# How much VRAM do you need for a 30B–32B model? > A 30B–32B LLM needs about 23.8 GB of VRAM at Q4, 41.0 GB at Q8 and 76.4 GB at FP16. Context-length table and the cheapest GPU and Mac that fit. Source: https://whatcanirunai.vercel.app/how-much-vram/32b/ # How much VRAM do you need for a 30B–32B model? Short answer: about **23.8 GB** at Q4\_K\_M, the usual quant for local use. Below: every quant, longer contexts, the cheapest hardware that fits, and every 30B–32B open model we track. Q4\_K\_M 23.8 GB 19.9 GB weights Q8\_0 41.0 GB 34.8 GB weights FP16 76.4 GB 65.5 GB weights Worked out for [Qwen3-32B](https://whatcanirunai.vercel.app/models/qwen3-32b/) (32.8B 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: **Intel Arc Pro B70 32GB** (32 GB, $949). Cheapest Mac or mini PC: **Mac mini M5 Pro 64GB** ($3,199). ## 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 30B–32B model at each quant__ | Setting | Memory needed | Cheapest GPU | Cheapest Mac / mini PC | | ---------------------- | ------------- | --------------------------------------------------------------------------------------------- | ----------------------------------------------------------------------------------------------------- | | Q4\_K\_M, \~4k context | 23.8 GB | [Intel Arc Pro B70 32GB](https://whatcanirunai.vercel.app/hardware/arc-pro-b70/) 32 GB usable · $949 | [Mac mini M5 Pro 64GB](https://whatcanirunai.vercel.app/hardware/mac-mini-m5-pro-64/) 48 GB usable · $3,199 | | Q8\_0, \~4k context | 41.0 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 | | FP16, \~4k context | 76.4 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 | | Q4\_K\_M, 32k context | 31.4 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 | ## Memory by context length Each extra token of context adds to the KV cache. Numbers for Qwen3-32B, from its published config; a Q8 KV cache halves the cache part. __Memory needed for a 30B–32B model by context length__ | Context | Q4\_K\_M, FP16 cache | Q4\_K\_M, Q8 cache | Q8\_0, FP16 cache | | ----------- | -------------------- | ------------------ | ----------------- | | 4k tokens | 23.8 GB | 23.8 GB | 41.0 GB | | 8k tokens | 24.9 GB | 23.8 GB | 42.1 GB | | 16k tokens | 27.1 GB | 24.9 GB | 44.3 GB | | 32k tokens | 31.4 GB | 27.1 GB | 48.5 GB | | 64k tokens | 39.9 GB | 31.4 GB | 57.1 GB | | 128k tokens | 57.1 GB | 39.9 GB | 74.3 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 30B–32B model at Q4__ | Hardware | Q4 need vs. usable | Q4 / Q8 / FP16 | Est. tok/s | | ------------------------------------------------------------------------------------------------------ | ------------------- | ------------------------------- | ---------- | | [NVIDIA Tesla P40 24GB](https://whatcanirunai.vercel.app/hardware/tesla-p40/)GPU | **23.8** / 24.0 GB | Q4TightQ8Won’t fitFP16Won’t fit | 10–15 | | [Radeon RX 7900 XTX](https://whatcanirunai.vercel.app/hardware/rx-7900-xtx/)GPU · $999 | **23.8** / 24.0 GB | Q4TightQ8Won’t fitFP16Won’t fit | 29–41 | | [GeForce RTX 3090](https://whatcanirunai.vercel.app/hardware/rtx-3090/)GPU · $1,499 | **23.8** / 24.0 GB | Q4TightQ8Won’t fitFP16Won’t fit | 28–40 | | [GeForce RTX 4090](https://whatcanirunai.vercel.app/hardware/rtx-4090/)GPU · $1,599 | **23.8** / 24.0 GB | Q4TightQ8Won’t fitFP16Won’t fit | 30–43 | | [Mac mini M6 32GB](https://whatcanirunai.vercel.app/hardware/mac-mini-m6-32/)unified memory · $1,799 | **23.8** / 24.0 GB | Q4TightQ8Won’t fitFP16Won’t fit | 4–7 | | [GeForce RTX 3090 Ti](https://whatcanirunai.vercel.app/hardware/rtx-3090-ti/)GPU · $1,999 | **23.8** / 24.0 GB | Q4TightQ8Won’t fitFP16Won’t fit | 30–43 | | [NVIDIA RTX A5000](https://whatcanirunai.vercel.app/hardware/rtx-a5000/)GPU · $2,250 | **23.8** / 24.0 GB | Q4TightQ8Won’t fitFP16Won’t fit | 23–33 | | [MacBook Pro M6 32GB](https://whatcanirunai.vercel.app/hardware/macbook-pro-m6-32/)unified memory · $2,399 | **23.8** / 24.0 GB | Q4TightQ8Won’t fitFP16Won’t fit | 4–7 | | [NVIDIA Titan RTX](https://whatcanirunai.vercel.app/hardware/titan-rtx/)GPU · $2,499 | **23.8** / 24.0 GB | Q4TightQ8Won’t fitFP16Won’t fit | 20–29 | | [NVIDIA Tesla V100 32GB (PCIe)](https://whatcanirunai.vercel.app/hardware/tesla-v100-32gb/)GPU | **23.8** / 32.0 GB | Q4FitsQ8Won’t fitFP16Won’t fit | 27–39 | | [AMD Radeon Instinct MI50 32GB](https://whatcanirunai.vercel.app/hardware/instinct-mi50-32gb/)GPU | **23.8** / 32.0 GB | Q4FitsQ8Won’t fitFP16Won’t fit | 31–44 | | [AMD Radeon Instinct MI60 32GB](https://whatcanirunai.vercel.app/hardware/instinct-mi60/)GPU | **23.8** / 32.0 GB | Q4FitsQ8Won’t fitFP16Won’t fit | 31–44 | | [Intel Arc Pro B70 32GB](https://whatcanirunai.vercel.app/hardware/arc-pro-b70/)GPU · $949 | **23.8** / 32.0 GB | Q4FitsQ8Won’t fitFP16Won’t fit | 18–26 | | [GeForce RTX 5090](https://whatcanirunai.vercel.app/hardware/rtx-5090/)GPU · $1,999 | **23.8** / 32.0 GB | Q4FitsQ8Won’t fitFP16Won’t fit | 54–77 | | [Mac mini M5 Pro 64GB](https://whatcanirunai.vercel.app/hardware/mac-mini-m5-pro-64/)unified memory · $3,199 | **23.8** / 48.0 GB | Q4FitsQ8FitsFP16Won’t fit | 7–12 | | [NVIDIA RTX A6000](https://whatcanirunai.vercel.app/hardware/rtx-a6000/)GPU · $4,649 | **23.8** / 48.0 GB | Q4FitsQ8FitsFP16Won’t fit | 23–33 | | [NVIDIA DGX Spark 64GB](https://whatcanirunai.vercel.app/hardware/dgx-spark-64/)unified memory · $4,999 | **23.8** / 48.0 GB | Q4FitsQ8FitsFP16Won’t fit | 6–11 | | [NVIDIA RTX 6000 Ada 48GB](https://whatcanirunai.vercel.app/hardware/rtx-6000-ada/)GPU · $6,799 | **23.8** / 48.0 GB | Q4FitsQ8FitsFP16Won’t fit | 29–41 | | [Framework Desktop (Ryzen AI Max+ 395) 128GB](https://whatcanirunai.vercel.app/hardware/framework-desktop-128/)unified memory · $3,449 | **23.8** / 96.0 GB | Q4FitsQ8FitsFP16Fits | 6–10 | | [MacBook Pro M4 Max 128GB](https://whatcanirunai.vercel.app/hardware/macbook-pro-m4-max-128/)unified memory · $4,999 | **23.8** / 96.0 GB | Q4FitsQ8FitsFP16Fits | 12–22 | | [MacBook Pro M5 Max 128GB](https://whatcanirunai.vercel.app/hardware/macbook-pro-m5-max-128/)unified memory · $6,699 | **23.8** / 96.0 GB | Q4FitsQ8FitsFP16Fits | 14–25 | | [NVIDIA DGX Spark 128GB](https://whatcanirunai.vercel.app/hardware/dgx-spark-128/)unified memory · $6,950 | **23.8** / 96.0 GB | Q4FitsQ8FitsFP16Fits | 6–11 | | [NVIDIA RTX Pro 6000 Blackwell 96GB](https://whatcanirunai.vercel.app/hardware/rtx-pro-6000-blackwell/)GPU · $8,565 | **23.8** / 96.0 GB | Q4FitsQ8FitsFP16Fits | 54–77 | | [Mac Studio M5 Ultra 256GB](https://whatcanirunai.vercel.app/hardware/mac-studio-m5-ultra-256/)unified memory · $9,499 | **23.8** / 192.0 GB | Q4FitsQ8FitsFP16Fits | 27–48 | | [Mac Studio M5 Ultra 512GB](https://whatcanirunai.vercel.app/hardware/mac-studio-m5-ultra-512/)unified memory · $22,299 | **23.8** / 384.0 GB | Q4FitsQ8FitsFP16Fits | 27–48 | ## 30B–32B models we track __Memory needed by each 30B–32B model we track__ | Model | Q4\_K\_M | Q8\_0 | FP16 | | ------------------------------------------------------------------------------------------------------------------------------ | -------- | ------- | ------- | | [ERNIE-4.5-VL-28B-A3B-Thinking](https://whatcanirunai.vercel.app/models/ernie-4.5-vl-28b-a3b-thinking/)29.7B params · baidu · MoE | 21.7 GB | 37.2 GB | 69.2 GB | | [Muse-Glimmer-30B](https://whatcanirunai.vercel.app/models/muse-glimmer-30b/)29.8B params · meta-models | 21.8 GB | 37.4 GB | 69.5 GB | | [granite-switch-4.1-30b](https://whatcanirunai.vercel.app/models/granite-switch-4.1-30b/)30.0B params · ibm-granite | 21.9 GB | 37.6 GB | 70.0 GB | | [Hy-Memory-30B-A3B](https://whatcanirunai.vercel.app/models/hy-memory-30b-a3b/)30.1B params · tencent · MoE | 22.0 GB | 37.7 GB | 70.1 GB | | [Hy-MT2-30B-A3B](https://whatcanirunai.vercel.app/models/hy-mt2-30b-a3b/)30.1B params · tencent · MoE | 22.0 GB | 37.7 GB | 70.1 GB | | [North-Mini-Code-1.0](https://whatcanirunai.vercel.app/models/north-mini-code-1.0/)30.5B params · CohereLabs · MoE | 22.3 GB | 38.2 GB | 71.1 GB | | [Qwen3-30B-A3B](https://whatcanirunai.vercel.app/models/qwen3-30b-a3b/)30.5B params · Qwen · MoE | 22.3 GB | 38.3 GB | 71.2 GB | | [Qwen3-Coder-30B-A3B-Instruct](https://whatcanirunai.vercel.app/models/qwen3-coder-30b-a3b-instruct/)30.5B params · Qwen · MoE | 22.3 GB | 38.3 GB | 71.2 GB | | [Qwen3-30B-A3B-Instruct-2507](https://whatcanirunai.vercel.app/models/qwen3-30b-a3b-instruct-2507/)30.5B params · Qwen · MoE | 22.3 GB | 38.3 GB | 71.2 GB | | [Qwen3-30B-A3B-Thinking-2507](https://whatcanirunai.vercel.app/models/qwen3-30b-a3b-thinking-2507/)30.5B params · Qwen · MoE | 22.3 GB | 38.3 GB | 71.2 GB | | [Tongyi-DeepResearch-30B-A3B](https://whatcanirunai.vercel.app/models/tongyi-deepresearch-30b-a3b/)30.5B params · Alibaba-NLP · MoE | 22.3 GB | 38.3 GB | 71.2 GB | | [Rubicon-Preview](https://whatcanirunai.vercel.app/models/rubicon-preview/)30.5B params · inclusionAI · MoE | 22.3 GB | 38.3 GB | 71.2 GB | | [kanana-2-30b-a3b-instruct-2601](https://whatcanirunai.vercel.app/models/kanana-2-30b-a3b-instruct-2601/)30.7B params · kakaocorp · MoE | 22.4 GB | 38.5 GB | 71.5 GB | | [kanana-2-30b-a3b-instruct](https://whatcanirunai.vercel.app/models/kanana-2-30b-a3b-instruct/)30.7B params · kakaocorp · MoE | 22.4 GB | 38.5 GB | 71.5 GB | | [kanana-2-30b-a3b-thinking-2601](https://whatcanirunai.vercel.app/models/kanana-2-30b-a3b-thinking-2601/)30.7B params · kakaocorp · MoE | 22.4 GB | 38.5 GB | 71.5 GB | | [Qwen3-VL-30B-A3B-Instruct](https://whatcanirunai.vercel.app/models/qwen3-vl-30b-a3b-instruct/)31.1B params · Qwen · MoE | 22.7 GB | 39.0 GB | 72.5 GB | | [Qwen3-VL-30B-A3B-Thinking](https://whatcanirunai.vercel.app/models/qwen3-vl-30b-a3b-thinking/)31.1B params · Qwen · MoE | 22.7 GB | 39.0 GB | 72.5 GB | | [UI-Venus-1.5-30B-A3B](https://whatcanirunai.vercel.app/models/ui-venus-1.5-30b-a3b/)31.1B params · inclusionAI · MoE | 22.7 GB | 39.0 GB | 72.5 GB | | [Xing4.0-29B-A4B](https://whatcanirunai.vercel.app/models/xing4.0-29b-a4b/)31.2B params · XingChen-AGI · MoE | 22.8 GB | 39.1 GB | 72.8 GB | | [Xing4.0-29B-Intent](https://whatcanirunai.vercel.app/models/xing4.0-29b-intent/)31.2B params · XingChen-AGI · MoE | 22.8 GB | 39.1 GB | 72.8 GB | | [GLM-4.7-Flash](https://whatcanirunai.vercel.app/models/glm-4.7-flash/)31.2B params · zai-org · MoE | 22.8 GB | 39.1 GB | 72.8 GB | | [Gemma 4 31B](https://whatcanirunai.vercel.app/models/gemma-4-31b-it/)31.3B params · google | 22.8 GB | 39.2 GB | 72.9 GB | | [gemma-4-31B](https://whatcanirunai.vercel.app/models/gemma-4-31b/)31.3B params · google | 22.8 GB | 39.2 GB | 72.9 GB | | [NVIDIA-Nemotron-3.5-Lightning-30B-A3B-BF16](https://whatcanirunai.vercel.app/models/nvidia-nemotron-3.5-lightning-30b-a3b-bf16/)31.6B params · nvidia · MoE | 23.0 GB | 39.6 GB | 73.6 GB | | [NVIDIA-Nemotron-3-Nano-30B-A3B-BF16](https://whatcanirunai.vercel.app/models/nvidia-nemotron-3-nano-30b-a3b-bf16/)31.6B params · nvidia · MoE | 23.0 GB | 39.6 GB | 73.6 GB | | [Nemotron-Cascade-2-30B-A3B](https://whatcanirunai.vercel.app/models/nemotron-cascade-2-30b-a3b/)31.6B params · nvidia · MoE | 23.0 GB | 39.6 GB | 73.6 GB | | [EXAONE-3.5-32B-Instruct](https://whatcanirunai.vercel.app/models/exaone-3.5-32b-instruct/)32.0B params · LGAI-EXAONE | 23.3 GB | 40.1 GB | 74.6 GB | | [EXAONE-Deep-32B](https://whatcanirunai.vercel.app/models/exaone-deep-32b/)32.0B params · LGAI-EXAONE | 23.3 GB | 40.1 GB | 74.6 GB | | [EXAONE-4.0-32B](https://whatcanirunai.vercel.app/models/exaone-4.0-32b/)32.0B params · LGAI-EXAONE | 23.3 GB | 40.1 GB | 74.6 GB | | [EXAONE-4.0.1-32B](https://whatcanirunai.vercel.app/models/exaone-4.0.1-32b/)32.0B params · LGAI-EXAONE | 23.3 GB | 40.1 GB | 74.6 GB | | [sarvam-30b](https://whatcanirunai.vercel.app/models/sarvam-30b/)32.2B params · sarvamai · MoE | 23.4 GB | 40.3 GB | 75.0 GB | | [granite-4.0-h-small](https://whatcanirunai.vercel.app/models/granite-4.0-h-small/)32.2B params · ibm-granite · MoE | 23.5 GB | 40.4 GB | 75.1 GB | | [Olmo-3-32B-Think](https://whatcanirunai.vercel.app/models/olmo-3-32b-think/)32.2B params · allenai | 23.5 GB | 40.4 GB | 75.1 GB | | [Olmo-3-1125-32B](https://whatcanirunai.vercel.app/models/olmo-3-1125-32b/)32.2B params · allenai | 23.5 GB | 40.4 GB | 75.1 GB | | [Olmo-3.1-32B-Instruct](https://whatcanirunai.vercel.app/models/olmo-3.1-32b-instruct/)32.2B params · allenai | 23.5 GB | 40.4 GB | 75.1 GB | | [Olmo-3.1-32B-Think](https://whatcanirunai.vercel.app/models/olmo-3.1-32b-think/)32.2B params · allenai | 23.5 GB | 40.4 GB | 75.1 GB | | [Olmo-3.1-32B-Instruct-DPO](https://whatcanirunai.vercel.app/models/olmo-3.1-32b-instruct-dpo/)32.2B params · allenai | 23.5 GB | 40.4 GB | 75.1 GB | | [OLMo-2-0325-32B-Instruct](https://whatcanirunai.vercel.app/models/olmo-2-0325-32b-instruct/)32.2B params · allenai | 23.5 GB | 40.4 GB | 75.1 GB | | [OLMo-2-0325-32B](https://whatcanirunai.vercel.app/models/olmo-2-0325-32b/)32.2B params · allenai | 23.5 GB | 40.4 GB | 75.1 GB | | [GLM-Z1-32B-0414](https://whatcanirunai.vercel.app/models/glm-z1-32b-0414/)32.6B params · zai-org | 23.7 GB | 40.8 GB | 75.9 GB | | [GLM-4-32B-0414](https://whatcanirunai.vercel.app/models/glm-4-32b-0414/)32.6B params · zai-org | 23.7 GB | 40.8 GB | 75.9 GB | | [Qwen3-32B](https://whatcanirunai.vercel.app/models/qwen3-32b/)32.8B params · Qwen | 23.8 GB | 41.0 GB | 76.4 GB | | [Qwen2.5-Coder-32B-Instruct](https://whatcanirunai.vercel.app/models/qwen2.5-coder-32b-instruct/)32.8B params · Qwen | 23.8 GB | 41.0 GB | 76.4 GB | | [DeepSeek-R1-Distill-Qwen-32B](https://whatcanirunai.vercel.app/models/deepseek-r1-distill-qwen-32b/)32.8B params · deepseek-ai | 23.8 GB | 41.0 GB | 76.4 GB | | [Qwen2.5-32B-Instruct](https://whatcanirunai.vercel.app/models/qwen2.5-32b-instruct/)32.8B params · Qwen | 23.8 GB | 41.0 GB | 76.4 GB | | [QwQ-32B](https://whatcanirunai.vercel.app/models/qwq-32b/)32.8B params · Qwen | 23.8 GB | 41.0 GB | 76.4 GB | | [QwQ-32B-Preview](https://whatcanirunai.vercel.app/models/qwq-32b-preview/)32.8B params · Qwen | 23.8 GB | 41.0 GB | 76.4 GB | | [Qwen2.5-32B](https://whatcanirunai.vercel.app/models/qwen2.5-32b/)32.8B params · Qwen | 23.8 GB | 41.0 GB | 76.4 GB | | [Qwen2.5-Coder-32B](https://whatcanirunai.vercel.app/models/qwen2.5-coder-32b/)32.8B params · Qwen | 23.8 GB | 41.0 GB | 76.4 GB | | [Nemotron-3-Nano-Omni-30B-A3B-Reasoning-BF16](https://whatcanirunai.vercel.app/models/nemotron-3-nano-omni-30b-a3b-reasoning-bf16/)33.0B params · nvidia · MoE | 24.0 GB | 41.3 GB | 76.9 GB | | [Nanbeige4.3-33B](https://whatcanirunai.vercel.app/models/nanbeige4.3-33b/)33.1B params · Nanbeige | 24.1 GB | 41.5 GB | 77.2 GB | | [GLM-Z1-Rumination-32B-0414](https://whatcanirunai.vercel.app/models/glm-z1-rumination-32b-0414/)33.1B params · zai-org | 24.1 GB | 41.5 GB | 77.2 GB | | [FlexOlmo-7x7B-1T](https://whatcanirunai.vercel.app/models/flexolmo-7x7b-1t/)33.3B params · allenai | 24.2 GB | 41.7 GB | 77.5 GB | | [HyperCLOVAX-SEED-Think-32B](https://whatcanirunai.vercel.app/models/hyperclovax-seed-think-32b/)33.3B params · naver-hyperclovax | 24.2 GB | 41.7 GB | 77.6 GB | | [Qwen3-VL-32B-Instruct](https://whatcanirunai.vercel.app/models/qwen3-vl-32b-instruct/)33.4B params · Qwen | 24.3 GB | 41.8 GB | 77.7 GB | | [Laguna-XS.2](https://whatcanirunai.vercel.app/models/laguna-xs.2/)33.4B params · poolside · MoE | 24.3 GB | 41.9 GB | 77.9 GB | | [Qwen2.5-VL-32B-Instruct](https://whatcanirunai.vercel.app/models/qwen2.5-vl-32b-instruct/)33.5B params · Qwen | 24.3 GB | 41.9 GB | 77.9 GB | | [Falcon-H1-34B-Instruct](https://whatcanirunai.vercel.app/models/falcon-h1-34b-instruct/)33.6B params · tiiuae | 24.5 GB | 42.1 GB | 78.4 GB | | [EXAONE-4.5-33B](https://whatcanirunai.vercel.app/models/exaone-4.5-33b/)34.4B params · LGAI-EXAONE | 24.9 GB | 43.0 GB | 80.0 GB | | [Qwen-AgentWorld-35B-A3B](https://whatcanirunai.vercel.app/models/qwen-agentworld-35b-a3b/)34.7B params · Qwen · MoE | 25.2 GB | 43.4 GB | 80.7 GB | | [KAT-Coder-V2.5-Dev](https://whatcanirunai.vercel.app/models/kat-coder-v2.5-dev/)34.7B params · Kwaipilot · MoE | 25.2 GB | 43.4 GB | 80.7 GB | | [Clover-35B-A3B-Preview](https://whatcanirunai.vercel.app/models/clover-35b-a3b-preview/)34.7B params · Diffuspace · MoE | 25.2 GB | 43.4 GB | 80.7 GB | | [Agents-A1](https://whatcanirunai.vercel.app/models/agents-a1/)35.1B params · InternScience · MoE | 25.5 GB | 43.9 GB | 81.7 GB | | [Nex-N2.5-mini](https://whatcanirunai.vercel.app/models/nex-n2.5-mini/)35.1B params · nex-agi · MoE | 25.5 GB | 43.9 GB | 81.7 GB | | [Nex-N2-mini](https://whatcanirunai.vercel.app/models/nex-n2-mini/)35.1B params · nex-agi · MoE | 25.5 GB | 43.9 GB | 81.7 GB | | [Axion-1.0-35B](https://whatcanirunai.vercel.app/models/axion-1.0-35b/)35.1B params · Axion1-0 · MoE | 25.5 GB | 43.9 GB | 81.7 GB | | [NorthCUA-Mini](https://whatcanirunai.vercel.app/models/northcua-mini/)35.1B params · beipo-ai · MoE | 25.5 GB | 43.9 GB | 81.7 GB | | [MemT-1.0-Nano](https://whatcanirunai.vercel.app/models/memt-1.0-nano/)35.1B params · MemTensor · MoE | 25.5 GB | 43.9 GB | 81.8 GB | | [MiniCPM-V-4.7-35B-A3B](https://whatcanirunai.vercel.app/models/minicpm-v-4.7-35b-a3b/)35.2B params · openbmb · MoE | 25.5 GB | 44.0 GB | 82.0 GB | | [Ornith-1.5-35B-A3B](https://whatcanirunai.vercel.app/models/ornith-1.5-35b-a3b/)36.0B params · ornith-ai · MoE | 26.1 GB | 44.9 GB | 83.7 GB | | [Sage-A1.5](https://whatcanirunai.vercel.app/models/sage-a1.5/)36.0B params · InternScience | 26.1 GB | 44.9 GB | 83.7 GB | | [Qwen3.6-35B-A3B](https://whatcanirunai.vercel.app/models/qwen3.6-35b-a3b/)36.0B params · Qwen · MoE | 26.1 GB | 44.9 GB | 83.7 GB | | [Qwen3.5-35B-A3B](https://whatcanirunai.vercel.app/models/qwen3.5-35b-a3b/)36.0B params · Qwen · MoE | 26.1 GB | 44.9 GB | 83.7 GB | | [Apex-35B-V1.0](https://whatcanirunai.vercel.app/models/apex-35b-v1.0/)36.0B params · Apexintelligence-AI · MoE | 26.1 GB | 44.9 GB | 83.7 GB | | [Intern-S2-Preview](https://whatcanirunai.vercel.app/models/intern-s2-preview/)36.1B params · internlm · MoE | 26.2 GB | 45.1 GB | 84.0 GB | | [Seed-OSS-36B-Instruct](https://whatcanirunai.vercel.app/models/seed-oss-36b-instruct/)36.2B params · ByteDance-Seed | 26.2 GB | 45.2 GB | 84.1 GB | | [Seed-OSS-36B-Base-woSyn](https://whatcanirunai.vercel.app/models/seed-oss-36b-base-wosyn/)36.2B params · ByteDance-Seed | 26.2 GB | 45.2 GB | 84.1 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 30B–32B model? About 23.8 GB at Q4\_K\_M, 41.0 GB at Q8\_0 and 76.4 GB at FP16, at a \~4k context. That is for Qwen3-32B (32.8B parameters): weights plus about 15% for the KV cache and 1 GB for the runtime. How much VRAM does a 30B–32B model need for 32k context? About 31.4 GB at Q4\_K\_M with an FP16 KV cache, or 27.1 GB with a Q8 cache, based on Qwen3-32B's published config. Models with different attention layouts need more or less. What is the cheapest GPU for a 30B–32B model? Of the GPUs we track, the Intel Arc Pro B70 32GB (32 GB, $949 list price) is the cheapest that runs it fully on the GPU at Q4. Can a Mac run a 30B–32B model? Yes. The cheapest Mac or unified-memory mini PC we track that runs it at Q4 is the Mac mini M5 Pro 64GB (64 GB, about 48.0 GB usable, $3,199). 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/) · [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/