# How much VRAM do you need for a 120B model? > A 120B LLM needs about 82.5 GB of VRAM at Q4, 143.8 GB at Q8 and 269.7 GB at FP16. Context-length table and the cheapest GPU and Mac that fit. Source: https://whatcanirunai.vercel.app/how-much-vram/120b/ # How much VRAM do you need for a 120B model? Short answer: about **82.5 GB** at Q4\_K\_M, the usual quant for local use. Below: every quant, longer contexts, the cheapest hardware that fits, and every 120B open model we track. Q4\_K\_M 82.5 GB 70.8 GB weights Q8\_0 143.8 GB 124.1 GB weights FP16 269.7 GB 233.7 GB weights Worked out for [gpt-oss-120b](https://whatcanirunai.vercel.app/models/gpt-oss-120b/) (116.8B 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: **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 120B model at each quant__ | Setting | Memory needed | Cheapest GPU | Cheapest Mac / mini PC | | ---------------------- | ------------- | --------------------------------------------------------------------------------------------- | ----------------------------------------------------------------------------------------------------- | | Q4\_K\_M, \~4k context | 82.5 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 | 143.8 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 | 269.7 GB | None we track | [Mac Studio M5 Ultra 512GB](https://whatcanirunai.vercel.app/hardware/mac-studio-m5-ultra-512/) 384 GB usable · $22,299 | | Q4\_K\_M, 32k context | 83.5 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 gpt-oss-120b, from its published config; a Q8 KV cache halves the cache part. __Memory needed for a 120B model by context length__ | Context | Q4\_K\_M, FP16 cache | Q4\_K\_M, Q8 cache | Q8\_0, FP16 cache | | ----------- | -------------------- | ------------------ | ----------------- | | 4k tokens | 82.5 GB | 82.5 GB | 143.8 GB | | 8k tokens | 82.6 GB | 82.5 GB | 143.9 GB | | 16k tokens | 82.9 GB | 82.6 GB | 144.2 GB | | 32k tokens | 83.5 GB | 82.9 GB | 144.8 GB | | 64k tokens | 84.7 GB | 83.5 GB | 146.0 GB | | 128k tokens | 87.1 GB | 84.7 GB | 148.4 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)); MoE shows an upper bound only.FitsTight __Tracked hardware that runs a 120B 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 | **82.5** / 96.0 GB | Q4FitsQ8Won’t fitFP16Won’t fit | up to 66 | | [MacBook Pro M4 Max 128GB](https://whatcanirunai.vercel.app/hardware/macbook-pro-m4-max-128/)unified memory · $4,999 | **82.5** / 96.0 GB | Q4FitsQ8Won’t fitFP16Won’t fit | up to 141 | | [MacBook Pro M5 Max 128GB](https://whatcanirunai.vercel.app/hardware/macbook-pro-m5-max-128/)unified memory · $6,699 | **82.5** / 96.0 GB | Q4FitsQ8Won’t fitFP16Won’t fit | up to 159 | | [NVIDIA DGX Spark 128GB](https://whatcanirunai.vercel.app/hardware/dgx-spark-128/)unified memory · $6,950 | **82.5** / 96.0 GB | Q4FitsQ8Won’t fitFP16Won’t fit | up to 71 | | [NVIDIA RTX Pro 6000 Blackwell 96GB](https://whatcanirunai.vercel.app/hardware/rtx-pro-6000-blackwell/)GPU · $8,565 | **82.5** / 96.0 GB | Q4FitsQ8Won’t fitFP16Won’t fit | up to 493 | | [Mac Studio M5 Ultra 256GB](https://whatcanirunai.vercel.app/hardware/mac-studio-m5-ultra-256/)unified memory · $9,499 | **82.5** / 192.0 GB | Q4FitsQ8FitsFP16Won’t fit | up to 310 | | [Mac Studio M5 Ultra 512GB](https://whatcanirunai.vercel.app/hardware/mac-studio-m5-ultra-512/)unified memory · $22,299 | **82.5** / 384.0 GB | Q4FitsQ8FitsFP16Fits | up to 310 | ## 120B models we track __Memory needed by each 120B model we track__ | Model | Q4\_K\_M | Q8\_0 | FP16 | | --------------------------------------------------------------------------------------------------------------------- | -------- | -------- | -------- | | [Solar-Open-100B](https://whatcanirunai.vercel.app/models/solar-open-100b/)102.7B params · upstage · MoE | 72.6 GB | 126.4 GB | 237.1 GB | | [Ling-flash-2.0](https://whatcanirunai.vercel.app/models/ling-flash-2.0/)102.9B params · inclusionAI · MoE | 72.7 GB | 126.7 GB | 237.6 GB | | [LLaDA2.1-flash](https://whatcanirunai.vercel.app/models/llada2.1-flash/)102.9B params · inclusionAI · MoE | 72.7 GB | 126.7 GB | 237.6 GB | | [Ring-flash-2.0](https://whatcanirunai.vercel.app/models/ring-flash-2.0/)102.9B params · inclusionAI · MoE | 72.7 GB | 126.7 GB | 237.6 GB | | [LLaDA2.2-flash](https://whatcanirunai.vercel.app/models/llada2.2-flash/)102.9B params · inclusionAI · MoE | 72.7 GB | 126.7 GB | 237.6 GB | | [sarvam-105b](https://whatcanirunai.vercel.app/models/sarvam-105b/)106.0B params · sarvamai · MoE | 74.9 GB | 130.6 GB | 244.9 GB | | [Ling-2.6-flash](https://whatcanirunai.vercel.app/models/ling-2.6-flash/)107.5B params · inclusionAI · MoE | 75.9 GB | 132.3 GB | 248.2 GB | | [GLM-4.5V](https://whatcanirunai.vercel.app/models/glm-4.5v/)107.7B params · zai-org · MoE | 76.1 GB | 132.6 GB | 248.7 GB | | [GLM-4.6V](https://whatcanirunai.vercel.app/models/glm-4.6v/)107.7B params · zai-org · MoE | 76.1 GB | 132.6 GB | 248.7 GB | | [Llama-4-Scout-17B-16E-Instruct](https://whatcanirunai.vercel.app/models/llama-4-scout-17b-16e-instruct/)108.6B params · meta-llama · MoE | 76.7 GB | 133.7 GB | 250.9 GB | | [Llama-4-Scout-17B-16E](https://whatcanirunai.vercel.app/models/llama-4-scout-17b-16e/)108.6B params · meta-llama · MoE | 76.7 GB | 133.7 GB | 250.9 GB | | [GLM-4.5-Air](https://whatcanirunai.vercel.app/models/glm-4.5-air/)110.5B params · zai-org · MoE | 78.0 GB | 136.0 GB | 255.1 GB | | [c4ai-command-a-03-2025](https://whatcanirunai.vercel.app/models/c4ai-command-a-03-2025/)111.1B params · CohereLabs | 78.4 GB | 136.7 GB | 256.4 GB | | [gpt-oss-120b](https://whatcanirunai.vercel.app/models/gpt-oss-120b/)116.8B params · openai · MoE | 82.5 GB | 143.8 GB | 269.7 GB | | [Laguna-S-2.1](https://whatcanirunai.vercel.app/models/laguna-s-2.1/)117.6B params · poolside · MoE | 83.0 GB | 144.6 GB | 271.4 GB | | [Youtu-LLM-A7B-Base](https://whatcanirunai.vercel.app/models/youtu-llm-a7b-base/)118.4B params · tencent · MoE | 83.5 GB | 145.7 GB | 273.3 GB | | [Mistral-Small-4-119B-2603](https://whatcanirunai.vercel.app/models/mistral-small-4-119b-2603/)119.4B params · mistralai · MoE | 84.2 GB | 146.9 GB | 275.6 GB | | [NVIDIA-Nemotron-3-Super-120B-A12B-BF16](https://whatcanirunai.vercel.app/models/nvidia-nemotron-3-super-120b-a12b-bf16/)123.6B params · nvidia · MoE | 87.2 GB | 152.0 GB | 285.3 GB | | [Ling-3.0-flash-VL](https://whatcanirunai.vercel.app/models/ling-3.0-flash-vl/)124.8B params · inclusionAI · MoE | 88.0 GB | 153.5 GB | 288.2 GB | | [Devstral-2-123B-Instruct-2512](https://whatcanirunai.vercel.app/models/devstral-2-123b-instruct-2512/)125.0B params · mistralai | 88.2 GB | 153.8 GB | 288.6 GB | | [Ornith-1.5-122B](https://whatcanirunai.vercel.app/models/ornith-1.5-122b/)125.1B params · ornith-ai | 88.2 GB | 153.8 GB | 288.7 GB | | [Qwen3.5-122B-A10B](https://whatcanirunai.vercel.app/models/qwen3.5-122b-a10b/)125.1B params · Qwen · MoE | 88.2 GB | 153.8 GB | 288.7 GB | | [Ling-3.0-flash-Sante](https://whatcanirunai.vercel.app/models/ling-3.0-flash-sante/)127.5B params · inclusionAI · MoE | 89.9 GB | 156.8 GB | 294.2 GB | | [Ling-3.0-flash](https://whatcanirunai.vercel.app/models/ling-3.0-flash/)127.5B params · inclusionAI · MoE | 89.9 GB | 156.8 GB | 294.2 GB | | [Ling-3.0-flash-Fin](https://whatcanirunai.vercel.app/models/ling-3.0-flash-fin/)127.5B params · inclusionAI · MoE | 89.9 GB | 156.8 GB | 294.2 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 120B model? About 82.5 GB at Q4\_K\_M, 143.8 GB at Q8\_0 and 269.7 GB at FP16, at a \~4k context. That is for gpt-oss-120b (116.8B parameters): weights plus about 15% for the KV cache and 1 GB for the runtime. How much VRAM does a 120B model need for 32k context? About 83.5 GB at Q4\_K\_M with an FP16 KV cache, or 82.9 GB with a Q8 cache, based on gpt-oss-120b's published config. Models with different attention layouts need more or less. What is the cheapest GPU for a 120B 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 120B 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/) · [70B](https://whatcanirunai.vercel.app/how-much-vram/70b/) · [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/