# How much VRAM do you need for a 1T+ model? | What Can I Run? > A 1T+ LLM needs about 716.6 GB of VRAM at Q4, 1255.1 GB at Q8 and 2361.7 GB at FP16. Context-length table and the cheapest GPU and Mac that fit. Source: https://whatcanirunai.vercel.app/how-much-vram/1t/ # How much VRAM do you need for a 1T+ model? Short answer: about **716.6 GB** at Q4\_K\_M, the usual quant for local use. Below: every quant, longer contexts, the cheapest hardware that fits, and every 1T+ open model we track. Q4\_K\_M 716.6 GB 622.3 GB weights Q8\_0 1255.1 GB 1090.6 GB weights FP16 2361.7 GB 2052.8 GB weights Worked out for [Kimi-K2-Instruct](https://whatcanirunai.vercel.app/models/kimi-k2-instruct/) (1026.4B parameters, from the Hugging Face API). Formula: weights × 1.15 + 1 GB at a \~4k context ([how we calculate](https://whatcanirunai.vercel.app/method/)). No single consumer GPU we track fits it at Q4. ## 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 1T+ model at each quant__ | Setting | Memory needed | Cheapest GPU | Cheapest Mac / mini PC | | ---------------------- | ------------- | ------------- | ---------------------- | | Q4\_K\_M, \~4k context | 716.6 GB | None we track | None we track | | Q8\_0, \~4k context | 1255.1 GB | None we track | None we track | | FP16, \~4k context | 2361.7 GB | None we track | None we track | No single consumer GPU we track holds a 1T+ model at Q4\. Your options: two or more GPUs (see the [ready-made builds](https://whatcanirunai.vercel.app/builds/) or the [planner](https://whatcanirunai.vercel.app/planner/)), a large unified-memory machine (see the [best Mac for local LLMs](https://whatcanirunai.vercel.app/best-mac-for-local-llm/)), or a smaller quant with lower quality. ## 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)); MoE shows an upper bound only.FitsTight None of the devices we track run it at Q4. ## 1T+ models we track __Memory needed by each 1T+ model we track__ | Model | Q4\_K\_M | Q8\_0 | FP16 | | ------------------------------------------------------------------------------------------------ | --------- | --------- | --------- | | [Muse-Spark-Open-816B-A42B](https://whatcanirunai.vercel.app/models/muse-spark-open-816b-a42b/)822.0B params · meta-models · MoE | 574.1 GB | 1005.4 GB | 1891.7 GB | | [Intern-S1-Pro](https://whatcanirunai.vercel.app/models/intern-s1-pro/)917.0B params · internlm · MoE | 640.4 GB | 1121.5 GB | 2110.2 GB | | [Inkling](https://whatcanirunai.vercel.app/models/inkling/)952.4B params · thinkingmachines · MoE | 665.0 GB | 1164.7 GB | 2191.5 GB | | [Ling-1T](https://whatcanirunai.vercel.app/models/ling-1t/)999.7B params · inclusionAI · MoE | 698.0 GB | 1222.5 GB | 2300.3 GB | | [Ring-1T](https://whatcanirunai.vercel.app/models/ring-1t/)999.7B params · inclusionAI · MoE | 698.0 GB | 1222.5 GB | 2300.3 GB | | [Ring-2.5-1T](https://whatcanirunai.vercel.app/models/ring-2.5-1t/)1012.2B params · inclusionAI · MoE | 706.7 GB | 1237.8 GB | 2329.1 GB | | [Ling-2.5-1T](https://whatcanirunai.vercel.app/models/ling-2.5-1t/)1012.2B params · inclusionAI · MoE | 706.7 GB | 1237.8 GB | 2329.1 GB | | [MiMo-V2.5-Pro](https://whatcanirunai.vercel.app/models/mimo-v2.5-pro/)1023.2B params · XiaomiMiMo · MoE | 714.4 GB | 1251.2 GB | 2354.3 GB | | [MiMo-V2.6-Pro-RL](https://whatcanirunai.vercel.app/models/mimo-v2.6-pro-rl/)1024.2B params · XiaomiMiMo · MoE | 715.1 GB | 1252.5 GB | 2356.7 GB | | [MiMo-V2.6-Pro-MOPD](https://whatcanirunai.vercel.app/models/mimo-v2.6-pro-mopd/)1024.2B params · XiaomiMiMo · MoE | 715.1 GB | 1252.5 GB | 2356.7 GB | | [Kimi-K2-Instruct](https://whatcanirunai.vercel.app/models/kimi-k2-instruct/)1026.4B params · moonshotai · MoE | 716.6 GB | 1255.1 GB | 2361.7 GB | | [Kimi-K2-Thinking](https://whatcanirunai.vercel.app/models/kimi-k2-thinking/)1026.4B params · moonshotai · MoE | 716.6 GB | 1255.1 GB | 2361.7 GB | | [Kimi-K2-Instruct-0905](https://whatcanirunai.vercel.app/models/kimi-k2-instruct-0905/)1026.4B params · moonshotai · MoE | 716.6 GB | 1255.1 GB | 2361.7 GB | | [Kimi-K2.5](https://whatcanirunai.vercel.app/models/kimi-k2.5/)1026.9B params · moonshotai · MoE | 716.9 GB | 1255.7 GB | 2362.8 GB | | [Kimi-K2.6](https://whatcanirunai.vercel.app/models/kimi-k2.6/)1026.9B params · moonshotai · MoE | 716.9 GB | 1255.7 GB | 2362.8 GB | | [Kimi-K2.7-Code](https://whatcanirunai.vercel.app/models/kimi-k2.7-code/)1026.9B params · moonshotai · MoE | 716.9 GB | 1255.7 GB | 2362.8 GB | | [DeepSeek-V4-Pro](https://whatcanirunai.vercel.app/models/deepseek-v4-pro/)1598.8B params · deepseek-ai · MoE | 1115.7 GB | 1954.6 GB | 3678.3 GB | | [DeepSeek-V4-Pro-0813](https://whatcanirunai.vercel.app/models/deepseek-v4-pro-0813/)1650.5B params · deepseek-ai · MoE | 1151.7 GB | 2017.7 GB | 3797.1 GB | | [LongCat-2.0](https://whatcanirunai.vercel.app/models/longcat-2.0/)1775.6B params · meituan-longcat | 1238.9 GB | 2170.5 GB | 4084.8 GB | | [LongCat-2.5](https://whatcanirunai.vercel.app/models/longcat-2.5/)1779.8B params · meituan-longcat · MoE | 1241.8 GB | 2175.6 GB | 4094.4 GB | | [Qwen3.8-2.4T-A95B](https://whatcanirunai.vercel.app/models/qwen3.8-2.4t-a95b/)2446.2B params · Qwen · MoE | 1706.4 GB | 2989.9 GB | 5627.2 GB | | [Kimi-K3](https://whatcanirunai.vercel.app/models/kimi-k3/)2779.9B params · moonshotai · MoE | 1939.1 GB | 3397.7 GB | 6394.8 GB | | [Kimi-K3.1-Pro](https://whatcanirunai.vercel.app/models/kimi-k3.1-pro/)2780.1B params · moonshotai · MoE | 1939.3 GB | 3397.9 GB | 6395.3 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 1T+ model? About 716.6 GB at Q4\_K\_M, 1255.1 GB at Q8\_0 and 2361.7 GB at FP16, at a \~4k context. That is for Kimi-K2-Instruct (1026.4B parameters): weights plus about 15% for the KV cache and 1 GB for the runtime. What is the cheapest GPU for a 1T+ model? None of the single consumer GPUs we track hold it at Q4\. Use several GPUs, a large unified-memory machine, or CPU offload (slow). Can a Mac run a 1T+ model? None of the Macs or mini PCs we track fit it at Q4. 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/) · [12B–14B](https://whatcanirunai.vercel.app/how-much-vram/14b/) · [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/) · [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/