# How much VRAM do you need for a 70B model? | What Can I Run? > A 70B LLM needs about 50.2 GB of VRAM at Q4, 87.2 GB at Q8 and 163.3 GB at FP16. Context-length table and the cheapest GPU and Mac that fit. Source: https://whatcanirunai.vercel.app/how-much-vram/70b/ # How much VRAM do you need for a 70B model? Short answer: about **50.2 GB** at Q4\_K\_M, the usual quant for local use. Below: every quant, longer contexts, the cheapest hardware that fits, and every 70B open model we track. Q4\_K\_M 50.2 GB 42.8 GB weights Q8\_0 87.2 GB 75.0 GB weights FP16 163.3 GB 141.1 GB weights Worked out for [Llama-3.3-70B-Instruct](https://whatcanirunai.vercel.app/models/llama-3.3-70b-instruct/) (70.6B 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: **NVIDIA RTX Pro 6000 Blackwell 96GB** (96 GB, $8,565). Cheapest Mac or mini PC: **Framework Desktop (Ryzen AI Max+ 395) 128GB** ($3,449). ## 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 70B model at each quant__ | Setting | Memory needed | Cheapest GPU | Cheapest Mac / mini PC | | ---------------------- | ------------- | --------------------------------------------------------------------------------------------- | ----------------------------------------------------------------------------------------------------- | | Q4\_K\_M, \~4k context | 50.2 GB | [NVIDIA RTX Pro 6000 Blackwell 96GB](https://whatcanirunai.vercel.app/hardware/rtx-pro-6000-blackwell/) 96 GB usable · $8,565 | [Framework Desktop (Ryzen AI Max+ 395) 128GB](https://whatcanirunai.vercel.app/hardware/framework-desktop-128/) 96 GB usable · $3,449 | | Q8\_0, \~4k context | 87.2 GB | None we track | [Mac Studio M5 Ultra 256GB](https://whatcanirunai.vercel.app/hardware/mac-studio-m5-ultra-256/) 192 GB usable · $9,499 | | FP16, \~4k context | 163.3 GB | None we track | [Mac Studio M5 Ultra 256GB](https://whatcanirunai.vercel.app/hardware/mac-studio-m5-ultra-256/) 192 GB usable · $9,499 | | Q4\_K\_M, 32k context | 59.6 GB | [NVIDIA RTX Pro 6000 Blackwell 96GB](https://whatcanirunai.vercel.app/hardware/rtx-pro-6000-blackwell/) 96 GB usable · $8,565 | [Framework Desktop (Ryzen AI Max+ 395) 128GB](https://whatcanirunai.vercel.app/hardware/framework-desktop-128/) 96 GB usable · $3,449 | ## Memory by context length Each extra token of context adds to the KV cache. Numbers for Llama-3.3-70B-Instruct, from its published config; a Q8 KV cache halves the cache part. __Memory needed for a 70B model by context length__ | Context | Q4\_K\_M, FP16 cache | Q4\_K\_M, Q8 cache | Q8\_0, FP16 cache | | ----------- | -------------------- | ------------------ | ----------------- | | 4k tokens | 50.2 GB | 50.2 GB | 87.2 GB | | 8k tokens | 51.5 GB | 50.2 GB | 88.5 GB | | 16k tokens | 54.2 GB | 51.5 GB | 91.2 GB | | 32k tokens | 59.6 GB | 54.2 GB | 96.6 GB | | 64k tokens | 70.3 GB | 59.6 GB | 107.3 GB | | 128k tokens | 91.8 GB | 70.3 GB | 128.8 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 70B model at Q4__ | Hardware | Q4 need vs. usable | Q4 / Q8 / FP16 | Est. tok/s | | ------------------------------------------------------------------------------------------------------ | ------------------- | -------------------------- | ---------- | | [Framework Desktop (Ryzen AI Max+ 395) 128GB](https://whatcanirunai.vercel.app/hardware/framework-desktop-128/)unified memory · $3,449 | **50.2** / 96.0 GB | Q4FitsQ8TightFP16Won’t fit | 3–5 | | [MacBook Pro M4 Max 128GB](https://whatcanirunai.vercel.app/hardware/macbook-pro-m4-max-128/)unified memory · $4,999 | **50.2** / 96.0 GB | Q4FitsQ8TightFP16Won’t fit | 6–10 | | [MacBook Pro M5 Max 128GB](https://whatcanirunai.vercel.app/hardware/macbook-pro-m5-max-128/)unified memory · $6,699 | **50.2** / 96.0 GB | Q4FitsQ8TightFP16Won’t fit | 6–11 | | [NVIDIA DGX Spark 128GB](https://whatcanirunai.vercel.app/hardware/dgx-spark-128/)unified memory · $6,950 | **50.2** / 96.0 GB | Q4FitsQ8TightFP16Won’t fit | 3–5 | | [NVIDIA RTX Pro 6000 Blackwell 96GB](https://whatcanirunai.vercel.app/hardware/rtx-pro-6000-blackwell/)GPU · $8,565 | **50.2** / 96.0 GB | Q4FitsQ8TightFP16Won’t fit | 25–36 | | [Mac Studio M5 Ultra 256GB](https://whatcanirunai.vercel.app/hardware/mac-studio-m5-ultra-256/)unified memory · $9,499 | **50.2** / 192.0 GB | Q4FitsQ8FitsFP16Fits | 13–22 | | [Mac Studio M5 Ultra 512GB](https://whatcanirunai.vercel.app/hardware/mac-studio-m5-ultra-512/)unified memory · $22,299 | **50.2** / 384.0 GB | Q4FitsQ8FitsFP16Fits | 13–22 | ## 70B models we track __Memory needed by each 70B model we track__ | Model | Q4\_K\_M | Q8\_0 | FP16 | | ------------------------------------------------------------------------------------------------- | -------- | ------- | -------- | | [LongCat-Flash-Lite](https://whatcanirunai.vercel.app/models/longcat-flash-lite/)69.1B params · meituan-longcat | 49.2 GB | 85.4 GB | 159.9 GB | | [Llama-3.3-70B-Instruct](https://whatcanirunai.vercel.app/models/llama-3.3-70b-instruct/)70.6B params · meta-llama | 50.2 GB | 87.2 GB | 163.3 GB | | [DeepSeek-R1-Distill-Llama-70B](https://whatcanirunai.vercel.app/models/deepseek-r1-distill-llama-70b/)70.6B params · deepseek-ai | 50.2 GB | 87.2 GB | 163.3 GB | | [Llama-3.1-70B](https://whatcanirunai.vercel.app/models/llama-3.1-70b/)70.6B params · meta-llama | 50.2 GB | 87.2 GB | 163.3 GB | | [Hermes-2-Theta-Llama-3-70B](https://whatcanirunai.vercel.app/models/hermes-2-theta-llama-3-70b/)70.6B params · NousResearch | 50.2 GB | 87.2 GB | 163.3 GB | | [Meta-Llama-3.1-70B-Instruct](https://whatcanirunai.vercel.app/models/meta-llama-3.1-70b-instruct/)70.6B params · NousResearch | 50.2 GB | 87.2 GB | 163.3 GB | | [Apertus-70B-Instruct-2509](https://whatcanirunai.vercel.app/models/apertus-70b-instruct-2509/)70.6B params · swiss-ai | 50.2 GB | 87.3 GB | 163.4 GB | | [Apertus-70B-2509](https://whatcanirunai.vercel.app/models/apertus-70b-2509/)70.6B params · swiss-ai | 50.2 GB | 87.3 GB | 163.4 GB | | [Qwen2.5-72B-Instruct](https://whatcanirunai.vercel.app/models/qwen2.5-72b-instruct/)72.7B params · Qwen | 51.7 GB | 89.8 GB | 168.2 GB | | [UI-TARS-72B-DPO](https://whatcanirunai.vercel.app/models/ui-tars-72b-dpo/)73.4B params · ByteDance-Seed | 52.2 GB | 90.7 GB | 169.8 GB | | [Qwen2.5-VL-72B-Instruct](https://whatcanirunai.vercel.app/models/qwen2.5-vl-72b-instruct/)73.4B params · Qwen | 52.2 GB | 90.7 GB | 169.8 GB | | [Kolibri-1](https://whatcanirunai.vercel.app/models/kolibri-1/)78.1B params · Aleph-Alpha | 55.5 GB | 96.4 GB | 180.6 GB | | [Qwen3-Coder-Next](https://whatcanirunai.vercel.app/models/qwen3-coder-next/)79.7B params · Qwen · MoE | 56.5 GB | 98.4 GB | 184.3 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 70B model? About 50.2 GB at Q4\_K\_M, 87.2 GB at Q8\_0 and 163.3 GB at FP16, at a \~4k context. That is for Llama-3.3-70B-Instruct (70.6B parameters): weights plus about 15% for the KV cache and 1 GB for the runtime. How much VRAM does a 70B model need for 32k context? About 59.6 GB at Q4\_K\_M with an FP16 KV cache, or 54.2 GB with a Q8 cache, based on Llama-3.3-70B-Instruct's published config. Models with different attention layouts need more or less. What is the cheapest GPU for a 70B model? Of the GPUs we track, the NVIDIA RTX Pro 6000 Blackwell 96GB (96 GB, $8,565 list price) is the cheapest that runs it fully on the GPU at Q4. Can a Mac run a 70B model? Yes. The cheapest Mac or unified-memory mini PC we track that runs it at Q4 is the Framework Desktop (Ryzen AI Max+ 395) 128GB (128 GB, about 96.0 GB usable, $3,449). 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/) · [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/