# Best local LLMs for 16 GB VRAM | What Can I Run AI? > 388 open models run fully in 16 GB of VRAM at Q4, up to Ling-Coder-lite. See every model that fits, with memory needs. Source: https://whatcanirunai.vercel.app/best-llm-for/16gb-vram/ # Best local LLMs for 16 GB of VRAM Every open model we track that runs fully in 16 GB of GPU memory, biggest first. Checks are at Q4\_K\_M with about 4k context. Run fully at Q4 388 of 656 Run at Q8 too 340 Biggest at Q4 Ling-Coder-lite With 16 GB, the biggest model you can run comfortably is **Ling-Coder-lite** (\~12.7 GB at Q4). If you want higher quality at Q8, **HyperCLOVAX-SEED-Omni-8B** is the biggest that fits. ## Models that fit in 16 GB Bar: Q4 memory needed vs. 16 GB. The tick marks 90%, our comfort line.FitsTight | Model | Q4 need vs. 16 GB | Q4 / Q8 | | ---------------------------------------------------------------------------------------------- | ------------------ | ------------------ | | [Carbon-X (DNA)](https://whatcanirunai.vercel.app/models/carbon-x/)14.8B params, HuggingFaceBio | **11.3** / 16.0 GB | Q4FitsQ8Won’t fit | | [Qwen2.5-Coder-14B-Instruct](https://whatcanirunai.vercel.app/models/qwen2.5-coder-14b-instruct/)14.8B params, Qwen | **11.3** / 16.0 GB | Q4FitsQ8Won’t fit | | [DeepSeek-R1-Distill-Qwen-14B](https://whatcanirunai.vercel.app/models/deepseek-r1-distill-qwen-14b/)14.8B params, deepseek-ai | **11.3** / 16.0 GB | Q4FitsQ8Won’t fit | | [Qwen3-14B](https://whatcanirunai.vercel.app/models/qwen3-14b/)14.8B params, Qwen | **11.3** / 16.0 GB | Q4FitsQ8Won’t fit | | [phi-4](https://whatcanirunai.vercel.app/models/phi-4/)14.7B params, microsoft | **11.2** / 16.0 GB | Q4FitsQ8Won’t fit | | [OLMo-2-1124-13B-Instruct](https://whatcanirunai.vercel.app/models/olmo-2-1124-13b-instruct/)13.7B params, allenai | **10.6** / 16.0 GB | Q4FitsQ8Won’t fit | | [Mistral-Nemo-Instruct-2407](https://whatcanirunai.vercel.app/models/mistral-nemo-instruct-2407/)12.2B params, mistralai | **9.5** / 16.0 GB | Q4FitsQ8Tight | | [gemma-3-12b-it](https://whatcanirunai.vercel.app/models/gemma-3-12b-it/)12.2B params, google | **9.5** / 16.0 GB | Q4FitsQ8Tight | | [Mellum2.1-12B-A2.5B-Thinking](https://whatcanirunai.vercel.app/models/mellum2.1-12b-a2.5b-thinking/)12.1B params, JetBrains | **9.5** / 16.0 GB | Q4FitsQ8Tight | | [Gemma 4 12B](https://whatcanirunai.vercel.app/models/gemma-4-12b-it/)12.0B params, google | **9.3** / 16.0 GB | Q4FitsQ8Tight | | [Ornith-1.5-9B](https://whatcanirunai.vercel.app/models/ornith-1.5-9b/)9.7B params, ornith-ai | **7.7** / 16.0 GB | Q4FitsQ8Fits | | [Qwen3.5-9B](https://whatcanirunai.vercel.app/models/qwen3.5-9b/)9.7B params, Qwen | **7.7** / 16.0 GB | Q4FitsQ8Fits | | [Qwen3-VL-8B-Instruct](https://whatcanirunai.vercel.app/models/qwen3-vl-8b-instruct/)8.8B params, Qwen | **7.1** / 16.0 GB | Q4FitsQ8Fits | | [Qwen2.5-VL-7B-Instruct](https://whatcanirunai.vercel.app/models/qwen2.5-vl-7b-instruct/)8.3B params, Qwen | **6.8** / 16.0 GB | Q4FitsQ8Fits | | [Qwen3-8B](https://whatcanirunai.vercel.app/models/qwen3-8b/)8.2B params, Qwen | **6.7** / 16.0 GB | Q4FitsQ8Fits | | [granite-3.3-8b-instruct](https://whatcanirunai.vercel.app/models/granite-3.3-8b-instruct/)8.2B params, ibm-granite | **6.7** / 16.0 GB | Q4FitsQ8Fits | | [Llama-3.1-8B-Instruct](https://whatcanirunai.vercel.app/models/llama-3.1-8b-instruct/)8.0B params, meta-llama | **6.6** / 16.0 GB | Q4FitsQ8Fits | | [Nemotron 3.5 Bolt E4B](https://whatcanirunai.vercel.app/models/nemotron-3.5-bolt-e4b/)8.0B params, nvidia | **6.6** / 16.0 GB | Q4FitsQ8Fits | | [Gemma 4 E4B](https://whatcanirunai.vercel.app/models/gemma-4-e4b-it/)8.0B params, google | **6.6** / 16.0 GB | Q4FitsQ8Fits | | [Qwen2.5-Coder-7B-Instruct](https://whatcanirunai.vercel.app/models/qwen2.5-coder-7b-instruct/)7.6B params, Qwen | **6.3** / 16.0 GB | Q4FitsQ8Fits | | [DeepSeek-R1-Distill-Qwen-7B](https://whatcanirunai.vercel.app/models/deepseek-r1-distill-qwen-7b/)7.6B params, deepseek-ai | **6.3** / 16.0 GB | Q4FitsQ8Fits | | [Qwen2.5-7B-Instruct](https://whatcanirunai.vercel.app/models/qwen2.5-7b-instruct/)7.6B params, Qwen | **6.3** / 16.0 GB | Q4FitsQ8Fits | | [Mistral-7B-Instruct-v0.3](https://whatcanirunai.vercel.app/models/mistral-7b-instruct-v0.3/)7.2B params, mistralai | **6.1** / 16.0 GB | Q4FitsQ8Fits | | [Gemma 4 E2B](https://whatcanirunai.vercel.app/models/gemma-4-e2b-it/)5.1B params, google | **4.6** / 16.0 GB | Q4FitsQ8Fits | | [Qwen3.5-4B](https://whatcanirunai.vercel.app/models/qwen3.5-4b/)4.7B params, Qwen | **4.2** / 16.0 GB | Q4FitsQ8Fits | | [gemma-3-4b-it](https://whatcanirunai.vercel.app/models/gemma-3-4b-it/)4.3B params, google | **4.0** / 16.0 GB | Q4FitsQ8Fits | | [Spark-X2.6-4B](https://whatcanirunai.vercel.app/models/spark-x2.6-4b/)4.1B params, XHToken | **3.9** / 16.0 GB | Q4FitsQ8Fits | | [Qwen3-4B](https://whatcanirunai.vercel.app/models/qwen3-4b/)4.0B params, Qwen | **3.8** / 16.0 GB | Q4FitsQ8Fits | | [Nemotron Labs Dory 1.0 4B](https://whatcanirunai.vercel.app/models/nemotron-labs-dory1.0-4b/)4.0B params, nvidia | **3.8** / 16.0 GB | Q4FitsQ8Fits | | [Phi-4-mini-instruct](https://whatcanirunai.vercel.app/models/phi-4-mini-instruct/)3.8B params, microsoft | **3.7** / 16.0 GB | Q4FitsQ8Fits | | [Llama-3.2-3B-Instruct](https://whatcanirunai.vercel.app/models/llama-3.2-3b-instruct/)3.2B params, meta-llama | **3.2** / 16.0 GB | Q4FitsQ8Fits | | [MiniCPM5-2B](https://whatcanirunai.vercel.app/models/minicpm5-2b/)2.5B params, openbmb | **2.8** / 16.0 GB | Q4FitsQ8Fits | | [Qwen3-1.7B](https://whatcanirunai.vercel.app/models/qwen3-1.7b/)2.0B params, Qwen | **2.4** / 16.0 GB | Q4FitsQ8Fits | | [LFM3-1.7B](https://whatcanirunai.vercel.app/models/lfm3-1.7b/)1.7B params, LiquidAI | **2.2** / 16.0 GB | Q4FitsQ8Fits | | [Qwen2.5-1.5B-Instruct](https://whatcanirunai.vercel.app/models/qwen2.5-1.5b-instruct/)1.5B params, Qwen | **2.1** / 16.0 GB | Q4FitsQ8Fits | | [Llama-3.2-1B-Instruct](https://whatcanirunai.vercel.app/models/llama-3.2-1b-instruct/)1.2B params, meta-llama | **1.9** / 16.0 GB | Q4FitsQ8Fits | | [gemma-3-1b-it](https://whatcanirunai.vercel.app/models/gemma-3-1b-it/)1.0B params, google | **1.7** / 16.0 GB | Q4FitsQ8Fits | | [Qwen3-0.6B](https://whatcanirunai.vercel.app/models/qwen3-0.6b/)0.8B params, Qwen | **1.5** / 16.0 GB | Q4FitsQ8Fits | | [Qwen2.5-0.5B-Instruct](https://whatcanirunai.vercel.app/models/qwen2.5-0.5b-instruct/)0.5B params, Qwen | **1.3** / 16.0 GB | Q4FitsQ8Fits | | [gpt-oss-20b](https://whatcanirunai.vercel.app/models/gpt-oss-20b/)20.9B params, openai | **15.6** / 16.0 GB | Q4TightQ8Won’t fit | Showing the 40 most popular of 393 models that fit. [See all 393 in the model directory →](https://whatcanirunai.vercel.app/models/?gb=16) ## Hardware we track with 16–19 GB usable * [Radeon RX 7600 XT](https://whatcanirunai.vercel.app/what-can-i-run/rx-7600-xt/)16 GB usable, $329 * [Radeon RX 9060 XT 16GB](https://whatcanirunai.vercel.app/what-can-i-run/rx-9060-xt-16gb/)16 GB usable, $349 * [Intel Arc A770 16GB](https://whatcanirunai.vercel.app/what-can-i-run/arc-a770-16gb/)16 GB usable, $349 * [GeForce RTX 5060 Ti 16GB](https://whatcanirunai.vercel.app/what-can-i-run/rtx-5060-ti-16gb/)16 GB usable, $429 * [GeForce RTX 4060 Ti 16GB](https://whatcanirunai.vercel.app/what-can-i-run/rtx-4060-ti-16gb/)16 GB usable, $499 * [Radeon RX 7800 XT](https://whatcanirunai.vercel.app/what-can-i-run/rx-7800-xt/)16 GB usable, $499 * [Radeon RX 7900 GRE](https://whatcanirunai.vercel.app/what-can-i-run/rx-7900-gre/)16 GB usable, $549 * [Radeon RX 9070](https://whatcanirunai.vercel.app/what-can-i-run/rx-9070/)16 GB usable, $549 * [Radeon RX 6800](https://whatcanirunai.vercel.app/what-can-i-run/rx-6800/)16 GB usable, $579 * [Radeon RX 9070 XT](https://whatcanirunai.vercel.app/what-can-i-run/rx-9070-xt/)16 GB usable, $599 * [Radeon RX 6800 XT](https://whatcanirunai.vercel.app/what-can-i-run/rx-6800-xt/)16 GB usable, $649 * [NVIDIA RTX 2000 Ada 16GB](https://whatcanirunai.vercel.app/what-can-i-run/rtx-2000-ada/)16 GB usable, $649 * [GeForce RTX 5070 Ti](https://whatcanirunai.vercel.app/what-can-i-run/rtx-5070-ti/)16 GB usable, $749 * [GeForce RTX 4070 Ti Super](https://whatcanirunai.vercel.app/what-can-i-run/rtx-4070-ti-super/)16 GB usable, $799 * [GeForce RTX 4080 Super](https://whatcanirunai.vercel.app/what-can-i-run/rtx-4080-super/)16 GB usable, $999 * [GeForce RTX 5080](https://whatcanirunai.vercel.app/what-can-i-run/rtx-5080/)16 GB usable, $999 * [Radeon RX 6900 XT](https://whatcanirunai.vercel.app/what-can-i-run/rx-6900-xt/)16 GB usable, $999 * [NVIDIA RTX A4000](https://whatcanirunai.vercel.app/what-can-i-run/rtx-a4000/)16 GB usable, $1000 * [Radeon RX 6950 XT](https://whatcanirunai.vercel.app/what-can-i-run/rx-6950-xt/)16 GB usable, $1099 * [GeForce RTX 4080](https://whatcanirunai.vercel.app/what-can-i-run/rtx-4080/)16 GB usable, $1199 * [Mac mini M6 24GB](https://whatcanirunai.vercel.app/what-can-i-run/mac-mini-m6-24/)18 GB usable, $1299 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 16 GB of VRAM? Ling-Coder-lite (16.8B parameters) is the biggest model we track that runs comfortably in 16 GB at Q4\_K\_M, needing about 12.7 GB with a \~4k context. At Q8 the biggest is HyperCLOVAX-SEED-Omni-8B. How many models fit in 16 GB of VRAM? 388 of the 656 open models we track run fully in 16 GB at Q4 with room to spare, 340 also fit at Q8, and 5 more fit only with a short context. Can I run a model that needs more than 16 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 16 GB as the comfort line. Which hardware has about 16 GB of usable memory? The cheapest current device we track in this range is the Radeon RX 7600 XT (16 GB usable, $329 list price as of our last snapshot). 21 tracked devices have 16–19 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/) · [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/