# How much VRAM do you need for a 12B–14B model? > A 12B–14B LLM needs about 11.3 GB of VRAM at Q4, 19.0 GB at Q8 and 35.0 GB at FP16. Context-length table and the cheapest GPU and Mac that fit. Source: https://whatcanirunai.vercel.app/how-much-vram/14b/ # How much VRAM do you need for a 12B–14B model? Short answer: about **11.3 GB** at Q4\_K\_M, the usual quant for local use. Below: every quant, longer contexts, the cheapest hardware that fits, and every 12B–14B open model we track. Q4\_K\_M 11.3 GB 9.0 GB weights Q8\_0 19.0 GB 15.7 GB weights FP16 35.0 GB 29.5 GB weights Worked out for [Qwen3-14B](https://whatcanirunai.vercel.app/models/qwen3-14b/) (14.8B parameters, from the Hugging Face API). Formula: weights × 1.15 + 1 GB at a \~4k context ([how we calculate](https://whatcanirunai.vercel.app/method/)). Cheapest GPU that fits at Q4: **Radeon RX 7600 XT** (16 GB, $329). Cheapest Mac or mini PC: **Mac mini M6 24GB** ($1,299). ## Cheapest hardware for each quant Only devices with a sourced price. GPUs at list price (MSRP); street prices may be higher. __Cheapest GPU and Mac or mini PC for a 12B–14B model at each quant__ | Setting | Memory needed | Cheapest GPU | Cheapest Mac / mini PC | | ---------------------- | ------------- | -------------------------------------------------------------------- | --------------------------------------------------------------------------- | | Q4\_K\_M, \~4k context | 11.3 GB | [Radeon RX 7600 XT](https://whatcanirunai.vercel.app/hardware/rx-7600-xt/) 16 GB usable · $329 | [Mac mini M6 24GB](https://whatcanirunai.vercel.app/hardware/mac-mini-m6-24/) 18 GB usable · $1,299 | | Q8\_0, \~4k context | 19.0 GB | [Intel Arc Pro B70 32GB](https://whatcanirunai.vercel.app/hardware/arc-pro-b70/) 32 GB usable · $949 | [Mac mini M6 32GB](https://whatcanirunai.vercel.app/hardware/mac-mini-m6-32/) 24 GB usable · $1,799 | | FP16, \~4k context | 35.0 GB | [NVIDIA RTX A6000](https://whatcanirunai.vercel.app/hardware/rtx-a6000/) 48 GB usable · $4,649 | [Mac mini M5 Pro 64GB](https://whatcanirunai.vercel.app/hardware/mac-mini-m5-pro-64/) 48 GB usable · $3,199 | | Q4\_K\_M, 32k context | 16.0 GB | [Radeon RX 7900 XT](https://whatcanirunai.vercel.app/hardware/rx-7900-xt/) 20 GB usable · $899 | [Mac mini M6 24GB](https://whatcanirunai.vercel.app/hardware/mac-mini-m6-24/) 18 GB usable · $1,299 | ## Memory by context length Each extra token of context adds to the KV cache. Numbers for Qwen3-14B, from its published config; a Q8 KV cache halves the cache part. __Memory needed for a 12B–14B model by context length__ | Context | Q4\_K\_M, FP16 cache | Q4\_K\_M, Q8 cache | Q8\_0, FP16 cache | | ----------- | -------------------- | ------------------ | ----------------- | | 4k tokens | 11.3 GB | 11.3 GB | 19.0 GB | | 8k tokens | 12.0 GB | 11.3 GB | 19.7 GB | | 16k tokens | 13.3 GB | 12.0 GB | 21.1 GB | | 32k tokens | 16.0 GB | 13.3 GB | 23.7 GB | | 64k tokens | 21.4 GB | 16.0 GB | 29.1 GB | | 128k tokens | 32.1 GB | 21.4 GB | 39.8 GB | ## Hardware that runs it Bar: Q4 memory needed vs. usable memory. Speed is estimated decode at Q4 ([how we estimate](https://whatcanirunai.vercel.app/method/#speed)).FitsTight __Tracked hardware that runs a 12B–14B model at Q4__ | Hardware | Q4 need vs. usable | Q4 / Q8 / FP16 | Est. tok/s | | ------------------------------------------------------------------------------------------------------ | ------------------- | ------------------------------- | ---------- | | [GeForce RTX 2060 12GB](https://whatcanirunai.vercel.app/hardware/rtx-2060-12gb/)GPU | **11.3** / 12.0 GB | Q4TightQ8Won’t fitFP16Won’t fit | 23–32 | | [Intel Arc B580](https://whatcanirunai.vercel.app/hardware/arc-b580/)GPU · $249 | **11.3** / 12.0 GB | Q4TightQ8Won’t fitFP16Won’t fit | 31–43 | | [GeForce RTX 3060 12GB](https://whatcanirunai.vercel.app/hardware/rtx-3060-12gb/)GPU · $329 | **11.3** / 12.0 GB | Q4TightQ8Won’t fitFP16Won’t fit | 24–34 | | [Radeon RX 7700 XT](https://whatcanirunai.vercel.app/hardware/rx-7700-xt/)GPU · $449 | **11.3** / 12.0 GB | Q4TightQ8Won’t fitFP16Won’t fit | 29–41 | | [Radeon RX 6700 XT](https://whatcanirunai.vercel.app/hardware/rx-6700-xt/)GPU · $479 | **11.3** / 12.0 GB | Q4TightQ8Won’t fitFP16Won’t fit | 26–36 | | [GeForce RTX 5070](https://whatcanirunai.vercel.app/hardware/rtx-5070/)GPU · $549 | **11.3** / 12.0 GB | Q4TightQ8Won’t fitFP16Won’t fit | 45–64 | | [Radeon RX 6750 XT](https://whatcanirunai.vercel.app/hardware/rx-6750-xt/)GPU · $549 | **11.3** / 12.0 GB | Q4TightQ8Won’t fitFP16Won’t fit | 29–41 | | [GeForce RTX 4070](https://whatcanirunai.vercel.app/hardware/rtx-4070/)GPU · $599 | **11.3** / 12.0 GB | Q4TightQ8Won’t fitFP16Won’t fit | 34–48 | | [GeForce RTX 4070 Super](https://whatcanirunai.vercel.app/hardware/rtx-4070-super/)GPU · $599 | **11.3** / 12.0 GB | Q4TightQ8Won’t fitFP16Won’t fit | 34–48 | | [Mac mini M4 16GB](https://whatcanirunai.vercel.app/hardware/mac-mini-m4-16/)unified memory · $789 | **11.3** / 12.0 GB | Q4TightQ8Won’t fitFP16Won’t fit | 6–11 | | [GeForce RTX 3080 12GB](https://whatcanirunai.vercel.app/hardware/rtx-3080-12gb/)GPU · $799 | **11.3** / 12.0 GB | Q4TightQ8Won’t fitFP16Won’t fit | 61–87 | | [GeForce RTX 4070 Ti](https://whatcanirunai.vercel.app/hardware/rtx-4070-ti/)GPU · $799 | **11.3** / 12.0 GB | Q4TightQ8Won’t fitFP16Won’t fit | 34–48 | | [Mac mini M6 16GB](https://whatcanirunai.vercel.app/hardware/mac-mini-m6-16/)unified memory · $899 | **11.3** / 12.0 GB | Q4TightQ8Won’t fitFP16Won’t fit | 8–14 | | [GeForce RTX 3080 Ti](https://whatcanirunai.vercel.app/hardware/rtx-3080-ti/)GPU · $1,199 | **11.3** / 12.0 GB | Q4TightQ8Won’t fitFP16Won’t fit | 61–87 | | [Radeon RX 7600 XT](https://whatcanirunai.vercel.app/hardware/rx-7600-xt/)GPU · $329 | **11.3** / 16.0 GB | Q4FitsQ8Won’t fitFP16Won’t fit | 19–27 | | [Radeon RX 9060 XT 16GB](https://whatcanirunai.vercel.app/hardware/rx-9060-xt-16gb/)GPU · $349 | **11.3** / 16.0 GB | Q4FitsQ8Won’t fitFP16Won’t fit | 21–30 | | [Intel Arc A770 16GB](https://whatcanirunai.vercel.app/hardware/arc-a770-16gb/)GPU · $349 | **11.3** / 16.0 GB | Q4FitsQ8Won’t fitFP16Won’t fit | 38–53 | | [GeForce RTX 5060 Ti 16GB](https://whatcanirunai.vercel.app/hardware/rtx-5060-ti-16gb/)GPU · $429 | **11.3** / 16.0 GB | Q4FitsQ8Won’t fitFP16Won’t fit | 30–43 | | [GeForce RTX 4060 Ti 16GB](https://whatcanirunai.vercel.app/hardware/rtx-4060-ti-16gb/)GPU · $499 | **11.3** / 16.0 GB | Q4FitsQ8Won’t fitFP16Won’t fit | 19–27 | | [Radeon RX 7800 XT](https://whatcanirunai.vercel.app/hardware/rx-7800-xt/)GPU · $499 | **11.3** / 16.0 GB | Q4FitsQ8Won’t fitFP16Won’t fit | 42–59 | | [Radeon RX 7900 GRE](https://whatcanirunai.vercel.app/hardware/rx-7900-gre/)GPU · $549 | **11.3** / 16.0 GB | Q4FitsQ8Won’t fitFP16Won’t fit | 39–55 | | [Radeon RX 9070](https://whatcanirunai.vercel.app/hardware/rx-9070/)GPU · $549 | **11.3** / 16.0 GB | Q4FitsQ8Won’t fitFP16Won’t fit | 43–61 | | [Radeon RX 6800](https://whatcanirunai.vercel.app/hardware/rx-6800/)GPU · $579 | **11.3** / 16.0 GB | Q4FitsQ8Won’t fitFP16Won’t fit | 34–49 | | [Radeon RX 9070 XT](https://whatcanirunai.vercel.app/hardware/rx-9070-xt/)GPU · $599 | **11.3** / 16.0 GB | Q4FitsQ8Won’t fitFP16Won’t fit | 43–61 | | [Radeon RX 6800 XT](https://whatcanirunai.vercel.app/hardware/rx-6800-xt/)GPU · $649 | **11.3** / 16.0 GB | Q4FitsQ8Won’t fitFP16Won’t fit | 34–49 | | [NVIDIA RTX 2000 Ada 16GB](https://whatcanirunai.vercel.app/hardware/rtx-2000-ada/)GPU · $649 | **11.3** / 16.0 GB | Q4FitsQ8Won’t fitFP16Won’t fit | 15–21 | | [GeForce RTX 5070 Ti](https://whatcanirunai.vercel.app/hardware/rtx-5070-ti/)GPU · $749 | **11.3** / 16.0 GB | Q4FitsQ8Won’t fitFP16Won’t fit | 60–85 | | [GeForce RTX 4070 Ti Super](https://whatcanirunai.vercel.app/hardware/rtx-4070-ti-super/)GPU · $799 | **11.3** / 16.0 GB | Q4FitsQ8Won’t fitFP16Won’t fit | 45–64 | | [GeForce RTX 4080 Super](https://whatcanirunai.vercel.app/hardware/rtx-4080-super/)GPU · $999 | **11.3** / 16.0 GB | Q4FitsQ8Won’t fitFP16Won’t fit | 49–70 | | [GeForce RTX 5080](https://whatcanirunai.vercel.app/hardware/rtx-5080/)GPU · $999 | **11.3** / 16.0 GB | Q4FitsQ8Won’t fitFP16Won’t fit | 64–91 | | [Radeon RX 6900 XT](https://whatcanirunai.vercel.app/hardware/rx-6900-xt/)GPU · $999 | **11.3** / 16.0 GB | Q4FitsQ8Won’t fitFP16Won’t fit | 34–49 | | [NVIDIA RTX A4000](https://whatcanirunai.vercel.app/hardware/rtx-a4000/)GPU · $1,000 | **11.3** / 16.0 GB | Q4FitsQ8Won’t fitFP16Won’t fit | 30–43 | | [Radeon RX 6950 XT](https://whatcanirunai.vercel.app/hardware/rx-6950-xt/)GPU · $1,099 | **11.3** / 16.0 GB | Q4FitsQ8Won’t fitFP16Won’t fit | 39–55 | | [GeForce RTX 4080](https://whatcanirunai.vercel.app/hardware/rtx-4080/)GPU · $1,199 | **11.3** / 16.0 GB | Q4FitsQ8Won’t fitFP16Won’t fit | 48–68 | | [Mac mini M6 24GB](https://whatcanirunai.vercel.app/hardware/mac-mini-m6-24/)unified memory · $1,299 | **11.3** / 18.0 GB | Q4FitsQ8Won’t fitFP16Won’t fit | 9–15 | | [Radeon RX 7900 XT](https://whatcanirunai.vercel.app/hardware/rx-7900-xt/)GPU · $899 | **11.3** / 20.0 GB | Q4FitsQ8TightFP16Won’t fit | 54–76 | | [NVIDIA RTX 4000 Ada 20GB](https://whatcanirunai.vercel.app/hardware/rtx-4000-ada/)GPU · $1,250 | **11.3** / 20.0 GB | Q4FitsQ8TightFP16Won’t fit | 24–34 | | [NVIDIA Tesla P40 24GB](https://whatcanirunai.vercel.app/hardware/tesla-p40/)GPU | **11.3** / 24.0 GB | Q4FitsQ8FitsFP16Won’t fit | 23–33 | | [Radeon RX 7900 XTX](https://whatcanirunai.vercel.app/hardware/rx-7900-xtx/)GPU · $999 | **11.3** / 24.0 GB | Q4FitsQ8FitsFP16Won’t fit | 64–91 | | [GeForce RTX 3090](https://whatcanirunai.vercel.app/hardware/rtx-3090/)GPU · $1,499 | **11.3** / 24.0 GB | Q4FitsQ8FitsFP16Won’t fit | 63–89 | | [GeForce RTX 4090](https://whatcanirunai.vercel.app/hardware/rtx-4090/)GPU · $1,599 | **11.3** / 24.0 GB | Q4FitsQ8FitsFP16Won’t fit | 68–96 | | [Mac mini M6 32GB](https://whatcanirunai.vercel.app/hardware/mac-mini-m6-32/)unified memory · $1,799 | **11.3** / 24.0 GB | Q4FitsQ8FitsFP16Won’t fit | 9–15 | | [GeForce RTX 3090 Ti](https://whatcanirunai.vercel.app/hardware/rtx-3090-ti/)GPU · $1,999 | **11.3** / 24.0 GB | Q4FitsQ8FitsFP16Won’t fit | 68–96 | | [NVIDIA RTX A5000](https://whatcanirunai.vercel.app/hardware/rtx-a5000/)GPU · $2,250 | **11.3** / 24.0 GB | Q4FitsQ8FitsFP16Won’t fit | 51–73 | | [MacBook Pro M6 32GB](https://whatcanirunai.vercel.app/hardware/macbook-pro-m6-32/)unified memory · $2,399 | **11.3** / 24.0 GB | Q4FitsQ8FitsFP16Won’t fit | 9–15 | | [NVIDIA Titan RTX](https://whatcanirunai.vercel.app/hardware/titan-rtx/)GPU · $2,499 | **11.3** / 24.0 GB | Q4FitsQ8FitsFP16Won’t fit | 45–64 | | [NVIDIA Tesla V100 32GB (PCIe)](https://whatcanirunai.vercel.app/hardware/tesla-v100-32gb/)GPU | **11.3** / 32.0 GB | Q4FitsQ8FitsFP16Won’t fit | 60–85 | | [AMD Radeon Instinct MI50 32GB](https://whatcanirunai.vercel.app/hardware/instinct-mi50-32gb/)GPU | **11.3** / 32.0 GB | Q4FitsQ8FitsFP16Won’t fit | 69–97 | | [AMD Radeon Instinct MI60 32GB](https://whatcanirunai.vercel.app/hardware/instinct-mi60/)GPU | **11.3** / 32.0 GB | Q4FitsQ8FitsFP16Won’t fit | 69–97 | | [Intel Arc Pro B70 32GB](https://whatcanirunai.vercel.app/hardware/arc-pro-b70/)GPU · $949 | **11.3** / 32.0 GB | Q4FitsQ8FitsFP16Won’t fit | 41–58 | | [GeForce RTX 5090](https://whatcanirunai.vercel.app/hardware/rtx-5090/)GPU · $1,999 | **11.3** / 32.0 GB | Q4FitsQ8FitsFP16Won’t fit | 120–170 | | [Mac mini M5 Pro 64GB](https://whatcanirunai.vercel.app/hardware/mac-mini-m5-pro-64/)unified memory · $3,199 | **11.3** / 48.0 GB | Q4FitsQ8FitsFP16Fits | 15–27 | | [NVIDIA RTX A6000](https://whatcanirunai.vercel.app/hardware/rtx-a6000/)GPU · $4,649 | **11.3** / 48.0 GB | Q4FitsQ8FitsFP16Fits | 51–73 | | [NVIDIA DGX Spark 64GB](https://whatcanirunai.vercel.app/hardware/dgx-spark-64/)unified memory · $4,999 | **11.3** / 48.0 GB | Q4FitsQ8FitsFP16Fits | 14–24 | | [NVIDIA RTX 6000 Ada 48GB](https://whatcanirunai.vercel.app/hardware/rtx-6000-ada/)GPU · $6,799 | **11.3** / 48.0 GB | Q4FitsQ8FitsFP16Fits | 64–91 | | [Framework Desktop (Ryzen AI Max+ 395) 128GB](https://whatcanirunai.vercel.app/hardware/framework-desktop-128/)unified memory · $3,449 | **11.3** / 96.0 GB | Q4FitsQ8FitsFP16Fits | 13–23 | | [MacBook Pro M4 Max 128GB](https://whatcanirunai.vercel.app/hardware/macbook-pro-m4-max-128/)unified memory · $4,999 | **11.3** / 96.0 GB | Q4FitsQ8FitsFP16Fits | 27–49 | | [MacBook Pro M5 Max 128GB](https://whatcanirunai.vercel.app/hardware/macbook-pro-m5-max-128/)unified memory · $6,699 | **11.3** / 96.0 GB | Q4FitsQ8FitsFP16Fits | 31–55 | | [NVIDIA DGX Spark 128GB](https://whatcanirunai.vercel.app/hardware/dgx-spark-128/)unified memory · $6,950 | **11.3** / 96.0 GB | Q4FitsQ8FitsFP16Fits | 14–24 | | [NVIDIA RTX Pro 6000 Blackwell 96GB](https://whatcanirunai.vercel.app/hardware/rtx-pro-6000-blackwell/)GPU · $8,565 | **11.3** / 96.0 GB | Q4FitsQ8FitsFP16Fits | 120–170 | | [Mac Studio M5 Ultra 256GB](https://whatcanirunai.vercel.app/hardware/mac-studio-m5-ultra-256/)unified memory · $9,499 | **11.3** / 192.0 GB | Q4FitsQ8FitsFP16Fits | 60–107 | | [Mac Studio M5 Ultra 512GB](https://whatcanirunai.vercel.app/hardware/mac-studio-m5-ultra-512/)unified memory · $22,299 | **11.3** / 384.0 GB | Q4FitsQ8FitsFP16Fits | 60–107 | ## 12B–14B models we track __Memory needed by each 12B–14B model we track__ | Model | Q4\_K\_M | Q8\_0 | FP16 | | ----------------------------------------------------------------------------------------------------------- | -------- | ------- | ------- | | [Gemma 4 12B](https://whatcanirunai.vercel.app/models/gemma-4-12b-it/)12.0B params · google | 9.3 GB | 15.6 GB | 28.5 GB | | [Mellum2.1-12B-A2.5B-Thinking](https://whatcanirunai.vercel.app/models/mellum2.1-12b-a2.5b-thinking/)12.1B params · JetBrains · MoE | 9.5 GB | 15.8 GB | 28.9 GB | | [Mellum2-12B-A2.5B-Thinking](https://whatcanirunai.vercel.app/models/mellum2-12b-a2.5b-thinking/)12.1B params · JetBrains · MoE | 9.5 GB | 15.8 GB | 28.9 GB | | [Mellum2-12B-A2.5B-Instruct](https://whatcanirunai.vercel.app/models/mellum2-12b-a2.5b-instruct/)12.1B params · JetBrains · MoE | 9.5 GB | 15.8 GB | 28.9 GB | | [Mellum2-12B-A2.5B-Thinking-SFT](https://whatcanirunai.vercel.app/models/mellum2-12b-a2.5b-thinking-sft/)12.1B params · JetBrains · MoE | 9.5 GB | 15.8 GB | 28.9 GB | | [Mellum2.1-12B-A2.5B-Thinking-MTP](https://whatcanirunai.vercel.app/models/mellum2.1-12b-a2.5b-thinking-mtp/)12.1B params · JetBrains · MoE | 9.5 GB | 15.8 GB | 28.9 GB | | [gemma-3-12b-it](https://whatcanirunai.vercel.app/models/gemma-3-12b-it/)12.2B params · google | 9.5 GB | 15.9 GB | 29.0 GB | | [gemma-3-12b-pt](https://whatcanirunai.vercel.app/models/gemma-3-12b-pt/)12.2B params · google | 9.5 GB | 15.9 GB | 29.0 GB | | [Mistral-Nemo-Instruct-2407](https://whatcanirunai.vercel.app/models/mistral-nemo-instruct-2407/)12.2B params · mistralai | 9.5 GB | 16.0 GB | 29.2 GB | | [OLMo-2-1124-13B-Instruct](https://whatcanirunai.vercel.app/models/olmo-2-1124-13b-instruct/)13.7B params · allenai | 10.6 GB | 17.8 GB | 32.5 GB | | [phi-4](https://whatcanirunai.vercel.app/models/phi-4/)14.7B params · microsoft | 11.2 GB | 18.9 GB | 34.7 GB | | [Phi-4-reasoning](https://whatcanirunai.vercel.app/models/phi-4-reasoning/)14.7B params · microsoft | 11.2 GB | 18.9 GB | 34.7 GB | | [Phi-4-reasoning-plus](https://whatcanirunai.vercel.app/models/phi-4-reasoning-plus/)14.7B params · microsoft | 11.2 GB | 18.9 GB | 34.7 GB | | [HyperCLOVAX-SEED-Think-14B](https://whatcanirunai.vercel.app/models/hyperclovax-seed-think-14b/)14.7B params · naver-hyperclovax | 11.3 GB | 19.0 GB | 34.9 GB | | [Qwen3-14B](https://whatcanirunai.vercel.app/models/qwen3-14b/)14.8B params · Qwen | 11.3 GB | 19.0 GB | 35.0 GB | | [JanusCoder-14B](https://whatcanirunai.vercel.app/models/januscoder-14b/)14.8B params · internlm | 11.3 GB | 19.0 GB | 35.0 GB | | [Qwen2.5-Coder-14B-Instruct](https://whatcanirunai.vercel.app/models/qwen2.5-coder-14b-instruct/)14.8B params · Qwen | 11.3 GB | 19.0 GB | 35.0 GB | | [DeepSeek-R1-Distill-Qwen-14B](https://whatcanirunai.vercel.app/models/deepseek-r1-distill-qwen-14b/)14.8B params · deepseek-ai | 11.3 GB | 19.0 GB | 35.0 GB | | [Qwen2.5-14B-Instruct](https://whatcanirunai.vercel.app/models/qwen2.5-14b-instruct/)14.8B params · Qwen | 11.3 GB | 19.0 GB | 35.0 GB | | [Qwen2.5-14B](https://whatcanirunai.vercel.app/models/qwen2.5-14b/)14.8B params · Qwen | 11.3 GB | 19.0 GB | 35.0 GB | | [Qwen2.5-14B-Instruct-1M](https://whatcanirunai.vercel.app/models/qwen2.5-14b-instruct-1m/)14.8B params · Qwen | 11.3 GB | 19.0 GB | 35.0 GB | | [Qwen2.5-Coder-14B](https://whatcanirunai.vercel.app/models/qwen2.5-coder-14b/)14.8B params · Qwen | 11.3 GB | 19.0 GB | 35.0 GB | | [Carbon-X (DNA)](https://whatcanirunai.vercel.app/models/carbon-x/)14.8B params · HuggingFaceBio | 11.3 GB | 19.1 GB | 35.1 GB | | [Apriel-1.5-15b-Thinker](https://whatcanirunai.vercel.app/models/apriel-1.5-15b-thinker/)14.9B params · ServiceNow-AI | 11.4 GB | 19.2 GB | 35.2 GB | | [Apriel-1.6-15b-Thinker](https://whatcanirunai.vercel.app/models/apriel-1.6-15b-thinker/)14.9B params · ServiceNow-AI | 11.4 GB | 19.2 GB | 35.2 GB | | [Apriel-Nemotron-15b-Thinker](https://whatcanirunai.vercel.app/models/apriel-nemotron-15b-thinker/)15.0B params · ServiceNow-AI | 11.4 GB | 19.3 GB | 35.4 GB | | [Phi-4-reasoning-vision-15B](https://whatcanirunai.vercel.app/models/phi-4-reasoning-vision-15b/)15.1B params · microsoft | 11.5 GB | 19.5 GB | 35.8 GB | | [kanana-1.5-15.7b-a3b-instruct](https://whatcanirunai.vercel.app/models/kanana-1.5-15.7b-a3b-instruct/)15.7B params · kakaocorp · MoE | 11.9 GB | 20.2 GB | 37.1 GB | | [DeepSeek-Coder-V2-Lite-Instruct](https://whatcanirunai.vercel.app/models/deepseek-coder-v2-lite-instruct/)15.7B params · deepseek-ai · MoE | 12.0 GB | 20.2 GB | 37.1 GB | | [Instella-MoE-16B-A3B-Think](https://whatcanirunai.vercel.app/models/instella-moe-16b-a3b-think/)15.9B params · amd · MoE | 12.1 GB | 20.4 GB | 37.5 GB | | [Moonlight-16B-A3B-Instruct](https://whatcanirunai.vercel.app/models/moonlight-16b-a3b-instruct/)16.0B params · moonshotai · MoE | 12.1 GB | 20.5 GB | 37.7 GB | | [Moonlight-16B-A3B](https://whatcanirunai.vercel.app/models/moonlight-16b-a3b/)16.0B params · moonshotai · MoE | 12.1 GB | 20.5 GB | 37.7 GB | Have a different amount of memory? Try the [VRAM calculator](https://whatcanirunai.vercel.app/vram-calculator/) or see [best LLMs by VRAM size](https://whatcanirunai.vercel.app/best-llm-for/). ## Quick answers How much VRAM do I need for a 12B–14B model? About 11.3 GB at Q4\_K\_M, 19.0 GB at Q8\_0 and 35.0 GB at FP16, at a \~4k context. That is for Qwen3-14B (14.8B parameters): weights plus about 15% for the KV cache and 1 GB for the runtime. How much VRAM does a 12B–14B model need for 32k context? About 16.0 GB at Q4\_K\_M with an FP16 KV cache, or 13.3 GB with a Q8 cache, based on Qwen3-14B's published config. Models with different attention layouts need more or less. What is the cheapest GPU for a 12B–14B model? Of the GPUs we track, the Radeon RX 7600 XT (16 GB, $329 list price) is the cheapest that runs it fully on the GPU at Q4. Can a Mac run a 12B–14B model? Yes. The cheapest Mac or unified-memory mini PC we track that runs it at Q4 is the Mac mini M6 24GB (24 GB, about 18.0 GB usable, $1,299). Does a MoE model of the same size need less VRAM? No. A mixture-of-experts model still has to hold all of its weights in memory, so it needs about the same VRAM as a dense model with the same total parameters. It runs faster, because only a few experts are read per token. Other sizes: [1B–2B](https://whatcanirunai.vercel.app/how-much-vram/1b/) · [2B–3B](https://whatcanirunai.vercel.app/how-much-vram/3b/) · [4B–6B](https://whatcanirunai.vercel.app/how-much-vram/4b/) · [7B–8B](https://whatcanirunai.vercel.app/how-much-vram/8b/) · [16B–21B](https://whatcanirunai.vercel.app/how-much-vram/20b/) · [24B–27B](https://whatcanirunai.vercel.app/how-much-vram/27b/) · [30B–32B](https://whatcanirunai.vercel.app/how-much-vram/32b/) · [40B–50B](https://whatcanirunai.vercel.app/how-much-vram/49b/) · [70B](https://whatcanirunai.vercel.app/how-much-vram/70b/) · [120B](https://whatcanirunai.vercel.app/how-much-vram/120b/) · [200B–300B](https://whatcanirunai.vercel.app/how-much-vram/235b/) · [300B–480B](https://whatcanirunai.vercel.app/how-much-vram/400b/) · [500B–800B](https://whatcanirunai.vercel.app/how-much-vram/671b/) · [1T+](https://whatcanirunai.vercel.app/how-much-vram/1t/) · [All sizes](https://whatcanirunai.vercel.app/how-much-vram/) · [Ollama VRAM by tag](https://whatcanirunai.vercel.app/ollama-vram-requirements/) · [GGUF sizes by quant](https://whatcanirunai.vercel.app/gguf-vram-requirements/) --- Agent guide: https://whatcanirunai.vercel.app/llms.txt · JSON API: https://whatcanirunai.vercel.app/developers/