# How much VRAM do you need for a 500B–800B model? > A 500B–800B LLM needs about 478.2 GB of VRAM at Q4, 837.4 GB at Q8 and 1575.3 GB at FP16. Context-length table and the cheapest GPU and Mac that fit. Source: https://whatcanirunai.vercel.app/how-much-vram/671b/ # How much VRAM do you need for a 500B–800B model? Short answer: about **478.2 GB** at Q4\_K\_M, the usual quant for local use. Below: every quant, longer contexts, the cheapest hardware that fits, and every 500B–800B open model we track. Q4\_K\_M 478.2 GB 415.0 GB weights Q8\_0 837.4 GB 727.3 GB weights FP16 1575.3 GB 1369.0 GB weights Worked out for [DeepSeek-R1](https://whatcanirunai.vercel.app/models/deepseek-r1/) (684.5B 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 500B–800B model at each quant__ | Setting | Memory needed | Cheapest GPU | Cheapest Mac / mini PC | | ---------------------- | ------------- | ------------- | ---------------------- | | Q4\_K\_M, \~4k context | 478.2 GB | None we track | None we track | | Q8\_0, \~4k context | 837.4 GB | None we track | None we track | | FP16, \~4k context | 1575.3 GB | None we track | None we track | No single consumer GPU we track holds a 500B–800B 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. ## 500B–800B models we track __Memory needed by each 500B–800B model we track__ | Model | Q4\_K\_M | Q8\_0 | FP16 | | --------------------------------------------------------------------------------------------------------------------- | -------- | -------- | --------- | | [Beam-1.0-501B-A23B](https://whatcanirunai.vercel.app/models/beam-1.0-501b-a23b/)501.3B params · reflection · MoE | 350.5 GB | 613.5 GB | 1153.9 GB | | [NVIDIA-Nemotron-3-Ultra-550B-A55B-BF16](https://whatcanirunai.vercel.app/models/nvidia-nemotron-3-ultra-550b-a55b-bf16/)560.5B params · nvidia · MoE | 391.8 GB | 685.9 GB | 1290.2 GB | | [LongCat-Flash-Chat](https://whatcanirunai.vercel.app/models/longcat-flash-chat/)561.9B params · meituan-longcat | 392.7 GB | 687.5 GB | 1293.3 GB | | [LongCat-Flash-Thinking](https://whatcanirunai.vercel.app/models/longcat-flash-thinking/)561.9B params · meituan-longcat | 392.7 GB | 687.5 GB | 1293.3 GB | | [LongCat-Flash-Thinking-2601](https://whatcanirunai.vercel.app/models/longcat-flash-thinking-2601/)561.9B params · meituan-longcat | 392.7 GB | 687.5 GB | 1293.3 GB | | [Ling-3.1-flash](https://whatcanirunai.vercel.app/models/ling-3.1-flash/)572.1B params · inclusionAI · MoE | 399.9 GB | 700.1 GB | 1316.9 GB | | [Ling-3.1-flash-1019](https://whatcanirunai.vercel.app/models/ling-3.1-flash-1019/)572.1B params · inclusionAI · MoE | 399.9 GB | 700.1 GB | 1316.9 GB | | [Step-5-Preview](https://whatcanirunai.vercel.app/models/step-5-preview/)604.3B params · stepfun-ai · MoE | 422.3 GB | 739.4 GB | 1391.0 GB | | [DeepSeek-R1](https://whatcanirunai.vercel.app/models/deepseek-r1/)684.5B params · deepseek-ai · MoE | 478.2 GB | 837.4 GB | 1575.3 GB | | [DeepSeek-R1-Zero](https://whatcanirunai.vercel.app/models/deepseek-r1-zero/)684.5B params · deepseek-ai · MoE | 478.2 GB | 837.4 GB | 1575.3 GB | | [DeepSeek-V3-0324](https://whatcanirunai.vercel.app/models/deepseek-v3-0324/)684.5B params · deepseek-ai · MoE | 478.2 GB | 837.4 GB | 1575.4 GB | | [DeepSeek-R1-0528](https://whatcanirunai.vercel.app/models/deepseek-r1-0528/)684.5B params · deepseek-ai · MoE | 478.2 GB | 837.4 GB | 1575.4 GB | | [DeepSeek-V3.1-Base](https://whatcanirunai.vercel.app/models/deepseek-v3.1-base/)684.5B params · deepseek-ai · MoE | 478.2 GB | 837.4 GB | 1575.4 GB | | [DeepSeek-V3.2](https://whatcanirunai.vercel.app/models/deepseek-v3.2/)685.4B params · deepseek-ai · MoE | 478.8 GB | 838.4 GB | 1577.3 GB | | [DeepSeek-V3.2-Exp](https://whatcanirunai.vercel.app/models/deepseek-v3.2-exp/)685.4B params · deepseek-ai · MoE | 478.9 GB | 838.5 GB | 1577.4 GB | | [DeepSeek-V3.2-Speciale](https://whatcanirunai.vercel.app/models/deepseek-v3.2-speciale/)685.4B params · deepseek-ai · MoE | 478.9 GB | 838.5 GB | 1577.4 GB | | [K-EXAONE-2.0-750B-A37B](https://whatcanirunai.vercel.app/models/k-exaone-2.0-750b-a37b/)749.4B params · LGAI-EXAONE · MoE | 523.4 GB | 916.6 GB | 1724.5 GB | | [GLM-5.3](https://whatcanirunai.vercel.app/models/glm-5.3/)753.3B params · zai-org | 526.2 GB | 921.5 GB | 1733.7 GB | | [GLM-5.2](https://whatcanirunai.vercel.app/models/glm-5.2/)753.3B params · zai-org · MoE | 526.2 GB | 921.5 GB | 1733.7 GB | | [GLM-5.3-BF16](https://whatcanirunai.vercel.app/models/glm-5.3-bf16/)753.3B params · zai-org · MoE | 526.2 GB | 921.5 GB | 1733.7 GB | | [GLM-5](https://whatcanirunai.vercel.app/models/glm-5/)753.9B params · zai-org · MoE | 526.6 GB | 922.1 GB | 1734.9 GB | | [GLM-5.1](https://whatcanirunai.vercel.app/models/glm-5.1/)753.9B params · zai-org · MoE | 526.6 GB | 922.1 GB | 1734.9 GB | | [Hy4-preview](https://whatcanirunai.vercel.app/models/hy4-preview/)780.0B params · tencent · MoE | 544.8 GB | 954.0 GB | 1794.9 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 500B–800B model? About 478.2 GB at Q4\_K\_M, 837.4 GB at Q8\_0 and 1575.3 GB at FP16, at a \~4k context. That is for DeepSeek-R1 (684.5B parameters): weights plus about 15% for the KV cache and 1 GB for the runtime. What is the cheapest GPU for a 500B–800B 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 500B–800B 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/) · [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/