# How much VRAM do you need for a 7B–8B model? > A 7B–8B LLM needs about 6.7 GB of VRAM at Q4, 11.0 GB at Q8 and 19.8 GB at FP16. Context-length table and the cheapest GPU and Mac that fit. Source: https://whatcanirunai.vercel.app/how-much-vram/8b/ # How much VRAM do you need for a 7B–8B model? Short answer: about **6.7 GB** at Q4\_K\_M, the usual quant for local use. Below: every quant, longer contexts, the cheapest hardware that fits, and every 7B–8B open model we track. Q4\_K\_M 6.7 GB 5.0 GB weights Q8\_0 11.0 GB 8.7 GB weights FP16 19.8 GB 16.4 GB weights Worked out for [Qwen3-8B](https://whatcanirunai.vercel.app/models/qwen3-8b/) (8.2B 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 B570** (10 GB, $219). Cheapest Mac or mini PC: **Mac mini M4 16GB** ($789). ## 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 7B–8B model at each quant__ | Setting | Memory needed | Cheapest GPU | Cheapest Mac / mini PC | | ---------------------- | ------------- | -------------------------------------------------------------------- | ------------------------------------------------------------------- | | Q4\_K\_M, \~4k context | 6.7 GB | [Intel Arc B570](https://whatcanirunai.vercel.app/hardware/arc-b570/) 10 GB usable · $219 | [Mac mini M4 16GB](https://whatcanirunai.vercel.app/hardware/mac-mini-m4-16/) 12 GB usable · $789 | | Q8\_0, \~4k context | 11.0 GB | [Radeon RX 7600 XT](https://whatcanirunai.vercel.app/hardware/rx-7600-xt/) 16 GB usable · $329 | [Mac mini M6 24GB](https://whatcanirunai.vercel.app/hardware/mac-mini-m6-24/) 18 GB usable · $1,299 | | FP16, \~4k context | 19.8 GB | [Intel Arc Pro B70 32GB](https://whatcanirunai.vercel.app/hardware/arc-pro-b70/) 32 GB usable · $949 | [Mac mini M6 32GB](https://whatcanirunai.vercel.app/hardware/mac-mini-m6-32/) 24 GB usable · $1,799 | | Q4\_K\_M, 32k context | 10.9 GB | [Radeon RX 7600 XT](https://whatcanirunai.vercel.app/hardware/rx-7600-xt/) 16 GB usable · $329 | [Mac mini M6 24GB](https://whatcanirunai.vercel.app/hardware/mac-mini-m6-24/) 18 GB usable · $1,299 | ## Memory by context length Each extra token of context adds to the KV cache. Numbers for Qwen3-8B, from its published config; a Q8 KV cache halves the cache part. __Memory needed for a 7B–8B model by context length__ | Context | Q4\_K\_M, FP16 cache | Q4\_K\_M, Q8 cache | Q8\_0, FP16 cache | | ----------- | -------------------- | ------------------ | ----------------- | | 4k tokens | 6.7 GB | 6.7 GB | 11.0 GB | | 8k tokens | 7.3 GB | 6.7 GB | 11.6 GB | | 16k tokens | 8.5 GB | 7.3 GB | 12.8 GB | | 32k tokens | 10.9 GB | 8.5 GB | 15.2 GB | | 64k tokens | 15.8 GB | 10.9 GB | 20.1 GB | | 128k tokens | 25.4 GB | 15.8 GB | 29.7 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 7B–8B model at Q4__ | Hardware | Q4 need vs. usable | Q4 / Q8 / FP16 | Est. tok/s | | ------------------------------------------------------------------------------------------------------ | ------------------ | ------------------------------ | ---------- | | [GeForce RTX 3050 8GB](https://whatcanirunai.vercel.app/hardware/rtx-3050-8gb/)GPU · $249 | **6.7** / 8.0 GB | Q4FitsQ8Won’t fitFP16Won’t fit | 27–38 | | [Radeon RX 7600](https://whatcanirunai.vercel.app/hardware/rx-7600/)GPU · $269 | **6.7** / 8.0 GB | Q4FitsQ8Won’t fitFP16Won’t fit | 35–49 | | [Intel Arc A750](https://whatcanirunai.vercel.app/hardware/arc-a750/)GPU · $289 | **6.7** / 8.0 GB | Q4FitsQ8Won’t fitFP16Won’t fit | 62–88 | | [GeForce RTX 4060](https://whatcanirunai.vercel.app/hardware/rtx-4060/)GPU · $299 | **6.7** / 8.0 GB | Q4FitsQ8Won’t fitFP16Won’t fit | 33–47 | | [GeForce RTX 5060](https://whatcanirunai.vercel.app/hardware/rtx-5060/)GPU · $299 | **6.7** / 8.0 GB | Q4FitsQ8Won’t fitFP16Won’t fit | 54–77 | | [Radeon RX 9060 XT 8GB](https://whatcanirunai.vercel.app/hardware/rx-9060-xt-8gb/)GPU · $299 | **6.7** / 8.0 GB | Q4FitsQ8Won’t fitFP16Won’t fit | 39–55 | | [GeForce RTX 5060 Ti 8GB](https://whatcanirunai.vercel.app/hardware/rtx-5060-ti-8gb/)GPU · $379 | **6.7** / 8.0 GB | Q4FitsQ8Won’t fitFP16Won’t fit | 54–77 | | [GeForce RTX 3060 Ti](https://whatcanirunai.vercel.app/hardware/rtx-3060-ti/)GPU · $399 | **6.7** / 8.0 GB | Q4FitsQ8Won’t fitFP16Won’t fit | 54–77 | | [GeForce RTX 2060 Super](https://whatcanirunai.vercel.app/hardware/rtx-2060-super/)GPU · $399 | **6.7** / 8.0 GB | Q4FitsQ8Won’t fitFP16Won’t fit | 54–77 | | [GeForce RTX 4060 Ti 8GB](https://whatcanirunai.vercel.app/hardware/rtx-4060-ti-8gb/)GPU · $399 | **6.7** / 8.0 GB | Q4FitsQ8Won’t fitFP16Won’t fit | 35–49 | | [GeForce RTX 3070](https://whatcanirunai.vercel.app/hardware/rtx-3070/)GPU · $499 | **6.7** / 8.0 GB | Q4FitsQ8Won’t fitFP16Won’t fit | 54–77 | | [GeForce RTX 2070](https://whatcanirunai.vercel.app/hardware/rtx-2070/)GPU · $499 | **6.7** / 8.0 GB | Q4FitsQ8Won’t fitFP16Won’t fit | 54–77 | | [GeForce RTX 2070 Super](https://whatcanirunai.vercel.app/hardware/rtx-2070-super/)GPU · $499 | **6.7** / 8.0 GB | Q4FitsQ8Won’t fitFP16Won’t fit | 54–77 | | [GeForce RTX 3070 Ti](https://whatcanirunai.vercel.app/hardware/rtx-3070-ti/)GPU · $599 | **6.7** / 8.0 GB | Q4FitsQ8Won’t fitFP16Won’t fit | 73–104 | | [GeForce RTX 2080](https://whatcanirunai.vercel.app/hardware/rtx-2080/)GPU · $699 | **6.7** / 8.0 GB | Q4FitsQ8Won’t fitFP16Won’t fit | 54–77 | | [GeForce RTX 2080 Super](https://whatcanirunai.vercel.app/hardware/rtx-2080-super/)GPU · $699 | **6.7** / 8.0 GB | Q4FitsQ8Won’t fitFP16Won’t fit | 60–85 | | [Intel Arc B570](https://whatcanirunai.vercel.app/hardware/arc-b570/)GPU · $219 | **6.7** / 10.0 GB | Q4FitsQ8Won’t fitFP16Won’t fit | 46–65 | | [GeForce RTX 3080 10GB](https://whatcanirunai.vercel.app/hardware/rtx-3080-10gb/)GPU · $699 | **6.7** / 10.0 GB | Q4FitsQ8Won’t fitFP16Won’t fit | 92–130 | | [GeForce RTX 2080 Ti](https://whatcanirunai.vercel.app/hardware/rtx-2080-ti/)GPU · $999 | **6.7** / 11.0 GB | Q4FitsQ8Won’t fitFP16Won’t fit | 74–105 | | [GeForce RTX 2060 12GB](https://whatcanirunai.vercel.app/hardware/rtx-2060-12gb/)GPU | **6.7** / 12.0 GB | Q4FitsQ8TightFP16Won’t fit | 41–58 | | [Intel Arc B580](https://whatcanirunai.vercel.app/hardware/arc-b580/)GPU · $249 | **6.7** / 12.0 GB | Q4FitsQ8TightFP16Won’t fit | 55–78 | | [GeForce RTX 3060 12GB](https://whatcanirunai.vercel.app/hardware/rtx-3060-12gb/)GPU · $329 | **6.7** / 12.0 GB | Q4FitsQ8TightFP16Won’t fit | 43–62 | | [Radeon RX 7700 XT](https://whatcanirunai.vercel.app/hardware/rx-7700-xt/)GPU · $449 | **6.7** / 12.0 GB | Q4FitsQ8TightFP16Won’t fit | 52–74 | | [Radeon RX 6700 XT](https://whatcanirunai.vercel.app/hardware/rx-6700-xt/)GPU · $479 | **6.7** / 12.0 GB | Q4FitsQ8TightFP16Won’t fit | 46–66 | | [GeForce RTX 5070](https://whatcanirunai.vercel.app/hardware/rtx-5070/)GPU · $549 | **6.7** / 12.0 GB | Q4FitsQ8TightFP16Won’t fit | 81–115 | | [Radeon RX 6750 XT](https://whatcanirunai.vercel.app/hardware/rx-6750-xt/)GPU · $549 | **6.7** / 12.0 GB | Q4FitsQ8TightFP16Won’t fit | 52–74 | | [GeForce RTX 4070](https://whatcanirunai.vercel.app/hardware/rtx-4070/)GPU · $599 | **6.7** / 12.0 GB | Q4FitsQ8TightFP16Won’t fit | 61–86 | | [GeForce RTX 4070 Super](https://whatcanirunai.vercel.app/hardware/rtx-4070-super/)GPU · $599 | **6.7** / 12.0 GB | Q4FitsQ8TightFP16Won’t fit | 61–86 | | [Mac mini M4 16GB](https://whatcanirunai.vercel.app/hardware/mac-mini-m4-16/)unified memory · $789 | **6.7** / 12.0 GB | Q4FitsQ8TightFP16Won’t fit | 11–19 | | [GeForce RTX 3080 12GB](https://whatcanirunai.vercel.app/hardware/rtx-3080-12gb/)GPU · $799 | **6.7** / 12.0 GB | Q4FitsQ8TightFP16Won’t fit | 110–156 | | [GeForce RTX 4070 Ti](https://whatcanirunai.vercel.app/hardware/rtx-4070-ti/)GPU · $799 | **6.7** / 12.0 GB | Q4FitsQ8TightFP16Won’t fit | 61–86 | | [Mac mini M6 16GB](https://whatcanirunai.vercel.app/hardware/mac-mini-m6-16/)unified memory · $899 | **6.7** / 12.0 GB | Q4FitsQ8TightFP16Won’t fit | 14–25 | | [GeForce RTX 3080 Ti](https://whatcanirunai.vercel.app/hardware/rtx-3080-ti/)GPU · $1,199 | **6.7** / 12.0 GB | Q4FitsQ8TightFP16Won’t fit | 110–156 | | [Radeon RX 7600 XT](https://whatcanirunai.vercel.app/hardware/rx-7600-xt/)GPU · $329 | **6.7** / 16.0 GB | Q4FitsQ8FitsFP16Won’t fit | 35–49 | | [Radeon RX 9060 XT 16GB](https://whatcanirunai.vercel.app/hardware/rx-9060-xt-16gb/)GPU · $349 | **6.7** / 16.0 GB | Q4FitsQ8FitsFP16Won’t fit | 39–55 | | [Intel Arc A770 16GB](https://whatcanirunai.vercel.app/hardware/arc-a770-16gb/)GPU · $349 | **6.7** / 16.0 GB | Q4FitsQ8FitsFP16Won’t fit | 68–96 | | [GeForce RTX 5060 Ti 16GB](https://whatcanirunai.vercel.app/hardware/rtx-5060-ti-16gb/)GPU · $429 | **6.7** / 16.0 GB | Q4FitsQ8FitsFP16Won’t fit | 54–77 | | [GeForce RTX 4060 Ti 16GB](https://whatcanirunai.vercel.app/hardware/rtx-4060-ti-16gb/)GPU · $499 | **6.7** / 16.0 GB | Q4FitsQ8FitsFP16Won’t fit | 35–49 | | [Radeon RX 7800 XT](https://whatcanirunai.vercel.app/hardware/rx-7800-xt/)GPU · $499 | **6.7** / 16.0 GB | Q4FitsQ8FitsFP16Won’t fit | 75–107 | | [Radeon RX 7900 GRE](https://whatcanirunai.vercel.app/hardware/rx-7900-gre/)GPU · $549 | **6.7** / 16.0 GB | Q4FitsQ8FitsFP16Won’t fit | 70–99 | | [Radeon RX 9070](https://whatcanirunai.vercel.app/hardware/rx-9070/)GPU · $549 | **6.7** / 16.0 GB | Q4FitsQ8FitsFP16Won’t fit | 77–110 | | [Radeon RX 6800](https://whatcanirunai.vercel.app/hardware/rx-6800/)GPU · $579 | **6.7** / 16.0 GB | Q4FitsQ8FitsFP16Won’t fit | 62–88 | | [Radeon RX 9070 XT](https://whatcanirunai.vercel.app/hardware/rx-9070-xt/)GPU · $599 | **6.7** / 16.0 GB | Q4FitsQ8FitsFP16Won’t fit | 77–110 | | [Radeon RX 6800 XT](https://whatcanirunai.vercel.app/hardware/rx-6800-xt/)GPU · $649 | **6.7** / 16.0 GB | Q4FitsQ8FitsFP16Won’t fit | 62–88 | | [NVIDIA RTX 2000 Ada 16GB](https://whatcanirunai.vercel.app/hardware/rtx-2000-ada/)GPU · $649 | **6.7** / 16.0 GB | Q4FitsQ8FitsFP16Won’t fit | 27–38 | | [GeForce RTX 5070 Ti](https://whatcanirunai.vercel.app/hardware/rtx-5070-ti/)GPU · $749 | **6.7** / 16.0 GB | Q4FitsQ8FitsFP16Won’t fit | 108–153 | | [GeForce RTX 4070 Ti Super](https://whatcanirunai.vercel.app/hardware/rtx-4070-ti-super/)GPU · $799 | **6.7** / 16.0 GB | Q4FitsQ8FitsFP16Won’t fit | 81–115 | | [GeForce RTX 4080 Super](https://whatcanirunai.vercel.app/hardware/rtx-4080-super/)GPU · $999 | **6.7** / 16.0 GB | Q4FitsQ8FitsFP16Won’t fit | 89–126 | | [GeForce RTX 5080](https://whatcanirunai.vercel.app/hardware/rtx-5080/)GPU · $999 | **6.7** / 16.0 GB | Q4FitsQ8FitsFP16Won’t fit | 116–164 | | [Radeon RX 6900 XT](https://whatcanirunai.vercel.app/hardware/rx-6900-xt/)GPU · $999 | **6.7** / 16.0 GB | Q4FitsQ8FitsFP16Won’t fit | 62–88 | | [NVIDIA RTX A4000](https://whatcanirunai.vercel.app/hardware/rtx-a4000/)GPU · $1,000 | **6.7** / 16.0 GB | Q4FitsQ8FitsFP16Won’t fit | 54–77 | | [Radeon RX 6950 XT](https://whatcanirunai.vercel.app/hardware/rx-6950-xt/)GPU · $1,099 | **6.7** / 16.0 GB | Q4FitsQ8FitsFP16Won’t fit | 70–99 | | [GeForce RTX 4080](https://whatcanirunai.vercel.app/hardware/rtx-4080/)GPU · $1,199 | **6.7** / 16.0 GB | Q4FitsQ8FitsFP16Won’t fit | 87–123 | | [Mac mini M6 24GB](https://whatcanirunai.vercel.app/hardware/mac-mini-m6-24/)unified memory · $1,299 | **6.7** / 18.0 GB | Q4FitsQ8FitsFP16Won’t fit | 15–27 | | [Radeon RX 7900 XT](https://whatcanirunai.vercel.app/hardware/rx-7900-xt/)GPU · $899 | **6.7** / 20.0 GB | Q4FitsQ8FitsFP16Tight | 97–137 | | [NVIDIA RTX 4000 Ada 20GB](https://whatcanirunai.vercel.app/hardware/rtx-4000-ada/)GPU · $1,250 | **6.7** / 20.0 GB | Q4FitsQ8FitsFP16Tight | 43–62 | | [NVIDIA Tesla P40 24GB](https://whatcanirunai.vercel.app/hardware/tesla-p40/)GPU | **6.7** / 24.0 GB | Q4FitsQ8FitsFP16Fits | 42–59 | | [Radeon RX 7900 XTX](https://whatcanirunai.vercel.app/hardware/rx-7900-xtx/)GPU · $999 | **6.7** / 24.0 GB | Q4FitsQ8FitsFP16Fits | 116–164 | | [GeForce RTX 3090](https://whatcanirunai.vercel.app/hardware/rtx-3090/)GPU · $1,499 | **6.7** / 24.0 GB | Q4FitsQ8FitsFP16Fits | 113–160 | | [GeForce RTX 4090](https://whatcanirunai.vercel.app/hardware/rtx-4090/)GPU · $1,599 | **6.7** / 24.0 GB | Q4FitsQ8FitsFP16Fits | 122–173 | | [Mac mini M6 32GB](https://whatcanirunai.vercel.app/hardware/mac-mini-m6-32/)unified memory · $1,799 | **6.7** / 24.0 GB | Q4FitsQ8FitsFP16Fits | 15–27 | | [GeForce RTX 3090 Ti](https://whatcanirunai.vercel.app/hardware/rtx-3090-ti/)GPU · $1,999 | **6.7** / 24.0 GB | Q4FitsQ8FitsFP16Fits | 122–173 | | [NVIDIA RTX A5000](https://whatcanirunai.vercel.app/hardware/rtx-a5000/)GPU · $2,250 | **6.7** / 24.0 GB | Q4FitsQ8FitsFP16Fits | 93–131 | | [MacBook Pro M6 32GB](https://whatcanirunai.vercel.app/hardware/macbook-pro-m6-32/)unified memory · $2,399 | **6.7** / 24.0 GB | Q4FitsQ8FitsFP16Fits | 15–27 | | [NVIDIA Titan RTX](https://whatcanirunai.vercel.app/hardware/titan-rtx/)GPU · $2,499 | **6.7** / 24.0 GB | Q4FitsQ8FitsFP16Fits | 81–115 | | [NVIDIA Tesla V100 32GB (PCIe)](https://whatcanirunai.vercel.app/hardware/tesla-v100-32gb/)GPU | **6.7** / 32.0 GB | Q4FitsQ8FitsFP16Fits | 109–154 | | [AMD Radeon Instinct MI50 32GB](https://whatcanirunai.vercel.app/hardware/instinct-mi50-32gb/)GPU | **6.7** / 32.0 GB | Q4FitsQ8FitsFP16Fits | 124–175 | | [AMD Radeon Instinct MI60 32GB](https://whatcanirunai.vercel.app/hardware/instinct-mi60/)GPU | **6.7** / 32.0 GB | Q4FitsQ8FitsFP16Fits | 124–175 | | [Intel Arc Pro B70 32GB](https://whatcanirunai.vercel.app/hardware/arc-pro-b70/)GPU · $949 | **6.7** / 32.0 GB | Q4FitsQ8FitsFP16Fits | 73–104 | | [GeForce RTX 5090](https://whatcanirunai.vercel.app/hardware/rtx-5090/)GPU · $1,999 | **6.7** / 32.0 GB | Q4FitsQ8FitsFP16Fits | 217–307 | | [Mac mini M5 Pro 64GB](https://whatcanirunai.vercel.app/hardware/mac-mini-m5-pro-64/)unified memory · $3,199 | **6.7** / 48.0 GB | Q4FitsQ8FitsFP16Fits | 28–49 | | [NVIDIA RTX A6000](https://whatcanirunai.vercel.app/hardware/rtx-a6000/)GPU · $4,649 | **6.7** / 48.0 GB | Q4FitsQ8FitsFP16Fits | 93–131 | | [NVIDIA DGX Spark 64GB](https://whatcanirunai.vercel.app/hardware/dgx-spark-64/)unified memory · $4,999 | **6.7** / 48.0 GB | Q4FitsQ8FitsFP16Fits | 25–44 | | [NVIDIA RTX 6000 Ada 48GB](https://whatcanirunai.vercel.app/hardware/rtx-6000-ada/)GPU · $6,799 | **6.7** / 48.0 GB | Q4FitsQ8FitsFP16Fits | 116–164 | | [Framework Desktop (Ryzen AI Max+ 395) 128GB](https://whatcanirunai.vercel.app/hardware/framework-desktop-128/)unified memory · $3,449 | **6.7** / 96.0 GB | Q4FitsQ8FitsFP16Fits | 23–41 | | [MacBook Pro M4 Max 128GB](https://whatcanirunai.vercel.app/hardware/macbook-pro-m4-max-128/)unified memory · $4,999 | **6.7** / 96.0 GB | Q4FitsQ8FitsFP16Fits | 49–88 | | [MacBook Pro M5 Max 128GB](https://whatcanirunai.vercel.app/hardware/macbook-pro-m5-max-128/)unified memory · $6,699 | **6.7** / 96.0 GB | Q4FitsQ8FitsFP16Fits | 56–99 | | [NVIDIA DGX Spark 128GB](https://whatcanirunai.vercel.app/hardware/dgx-spark-128/)unified memory · $6,950 | **6.7** / 96.0 GB | Q4FitsQ8FitsFP16Fits | 25–44 | | [NVIDIA RTX Pro 6000 Blackwell 96GB](https://whatcanirunai.vercel.app/hardware/rtx-pro-6000-blackwell/)GPU · $8,565 | **6.7** / 96.0 GB | Q4FitsQ8FitsFP16Fits | 217–307 | | [Mac Studio M5 Ultra 256GB](https://whatcanirunai.vercel.app/hardware/mac-studio-m5-ultra-256/)unified memory · $9,499 | **6.7** / 192.0 GB | Q4FitsQ8FitsFP16Fits | 109–193 | | [Mac Studio M5 Ultra 512GB](https://whatcanirunai.vercel.app/hardware/mac-studio-m5-ultra-512/)unified memory · $22,299 | **6.7** / 384.0 GB | Q4FitsQ8FitsFP16Fits | 109–193 | ## 7B–8B models we track __Memory needed by each 7B–8B model we track__ | Model | Q4\_K\_M | Q8\_0 | FP16 | | ------------------------------------------------------------------------------------------------- | -------- | ------- | ------- | | [granite-4.0-tiny-preview](https://whatcanirunai.vercel.app/models/granite-4.0-tiny-preview/)6.7B params · ibm-granite · MoE | 5.7 GB | 9.2 GB | 16.3 GB | | [OLMo-7B-0724-hf](https://whatcanirunai.vercel.app/models/olmo-7b-0724-hf/)6.9B params · allenai | 5.8 GB | 9.4 GB | 16.8 GB | | [OLMoE-1B-7B-0924-Instruct](https://whatcanirunai.vercel.app/models/olmoe-1b-7b-0924-instruct/)6.9B params · allenai · MoE | 5.8 GB | 9.5 GB | 16.9 GB | | [OLMoE-1B-7B-0924](https://whatcanirunai.vercel.app/models/olmoe-1b-7b-0924/)6.9B params · allenai · MoE | 5.8 GB | 9.5 GB | 16.9 GB | | [OLMoE-1B-7B-0125-Instruct](https://whatcanirunai.vercel.app/models/olmoe-1b-7b-0125-instruct/)6.9B params · allenai · MoE | 5.8 GB | 9.5 GB | 16.9 GB | | [OLMoE-1B-7B-0125](https://whatcanirunai.vercel.app/models/olmoe-1b-7b-0125/)6.9B params · allenai · MoE | 5.8 GB | 9.5 GB | 16.9 GB | | [granite-4.0-h-tiny](https://whatcanirunai.vercel.app/models/granite-4.0-h-tiny/)6.9B params · ibm-granite · MoE | 5.8 GB | 9.5 GB | 17.0 GB | | [Mistral-7B-Instruct-v0.3](https://whatcanirunai.vercel.app/models/mistral-7b-instruct-v0.3/)7.2B params · mistralai | 6.1 GB | 9.9 GB | 17.7 GB | | [Olmo-3-7B-Think](https://whatcanirunai.vercel.app/models/olmo-3-7b-think/)7.3B params · allenai | 6.1 GB | 9.9 GB | 17.8 GB | | [Olmo-3-7B-Instruct](https://whatcanirunai.vercel.app/models/olmo-3-7b-instruct/)7.3B params · allenai | 6.1 GB | 9.9 GB | 17.8 GB | | [Olmo-3-1025-7B](https://whatcanirunai.vercel.app/models/olmo-3-1025-7b/)7.3B params · allenai | 6.1 GB | 9.9 GB | 17.8 GB | | [Olmo-3-7B-Instruct-SFT](https://whatcanirunai.vercel.app/models/olmo-3-7b-instruct-sft/)7.3B params · allenai | 6.1 GB | 9.9 GB | 17.8 GB | | [Olmo-3-7B-Think-SFT](https://whatcanirunai.vercel.app/models/olmo-3-7b-think-sft/)7.3B params · allenai | 6.1 GB | 9.9 GB | 17.8 GB | | [Olmo-3-7B-Instruct-DPO](https://whatcanirunai.vercel.app/models/olmo-3-7b-instruct-dpo/)7.3B params · allenai | 6.1 GB | 9.9 GB | 17.8 GB | | [Olmo-3-7B-RL-Zero-Math](https://whatcanirunai.vercel.app/models/olmo-3-7b-rl-zero-math/)7.3B params · allenai | 6.1 GB | 9.9 GB | 17.8 GB | | [Olmo-3.1-7B-RL-Zero-Math](https://whatcanirunai.vercel.app/models/olmo-3.1-7b-rl-zero-math/)7.3B params · allenai | 6.1 GB | 9.9 GB | 17.8 GB | | [Olmo-3-7B-Think-DPO](https://whatcanirunai.vercel.app/models/olmo-3-7b-think-dpo/)7.3B params · allenai | 6.1 GB | 9.9 GB | 17.8 GB | | [OLMo-2-1124-7B-Instruct](https://whatcanirunai.vercel.app/models/olmo-2-1124-7b-instruct/)7.3B params · allenai | 6.1 GB | 9.9 GB | 17.8 GB | | [Zamba2-7B-Instruct](https://whatcanirunai.vercel.app/models/zamba2-7b-instruct/)7.4B params · Zyphra | 6.1 GB | 10.0 GB | 17.9 GB | | [LLaDA-MoE-7B-A1B-Base](https://whatcanirunai.vercel.app/models/llada-moe-7b-a1b-base/)7.4B params · inclusionAI · MoE | 6.1 GB | 10.0 GB | 17.9 GB | | [Olmo-Hybrid-7B](https://whatcanirunai.vercel.app/models/olmo-hybrid-7b/)7.4B params · allenai | 6.2 GB | 10.1 GB | 18.1 GB | | [Falcon3-7B-Instruct](https://whatcanirunai.vercel.app/models/falcon3-7b-instruct/)7.5B params · tiiuae | 6.2 GB | 10.1 GB | 18.1 GB | | [starvector-8b-im2svg](https://whatcanirunai.vercel.app/models/starvector-8b-im2svg/)7.5B params · starvector | 6.2 GB | 10.2 GB | 18.3 GB | | [Falcon-H1R-7B](https://whatcanirunai.vercel.app/models/falcon-h1r-7b/)7.6B params · tiiuae | 6.3 GB | 10.3 GB | 18.4 GB | | [Falcon-H1-7B-Instruct](https://whatcanirunai.vercel.app/models/falcon-h1-7b-instruct/)7.6B params · tiiuae | 6.3 GB | 10.3 GB | 18.4 GB | | [Qwen2.5-Coder-7B-Instruct](https://whatcanirunai.vercel.app/models/qwen2.5-coder-7b-instruct/)7.6B params · Qwen | 6.3 GB | 10.3 GB | 18.5 GB | | [DeepSeek-R1-Distill-Qwen-7B](https://whatcanirunai.vercel.app/models/deepseek-r1-distill-qwen-7b/)7.6B params · deepseek-ai | 6.3 GB | 10.3 GB | 18.5 GB | | [Qwen2.5-7B-Instruct](https://whatcanirunai.vercel.app/models/qwen2.5-7b-instruct/)7.6B params · Qwen | 6.3 GB | 10.3 GB | 18.5 GB | | [Qwen2.5-7B](https://whatcanirunai.vercel.app/models/qwen2.5-7b/)7.6B params · Qwen | 6.3 GB | 10.3 GB | 18.5 GB | | [Qwen2-7B-Instruct](https://whatcanirunai.vercel.app/models/qwen2-7b-instruct/)7.6B params · Qwen | 6.3 GB | 10.3 GB | 18.5 GB | | [Qwen2.5-Coder-7B](https://whatcanirunai.vercel.app/models/qwen2.5-coder-7b/)7.6B params · Qwen | 6.3 GB | 10.3 GB | 18.5 GB | | [Qwen2-7B](https://whatcanirunai.vercel.app/models/qwen2-7b/)7.6B params · Qwen | 6.3 GB | 10.3 GB | 18.5 GB | | [Qwen2.5-7B-Instruct-1M](https://whatcanirunai.vercel.app/models/qwen2.5-7b-instruct-1m/)7.6B params · Qwen | 6.3 GB | 10.3 GB | 18.5 GB | | [Qwen2.5-Math-7B](https://whatcanirunai.vercel.app/models/qwen2.5-math-7b/)7.6B params · Qwen | 6.3 GB | 10.3 GB | 18.5 GB | | [Qwen2.5-Math-7B-Instruct](https://whatcanirunai.vercel.app/models/qwen2.5-math-7b-instruct/)7.6B params · Qwen | 6.3 GB | 10.3 GB | 18.5 GB | | [Marco-o1](https://whatcanirunai.vercel.app/models/marco-o1/)7.6B params · ATH-MaaS | 6.3 GB | 10.3 GB | 18.5 GB | | [SeaLLMs-v3-7B-Chat](https://whatcanirunai.vercel.app/models/seallms-v3-7b-chat/)7.6B params · SeaLLMs | 6.3 GB | 10.3 GB | 18.5 GB | | [Arcee-Spark](https://whatcanirunai.vercel.app/models/arcee-spark/)7.6B params · arcee-ai | 6.3 GB | 10.3 GB | 18.5 GB | | [Phi-mini-MoE-instruct](https://whatcanirunai.vercel.app/models/phi-mini-moe-instruct/)7.6B params · microsoft · MoE | 6.3 GB | 10.3 GB | 18.6 GB | | [internlm2\_5-7b-chat](https://whatcanirunai.vercel.app/models/internlm2-5-7b-chat/)7.7B params · internlm | 6.4 GB | 10.5 GB | 18.8 GB | | [internlm2\_5-7b-chat-1m](https://whatcanirunai.vercel.app/models/internlm2-5-7b-chat-1m/)7.7B params · internlm | 6.4 GB | 10.5 GB | 18.8 GB | | [Molmo2-O-7B](https://whatcanirunai.vercel.app/models/molmo2-o-7b/)7.8B params · allenai | 6.4 GB | 10.5 GB | 18.8 GB | | [FastVLM-7B](https://whatcanirunai.vercel.app/models/fastvlm-7b/)7.8B params · apple | 6.4 GB | 10.5 GB | 18.9 GB | | [salamandra-7b-instruct](https://whatcanirunai.vercel.app/models/salamandra-7b-instruct/)7.8B params · BSC-LT | 6.4 GB | 10.5 GB | 18.9 GB | | [salamandra-7b](https://whatcanirunai.vercel.app/models/salamandra-7b/)7.8B params · BSC-LT | 6.4 GB | 10.5 GB | 18.9 GB | | [salamandra-7b-instruct-2606](https://whatcanirunai.vercel.app/models/salamandra-7b-instruct-2606/)7.8B params · BSC-LT | 6.4 GB | 10.5 GB | 18.9 GB | | [EXAONE-3.5-7.8B-Instruct](https://whatcanirunai.vercel.app/models/exaone-3.5-7.8b-instruct/)7.8B params · LGAI-EXAONE | 6.5 GB | 10.6 GB | 19.0 GB | | [EXAONE-Deep-7.8B](https://whatcanirunai.vercel.app/models/exaone-deep-7.8b/)7.8B params · LGAI-EXAONE | 6.5 GB | 10.6 GB | 19.0 GB | | [MiMo-7B-RL](https://whatcanirunai.vercel.app/models/mimo-7b-rl/)7.8B params · XiaomiMiMo | 6.5 GB | 10.6 GB | 19.0 GB | | [MiMo-7B-Base](https://whatcanirunai.vercel.app/models/mimo-7b-base/)7.8B params · XiaomiMiMo | 6.5 GB | 10.6 GB | 19.0 GB | | [MiMo-7B-RL-0530](https://whatcanirunai.vercel.app/models/mimo-7b-rl-0530/)7.8B params · XiaomiMiMo | 6.5 GB | 10.6 GB | 19.0 GB | | [MiMo-7B-SFT](https://whatcanirunai.vercel.app/models/mimo-7b-sft/)7.8B params · XiaomiMiMo | 6.5 GB | 10.6 GB | 19.0 GB | | [Ling-3.0-tiny](https://whatcanirunai.vercel.app/models/ling-3.0-tiny/)7.9B params · inclusionAI · MoE | 6.5 GB | 10.6 GB | 19.2 GB | | [Nemotron 3.5 Bolt E4B](https://whatcanirunai.vercel.app/models/nemotron-3.5-bolt-e4b/)8.0B params · nvidia | 6.6 GB | 10.8 GB | 19.4 GB | | [Gemma 4 E4B](https://whatcanirunai.vercel.app/models/gemma-4-e4b-it/)8.0B params · google | 6.6 GB | 10.8 GB | 19.4 GB | | [Hy-MT2-7B](https://whatcanirunai.vercel.app/models/hy-mt2-7b/)8.0B params · tencent | 6.6 GB | 10.8 GB | 19.5 GB | | [Llama-3.1-8B-Instruct](https://whatcanirunai.vercel.app/models/llama-3.1-8b-instruct/)8.0B params · meta-llama | 6.6 GB | 10.8 GB | 19.5 GB | | [Llama-3.1-8B](https://whatcanirunai.vercel.app/models/llama-3.1-8b/)8.0B params · meta-llama | 6.6 GB | 10.8 GB | 19.5 GB | | [DeepSeek-R1-Distill-Llama-8B](https://whatcanirunai.vercel.app/models/deepseek-r1-distill-llama-8b/)8.0B params · deepseek-ai | 6.6 GB | 10.8 GB | 19.5 GB | | [Meta-Llama-3.1-8B-Instruct](https://whatcanirunai.vercel.app/models/meta-llama-3.1-8b-instruct/)8.0B params · NousResearch | 6.6 GB | 10.8 GB | 19.5 GB | | [L3-8B-Stheno-v3.2](https://whatcanirunai.vercel.app/models/l3-8b-stheno-v3.2/)8.0B params · Sao10K | 6.6 GB | 10.8 GB | 19.5 GB | | [Llama-3.1-Nemotron-Nano-8B-v1](https://whatcanirunai.vercel.app/models/llama-3.1-nemotron-nano-8b-v1/)8.0B params · nvidia | 6.6 GB | 10.8 GB | 19.5 GB | | [Meta-Llama-3.1-8B](https://whatcanirunai.vercel.app/models/meta-llama-3.1-8b/)8.0B params · NousResearch | 6.6 GB | 10.8 GB | 19.5 GB | | [LongWriter-llama3.1-8b](https://whatcanirunai.vercel.app/models/longwriter-llama3.1-8b/)8.0B params · zai-org | 6.6 GB | 10.8 GB | 19.5 GB | | [kanana-1.5-8b-instruct-2505](https://whatcanirunai.vercel.app/models/kanana-1.5-8b-instruct-2505/)8.0B params · kakaocorp | 6.6 GB | 10.8 GB | 19.5 GB | | [Llama-3.1-Tulu-3-8B](https://whatcanirunai.vercel.app/models/llama-3.1-tulu-3-8b/)8.0B params · allenai | 6.6 GB | 10.8 GB | 19.5 GB | | [Llama-3.1-Tulu-3-8B-DPO](https://whatcanirunai.vercel.app/models/llama-3.1-tulu-3-8b-dpo/)8.0B params · allenai | 6.6 GB | 10.8 GB | 19.5 GB | | [Llama-3.1-Tulu-3.1-8B](https://whatcanirunai.vercel.app/models/llama-3.1-tulu-3.1-8b/)8.0B params · allenai | 6.6 GB | 10.8 GB | 19.5 GB | | [Apertus-8B-Instruct-2509](https://whatcanirunai.vercel.app/models/apertus-8b-instruct-2509/)8.1B params · swiss-ai | 6.6 GB | 10.8 GB | 19.5 GB | | [Apertus-8B-2509](https://whatcanirunai.vercel.app/models/apertus-8b-2509/)8.1B params · swiss-ai | 6.6 GB | 10.8 GB | 19.5 GB | | [granite-3.1-8b-instruct](https://whatcanirunai.vercel.app/models/granite-3.1-8b-instruct/)8.2B params · ibm-granite | 6.7 GB | 11.0 GB | 19.8 GB | | [granite-3.2-8b-instruct](https://whatcanirunai.vercel.app/models/granite-3.2-8b-instruct/)8.2B params · ibm-granite | 6.7 GB | 11.0 GB | 19.8 GB | | [granite-3.0-8b-instruct](https://whatcanirunai.vercel.app/models/granite-3.0-8b-instruct/)8.2B params · ibm-granite | 6.7 GB | 11.0 GB | 19.8 GB | | [granite-3.3-8b-instruct](https://whatcanirunai.vercel.app/models/granite-3.3-8b-instruct/)8.2B params · ibm-granite | 6.7 GB | 11.0 GB | 19.8 GB | | [MiniCPM4-8B](https://whatcanirunai.vercel.app/models/minicpm4-8b/)8.2B params · openbmb | 6.7 GB | 11.0 GB | 19.8 GB | | [MiniCPM4.1-8B](https://whatcanirunai.vercel.app/models/minicpm4.1-8b/)8.2B params · openbmb | 6.7 GB | 11.0 GB | 19.8 GB | | [AgentCPM-Report](https://whatcanirunai.vercel.app/models/agentcpm-report/)8.2B params · openbmb | 6.7 GB | 11.0 GB | 19.8 GB | | [MiniCPM4-MCP](https://whatcanirunai.vercel.app/models/minicpm4-mcp/)8.2B params · openbmb | 6.7 GB | 11.0 GB | 19.8 GB | | [Qwen3-8B](https://whatcanirunai.vercel.app/models/qwen3-8b/)8.2B params · Qwen | 6.7 GB | 11.0 GB | 19.8 GB | | [DeepSeek-R1-0528-Qwen3-8B](https://whatcanirunai.vercel.app/models/deepseek-r1-0528-qwen3-8b/)8.2B params · deepseek-ai | 6.7 GB | 11.0 GB | 19.8 GB | | [Qwen3-8B-Base](https://whatcanirunai.vercel.app/models/qwen3-8b-base/)8.2B params · Qwen | 6.7 GB | 11.0 GB | 19.8 GB | | [WeDLM-8B-Instruct](https://whatcanirunai.vercel.app/models/wedlm-8b-instruct/)8.2B params · tencent | 6.7 GB | 11.0 GB | 19.8 GB | | [PaCoRe-8B](https://whatcanirunai.vercel.app/models/pacore-8b/)8.2B params · stepfun-ai | 6.7 GB | 11.0 GB | 19.8 GB | | [Seed-Coder-8B-Instruct](https://whatcanirunai.vercel.app/models/seed-coder-8b-instruct/)8.3B params · ByteDance-Seed | 6.8 GB | 11.1 GB | 20.0 GB | | [Seed-Coder-8B-Reasoning](https://whatcanirunai.vercel.app/models/seed-coder-8b-reasoning/)8.3B params · ByteDance-Seed | 6.8 GB | 11.1 GB | 20.0 GB | | [Stable-DiffCoder-8B-Instruct](https://whatcanirunai.vercel.app/models/stable-diffcoder-8b-instruct/)8.3B params · ByteDance-Seed | 6.8 GB | 11.1 GB | 20.0 GB | | [Qwen2-VL-7B-Instruct](https://whatcanirunai.vercel.app/models/qwen2-vl-7b-instruct/)8.3B params · Qwen | 6.8 GB | 11.1 GB | 20.1 GB | | [UI-TARS-7B-DPO](https://whatcanirunai.vercel.app/models/ui-tars-7b-dpo/)8.3B params · ByteDance-Seed | 6.8 GB | 11.1 GB | 20.1 GB | | [UI-TARS-7B-SFT](https://whatcanirunai.vercel.app/models/ui-tars-7b-sft/)8.3B params · ByteDance-Seed | 6.8 GB | 11.1 GB | 20.1 GB | | [Qwen2.5-VL-7B-Instruct](https://whatcanirunai.vercel.app/models/qwen2.5-vl-7b-instruct/)8.3B params · Qwen | 6.8 GB | 11.1 GB | 20.1 GB | | [UI-TARS-1.5-7B](https://whatcanirunai.vercel.app/models/ui-tars-1.5-7b/)8.3B params · ByteDance-Seed | 6.8 GB | 11.1 GB | 20.1 GB | | [Fara-7B](https://whatcanirunai.vercel.app/models/fara-7b/)8.3B params · microsoft | 6.8 GB | 11.1 GB | 20.1 GB | | [UI-Venus-Ground-7B](https://whatcanirunai.vercel.app/models/ui-venus-ground-7b/)8.3B params · inclusionAI | 6.8 GB | 11.1 GB | 20.1 GB | | [MiMo-VL-7B-RL-2508](https://whatcanirunai.vercel.app/models/mimo-vl-7b-rl-2508/)8.3B params · XiaomiMiMo | 6.8 GB | 11.1 GB | 20.1 GB | | [MiMo-VL-7B-RL](https://whatcanirunai.vercel.app/models/mimo-vl-7b-rl/)8.3B params · XiaomiMiMo | 6.8 GB | 11.1 GB | 20.1 GB | | [MiMo-Embodied-7B](https://whatcanirunai.vercel.app/models/mimo-embodied-7b/)8.3B params · XiaomiMiMo | 6.8 GB | 11.1 GB | 20.1 GB | | [MiMo-VL-7B-SFT](https://whatcanirunai.vercel.app/models/mimo-vl-7b-sft/)8.3B params · XiaomiMiMo | 6.8 GB | 11.1 GB | 20.1 GB | | [rnj-1-instruct](https://whatcanirunai.vercel.app/models/rnj-1-instruct/)8.3B params · EssentialAI | 6.8 GB | 11.2 GB | 20.1 GB | | [LFM2-8B-A1B](https://whatcanirunai.vercel.app/models/lfm2-8b-a1b/)8.3B params · LiquidAI · MoE | 6.8 GB | 11.2 GB | 20.2 GB | | [Mistral-NeMo-Minitron-8B-Base](https://whatcanirunai.vercel.app/models/mistral-nemo-minitron-8b-base/)8.4B params · nvidia | 6.9 GB | 11.3 GB | 20.4 GB | | [LFM2.5-8B-A1B](https://whatcanirunai.vercel.app/models/lfm2.5-8b-a1b/)8.5B params · LiquidAI · MoE | 6.9 GB | 11.3 GB | 20.5 GB | | [Nemotron-Labs-Diffusion-8B-Base](https://whatcanirunai.vercel.app/models/nemotron-labs-diffusion-8b-base/)8.5B params · nvidia | 6.9 GB | 11.4 GB | 20.5 GB | | [Intern-S1-mini](https://whatcanirunai.vercel.app/models/intern-s1-mini/)8.5B params · internlm | 7.0 GB | 11.4 GB | 20.6 GB | | [aya-vision-8b](https://whatcanirunai.vercel.app/models/aya-vision-8b/)8.6B params · CohereLabs | 7.0 GB | 11.5 GB | 20.9 GB | | [MiniCPM-V-4\_5](https://whatcanirunai.vercel.app/models/minicpm-v-4-5/)8.7B params · openbmb | 7.1 GB | 11.6 GB | 21.0 GB | | [Llama-3.1-Nemotron-Nano-VL-8B-V1](https://whatcanirunai.vercel.app/models/llama-3.1-nemotron-nano-vl-8b-v1/)8.7B params · nvidia | 7.1 GB | 11.7 GB | 21.1 GB | | [Qwen3-VL-8B-Instruct](https://whatcanirunai.vercel.app/models/qwen3-vl-8b-instruct/)8.8B params · Qwen | 7.1 GB | 11.7 GB | 21.2 GB | | [Qwen3-VL-8B-Thinking](https://whatcanirunai.vercel.app/models/qwen3-vl-8b-thinking/)8.8B params · Qwen | 7.1 GB | 11.7 GB | 21.2 GB | | [UI-Venus-1.5-8B](https://whatcanirunai.vercel.app/models/ui-venus-1.5-8b/)8.8B params · inclusionAI | 7.1 GB | 11.7 GB | 21.2 GB | | [granite-switch-4.1-8b](https://whatcanirunai.vercel.app/models/granite-switch-4.1-8b/)8.8B params · ibm-granite | 7.1 GB | 11.7 GB | 21.2 GB | | [granite-4.1-8b](https://whatcanirunai.vercel.app/models/granite-4.1-8b/)8.8B params · ibm-granite | 7.1 GB | 11.7 GB | 21.2 GB | | [granite-4.2-8b](https://whatcanirunai.vercel.app/models/granite-4.2-8b/)8.8B params · ibm-granite | 7.1 GB | 11.7 GB | 21.2 GB | | [internlm3-8b-instruct](https://whatcanirunai.vercel.app/models/internlm3-8b-instruct/)8.8B params · internlm | 7.1 GB | 11.8 GB | 21.2 GB | | [Yi-Coder-9B-Chat](https://whatcanirunai.vercel.app/models/yi-coder-9b-chat/)8.8B params · 01-ai | 7.2 GB | 11.8 GB | 21.3 GB | | [Yi-Coder-9B](https://whatcanirunai.vercel.app/models/yi-coder-9b/)8.8B params · 01-ai | 7.2 GB | 11.8 GB | 21.3 GB | | [ZAYA1-base](https://whatcanirunai.vercel.app/models/zaya1-base/)8.8B params · Zyphra | 7.2 GB | 11.8 GB | 21.3 GB | | [NVIDIA-Nemotron-Nano-9B-v2](https://whatcanirunai.vercel.app/models/nvidia-nemotron-nano-9b-v2/)8.9B params · nvidia | 7.2 GB | 11.9 GB | 21.4 GB | | [EuroLLM-9B-Instruct-2512](https://whatcanirunai.vercel.app/models/eurollm-9b-instruct-2512/)9.2B params · utter-project | 7.4 GB | 12.2 GB | 22.1 GB | | [gemma-2-9b-it](https://whatcanirunai.vercel.app/models/gemma-2-9b-it/)9.2B params · google | 7.4 GB | 12.3 GB | 22.3 GB | | [gemma-2-9b](https://whatcanirunai.vercel.app/models/gemma-2-9b/)9.2B params · google | 7.4 GB | 12.3 GB | 22.3 GB | | [Gemma-SEA-LION-v3-9B-IT](https://whatcanirunai.vercel.app/models/gemma-sea-lion-v3-9b-it/)9.2B params · aisingapore | 7.4 GB | 12.3 GB | 22.3 GB | | [moondream3-preview](https://whatcanirunai.vercel.app/models/moondream3-preview/)9.3B params · moondream | 7.5 GB | 12.3 GB | 22.3 GB | | [glm-4-9b-chat-hf](https://whatcanirunai.vercel.app/models/glm-4-9b-chat-hf/)9.4B params · zai-org | 7.6 GB | 12.5 GB | 22.6 GB | | [codegeex4-all-9b](https://whatcanirunai.vercel.app/models/codegeex4-all-9b/)9.4B params · zai-org | 7.6 GB | 12.5 GB | 22.6 GB | | [GLM-4-9B-0414](https://whatcanirunai.vercel.app/models/glm-4-9b-0414/)9.4B params · zai-org | 7.6 GB | 12.5 GB | 22.6 GB | | [GLM-Z1-9B-0414](https://whatcanirunai.vercel.app/models/glm-z1-9b-0414/)9.4B params · zai-org | 7.6 GB | 12.5 GB | 22.6 GB | | [NorthCUA-Nano](https://whatcanirunai.vercel.app/models/northcua-nano/)9.4B params · beipo-ai | 7.6 GB | 12.5 GB | 22.6 GB | | [MiniCPM-SALA](https://whatcanirunai.vercel.app/models/minicpm-sala/)9.5B params · openbmb | 7.6 GB | 12.6 GB | 22.8 GB | | [Ornith-1.5-9B](https://whatcanirunai.vercel.app/models/ornith-1.5-9b/)9.7B params · ornith-ai | 7.7 GB | 12.8 GB | 23.2 GB | | [Qwen3.5-9B](https://whatcanirunai.vercel.app/models/qwen3.5-9b/)9.7B params · Qwen | 7.7 GB | 12.8 GB | 23.2 GB | | [SenseNova-Loop-9B-Preview](https://whatcanirunai.vercel.app/models/sensenova-loop-9b-preview/)9.7B params · sensenova | 7.7 GB | 12.8 GB | 23.2 GB | | [ZAYA1-VL-8B](https://whatcanirunai.vercel.app/models/zaya1-vl-8b/)9.7B params · Zyphra | 7.8 GB | 12.9 GB | 23.4 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 7B–8B model? About 6.7 GB at Q4\_K\_M, 11.0 GB at Q8\_0 and 19.8 GB at FP16, at a \~4k context. That is for Qwen3-8B (8.2B parameters): weights plus about 15% for the KV cache and 1 GB for the runtime. How much VRAM does a 7B–8B model need for 32k context? About 10.9 GB at Q4\_K\_M with an FP16 KV cache, or 8.5 GB with a Q8 cache, based on Qwen3-8B's published config. Models with different attention layouts need more or less. What is the cheapest GPU for a 7B–8B model? Of the GPUs we track, the Intel Arc B570 (10 GB, $219 list price) is the cheapest that runs it fully on the GPU at Q4. Can a Mac run a 7B–8B model? Yes. The cheapest Mac or unified-memory mini PC we track that runs it at Q4 is the Mac mini M4 16GB (16 GB, about 12.0 GB usable, $789). 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/) · [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/) · [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/