-v2. See every model that fits, with memory needs. Source: https://whatcanirunai.vercel.app/best-llm-for/8gb-vram/ # Best local LLMs for 8 GB of VRAM Every open model we track that runs fully in 8 GB of GPU memory, biggest first. Checks are at Q4\_K\_M with about 4k context. Run fully at Q4 315 of 656 Run at Q8 too 192 Biggest at Q4 NVIDIA-Nemotron-Nano-9B-v2 With 8 GB, the biggest model you can run comfortably is **NVIDIA-Nemotron-Nano-9B-v2** (\~7.2 GB at Q4). If you want higher quality at Q8, **Molmo2-ER** is the biggest that fits. ## Models that fit in 8 GB Bar: Q4 memory needed vs. 8 GB. The tick marks 90%, our comfort line.FitsTight | Model | Q4 need vs. 8 GB | Q4 / Q8 | | ----------------------------------------------------------------------------------------------- | ---------------- | ------------------ | | [Qwen3-VL-8B-Instruct](https://whatcanirunai.vercel.app/models/qwen3-vl-8b-instruct/)8.8B params, Qwen | **7.1** / 8.0 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** / 8.0 GB | Q4FitsQ8Won’t fit | | [Qwen2-VL-7B-Instruct](https://whatcanirunai.vercel.app/models/qwen2-vl-7b-instruct/)8.3B params, Qwen | **6.8** / 8.0 GB | Q4FitsQ8Won’t fit | | [Qwen3-8B](https://whatcanirunai.vercel.app/models/qwen3-8b/)8.2B params, Qwen | **6.7** / 8.0 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** / 8.0 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** / 8.0 GB | Q4FitsQ8Won’t fit | | [Llama-3.1-8B](https://whatcanirunai.vercel.app/models/llama-3.1-8b/)8.0B params, meta-llama | **6.6** / 8.0 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** / 8.0 GB | Q4FitsQ8Won’t fit | | [Gemma 4 E4B](https://whatcanirunai.vercel.app/models/gemma-4-e4b-it/)8.0B params, google | **6.6** / 8.0 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** / 8.0 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** / 8.0 GB | Q4FitsQ8Won’t fit | | [Qwen2.5-7B-Instruct](https://whatcanirunai.vercel.app/models/qwen2.5-7b-instruct/)7.6B params, Qwen | **6.3** / 8.0 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** / 8.0 GB | Q4FitsQ8Won’t fit | | [Gemma 4 E2B](https://whatcanirunai.vercel.app/models/gemma-4-e2b-it/)5.1B params, google | **4.6** / 8.0 GB | Q4FitsQ8Tight | | [Qwen3.5-4B](https://whatcanirunai.vercel.app/models/qwen3.5-4b/)4.7B params, Qwen | **4.2** / 8.0 GB | Q4FitsQ8Fits | | [Qwen3-VL-4B-Instruct](https://whatcanirunai.vercel.app/models/qwen3-vl-4b-instruct/)4.4B params, Qwen | **4.1** / 8.0 GB | Q4FitsQ8Fits | | [gemma-3-4b-it](https://whatcanirunai.vercel.app/models/gemma-3-4b-it/)4.3B params, google | **4.0** / 8.0 GB | Q4FitsQ8Fits | | [Spark-X2.6-4B](https://whatcanirunai.vercel.app/models/spark-x2.6-4b/)4.1B params, XHToken | **3.9** / 8.0 GB | Q4FitsQ8Fits | | [Qwen3-4B](https://whatcanirunai.vercel.app/models/qwen3-4b/)4.0B params, Qwen | **3.8** / 8.0 GB | Q4FitsQ8Fits | | [Qwen3-4B-Instruct-2507](https://whatcanirunai.vercel.app/models/qwen3-4b-instruct-2507/)4.0B params, Qwen | **3.8** / 8.0 GB | Q4FitsQ8Fits | | [Nemotron Labs Dory 1.0 4B](https://whatcanirunai.vercel.app/models/nemotron-labs-dory1.0-4b/)4.0B params, nvidia | **3.8** / 8.0 GB | Q4FitsQ8Fits | | [Phi-4-mini-instruct](https://whatcanirunai.vercel.app/models/phi-4-mini-instruct/)3.8B params, microsoft | **3.7** / 8.0 GB | Q4FitsQ8Fits | | [Llama-3.2-3B-Instruct](https://whatcanirunai.vercel.app/models/llama-3.2-3b-instruct/)3.2B params, meta-llama | **3.2** / 8.0 GB | Q4FitsQ8Fits | | [Qwen2.5-3B-Instruct](https://whatcanirunai.vercel.app/models/qwen2.5-3b-instruct/)3.1B params, Qwen | **3.2** / 8.0 GB | Q4FitsQ8Fits | | [SmolLM3-3B](https://whatcanirunai.vercel.app/models/smollm3-3b/)3.1B params, HuggingFaceTB | **3.1** / 8.0 GB | Q4FitsQ8Fits | | [MiniCPM5-2B](https://whatcanirunai.vercel.app/models/minicpm5-2b/)2.5B params, openbmb | **2.8** / 8.0 GB | Q4FitsQ8Fits | | [Qwen3.5-2B](https://whatcanirunai.vercel.app/models/qwen3.5-2b/)2.3B params, Qwen | **2.6** / 8.0 GB | Q4FitsQ8Fits | | [Qwen3-VL-2B-Instruct](https://whatcanirunai.vercel.app/models/qwen3-vl-2b-instruct/)2.1B params, Qwen | **2.5** / 8.0 GB | Q4FitsQ8Fits | | [Qwen3-1.7B](https://whatcanirunai.vercel.app/models/qwen3-1.7b/)2.0B params, Qwen | **2.4** / 8.0 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** / 8.0 GB | Q4FitsQ8Fits | | [LFM3-1.7B](https://whatcanirunai.vercel.app/models/lfm3-1.7b/)1.7B params, LiquidAI | **2.2** / 8.0 GB | Q4FitsQ8Fits | | [Qwen2.5-1.5B-Instruct](https://whatcanirunai.vercel.app/models/qwen2.5-1.5b-instruct/)1.5B params, Qwen | **2.1** / 8.0 GB | Q4FitsQ8Fits | | [Llama-3.2-1B-Instruct](https://whatcanirunai.vercel.app/models/llama-3.2-1b-instruct/)1.2B params, meta-llama | **1.9** / 8.0 GB | Q4FitsQ8Fits | | [Llama-3.2-1B](https://whatcanirunai.vercel.app/models/llama-3.2-1b/)1.2B params, meta-llama | **1.9** / 8.0 GB | Q4FitsQ8Fits | | [gemma-3-1b-it](https://whatcanirunai.vercel.app/models/gemma-3-1b-it/)1.0B params, google | **1.7** / 8.0 GB | Q4FitsQ8Fits | | [Qwen3.5-0.8B](https://whatcanirunai.vercel.app/models/qwen3.5-0.8b/)0.9B params, Qwen | **1.6** / 8.0 GB | Q4FitsQ8Fits | | [Qwen3-0.6B](https://whatcanirunai.vercel.app/models/qwen3-0.6b/)0.8B params, Qwen | **1.5** / 8.0 GB | Q4FitsQ8Fits | | [Qwen2.5-0.5B-Instruct](https://whatcanirunai.vercel.app/models/qwen2.5-0.5b-instruct/)0.5B params, Qwen | **1.3** / 8.0 GB | Q4FitsQ8Fits | | [Ornith-1.5-9B](https://whatcanirunai.vercel.app/models/ornith-1.5-9b/)9.7B params, ornith-ai | **7.7** / 8.0 GB | Q4TightQ8Won’t fit | | [Qwen3.5-9B](https://whatcanirunai.vercel.app/models/qwen3.5-9b/)9.7B params, Qwen | **7.7** / 8.0 GB | Q4TightQ8Won’t fit | Showing the 40 most popular of 330 models that fit. [See all 330 in the model directory →](https://whatcanirunai.vercel.app/models/?gb=8) ## Hardware we track with 8–9 GB usable * [GeForce RTX 3050 8GB](https://whatcanirunai.vercel.app/what-can-i-run/rtx-3050-8gb/)8 GB usable, $249 * [Radeon RX 7600](https://whatcanirunai.vercel.app/what-can-i-run/rx-7600/)8 GB usable, $269 * [Intel Arc A750](https://whatcanirunai.vercel.app/what-can-i-run/arc-a750/)8 GB usable, $289 * [GeForce RTX 4060](https://whatcanirunai.vercel.app/what-can-i-run/rtx-4060/)8 GB usable, $299 * [GeForce RTX 5060](https://whatcanirunai.vercel.app/what-can-i-run/rtx-5060/)8 GB usable, $299 * [Radeon RX 9060 XT 8GB](https://whatcanirunai.vercel.app/what-can-i-run/rx-9060-xt-8gb/)8 GB usable, $299 * [GeForce RTX 5060 Ti 8GB](https://whatcanirunai.vercel.app/what-can-i-run/rtx-5060-ti-8gb/)8 GB usable, $379 * [GeForce RTX 3060 Ti](https://whatcanirunai.vercel.app/what-can-i-run/rtx-3060-ti/)8 GB usable, $399 * [GeForce RTX 2060 Super](https://whatcanirunai.vercel.app/what-can-i-run/rtx-2060-super/)8 GB usable, $399 * [GeForce RTX 4060 Ti 8GB](https://whatcanirunai.vercel.app/what-can-i-run/rtx-4060-ti-8gb/)8 GB usable, $399 * [GeForce RTX 3070](https://whatcanirunai.vercel.app/what-can-i-run/rtx-3070/)8 GB usable, $499 * [GeForce RTX 2070](https://whatcanirunai.vercel.app/what-can-i-run/rtx-2070/)8 GB usable, $499 * [GeForce RTX 2070 Super](https://whatcanirunai.vercel.app/what-can-i-run/rtx-2070-super/)8 GB usable, $499 * [GeForce RTX 3070 Ti](https://whatcanirunai.vercel.app/what-can-i-run/rtx-3070-ti/)8 GB usable, $599 * [GeForce RTX 2080](https://whatcanirunai.vercel.app/what-can-i-run/rtx-2080/)8 GB usable, $699 * [GeForce RTX 2080 Super](https://whatcanirunai.vercel.app/what-can-i-run/rtx-2080-super/)8 GB usable, $699 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 8 GB of VRAM? NVIDIA-Nemotron-Nano-9B-v2 (8.9B parameters) is the biggest model we track that runs comfortably in 8 GB at Q4\_K\_M, needing about 7.2 GB with a \~4k context. At Q8 the biggest is Molmo2-ER. How many models fit in 8 GB of VRAM? 315 of the 656 open models we track run fully in 8 GB at Q4 with room to spare, 192 also fit at Q8, and 15 more fit only with a short context. Can I run a model that needs more than 8 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 8 GB as the comfort line. Which hardware has about 8 GB of usable memory? The cheapest current device we track in this range is the GeForce RTX 3050 8GB (8 GB usable, $249 list price as of our last snapshot). 16 tracked devices have 8–9 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/) · [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/) · [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/