# How much VRAM do you need for a 2B–3B model?

> A 2B–3B LLM needs about 3.2 GB of VRAM at Q4, 4.9 GB at Q8 and 8.4 GB at FP16. Context-length table and the cheapest GPU and Mac that fit.

Source: https://whatcanirunai.vercel.app/how-much-vram/3b/

# How much VRAM do you need for a 2B–3B model?

Short answer: about **3.2 GB** at Q4\_K\_M, the usual quant for local use. Below: every quant, longer contexts, the cheapest hardware that fits, and every 2B–3B open model we track.

Q4\_K\_M

3.2 GB 1.9 GB weights

Q8\_0

4.9 GB 3.4 GB weights

FP16

8.4 GB 6.4 GB weights

Worked out for [Llama-3.2-3B-Instruct](https://whatcanirunai.vercel.app/models/llama-3.2-3b-instruct/) (3.2B 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 2B–3B model at each quant__
| Setting                | Memory needed | Cheapest GPU                                              | Cheapest Mac / mini PC                                            |
| ---------------------- | ------------- | --------------------------------------------------------- | ----------------------------------------------------------------- |
| Q4\_K\_M, \~4k context | 3.2 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    | 4.9 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 |
| FP16, \~4k context     | 8.4 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 |
| Q4\_K\_M, 32k context  | 6.5 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 |

## Memory by context length

Each extra token of context adds to the KV cache. Numbers for Llama-3.2-3B-Instruct, from its published config; a Q8 KV cache halves the cache part.

__Memory needed for a 2B–3B model by context length__
| Context     | Q4\_K\_M, FP16 cache | Q4\_K\_M, Q8 cache | Q8\_0, FP16 cache |
| ----------- | -------------------- | ------------------ | ----------------- |
| 4k tokens   | 3.2 GB               | 3.2 GB             | 4.9 GB            |
| 8k tokens   | 3.7 GB               | 3.2 GB             | 5.4 GB            |
| 16k tokens  | 4.6 GB               | 3.7 GB             | 6.3 GB            |
| 32k tokens  | 6.5 GB               | 4.6 GB             | 8.2 GB            |
| 64k tokens  | 10.3 GB              | 6.5 GB             | 12.0 GB           |
| 128k tokens | 17.8 GB              | 10.3 GB            | 19.5 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 2B–3B model at Q4__
| Hardware                                                                                               | Q4 need vs. usable | Q4 / Q8 / FP16            | Est. tok/s |
| ------------------------------------------------------------------------------------------------------ | ------------------ | ------------------------- | ---------- |
| [GeForce RTX 2060 6GB](https://whatcanirunai.vercel.app/hardware/rtx-2060/)GPU · $349                                                  | **3.2** / 6.0 GB   | Q4FitsQ8FitsFP16Won’t fit | 104–147    |
| [GeForce RTX 3050 8GB](https://whatcanirunai.vercel.app/hardware/rtx-3050-8gb/)GPU · $249                                              | **3.2** / 8.0 GB   | Q4FitsQ8FitsFP16Won’t fit | 69–98      |
| [Radeon RX 7600](https://whatcanirunai.vercel.app/hardware/rx-7600/)GPU · $269                                                         | **3.2** / 8.0 GB   | Q4FitsQ8FitsFP16Won’t fit | 89–126     |
| [Intel Arc A750](https://whatcanirunai.vercel.app/hardware/arc-a750/)GPU · $289                                                        | **3.2** / 8.0 GB   | Q4FitsQ8FitsFP16Won’t fit | 158–223    |
| [GeForce RTX 4060](https://whatcanirunai.vercel.app/hardware/rtx-4060/)GPU · $299                                                      | **3.2** / 8.0 GB   | Q4FitsQ8FitsFP16Won’t fit | 84–119     |
| [GeForce RTX 5060](https://whatcanirunai.vercel.app/hardware/rtx-5060/)GPU · $299                                                      | **3.2** / 8.0 GB   | Q4FitsQ8FitsFP16Won’t fit | 138–196    |
| [Radeon RX 9060 XT 8GB](https://whatcanirunai.vercel.app/hardware/rx-9060-xt-8gb/)GPU · $299                                           | **3.2** / 8.0 GB   | Q4FitsQ8FitsFP16Won’t fit | 99–140     |
| [GeForce RTX 5060 Ti 8GB](https://whatcanirunai.vercel.app/hardware/rtx-5060-ti-8gb/)GPU · $379                                        | **3.2** / 8.0 GB   | Q4FitsQ8FitsFP16Won’t fit | 138–196    |
| [GeForce RTX 3060 Ti](https://whatcanirunai.vercel.app/hardware/rtx-3060-ti/)GPU · $399                                                | **3.2** / 8.0 GB   | Q4FitsQ8FitsFP16Won’t fit | 138–196    |
| [GeForce RTX 2060 Super](https://whatcanirunai.vercel.app/hardware/rtx-2060-super/)GPU · $399                                          | **3.2** / 8.0 GB   | Q4FitsQ8FitsFP16Won’t fit | 138–196    |
| [GeForce RTX 4060 Ti 8GB](https://whatcanirunai.vercel.app/hardware/rtx-4060-ti-8gb/)GPU · $399                                        | **3.2** / 8.0 GB   | Q4FitsQ8FitsFP16Won’t fit | 89–126     |
| [GeForce RTX 3070](https://whatcanirunai.vercel.app/hardware/rtx-3070/)GPU · $499                                                      | **3.2** / 8.0 GB   | Q4FitsQ8FitsFP16Won’t fit | 138–196    |
| [GeForce RTX 2070](https://whatcanirunai.vercel.app/hardware/rtx-2070/)GPU · $499                                                      | **3.2** / 8.0 GB   | Q4FitsQ8FitsFP16Won’t fit | 138–196    |
| [GeForce RTX 2070 Super](https://whatcanirunai.vercel.app/hardware/rtx-2070-super/)GPU · $499                                          | **3.2** / 8.0 GB   | Q4FitsQ8FitsFP16Won’t fit | 138–196    |
| [GeForce RTX 3070 Ti](https://whatcanirunai.vercel.app/hardware/rtx-3070-ti/)GPU · $599                                                | **3.2** / 8.0 GB   | Q4FitsQ8FitsFP16Won’t fit | 187–265    |
| [GeForce RTX 2080](https://whatcanirunai.vercel.app/hardware/rtx-2080/)GPU · $699                                                      | **3.2** / 8.0 GB   | Q4FitsQ8FitsFP16Won’t fit | 138–196    |
| [GeForce RTX 2080 Super](https://whatcanirunai.vercel.app/hardware/rtx-2080-super/)GPU · $699                                          | **3.2** / 8.0 GB   | Q4FitsQ8FitsFP16Won’t fit | 153–216    |
| [Intel Arc B570](https://whatcanirunai.vercel.app/hardware/arc-b570/)GPU · $219                                                        | **3.2** / 10.0 GB  | Q4FitsQ8FitsFP16Fits      | 117–166    |
| [GeForce RTX 3080 10GB](https://whatcanirunai.vercel.app/hardware/rtx-3080-10gb/)GPU · $699                                            | **3.2** / 10.0 GB  | Q4FitsQ8FitsFP16Fits      | 234–332    |
| [GeForce RTX 2080 Ti](https://whatcanirunai.vercel.app/hardware/rtx-2080-ti/)GPU · $999                                                | **3.2** / 11.0 GB  | Q4FitsQ8FitsFP16Fits      | 190–269    |
| [GeForce RTX 2060 12GB](https://whatcanirunai.vercel.app/hardware/rtx-2060-12gb/)GPU                                                   | **3.2** / 12.0 GB  | Q4FitsQ8FitsFP16Fits      | 104–147    |
| [Intel Arc B580](https://whatcanirunai.vercel.app/hardware/arc-b580/)GPU · $249                                                        | **3.2** / 12.0 GB  | Q4FitsQ8FitsFP16Fits      | 140–199    |
| [GeForce RTX 3060 12GB](https://whatcanirunai.vercel.app/hardware/rtx-3060-12gb/)GPU · $329                                            | **3.2** / 12.0 GB  | Q4FitsQ8FitsFP16Fits      | 111–157    |
| [Radeon RX 7700 XT](https://whatcanirunai.vercel.app/hardware/rx-7700-xt/)GPU · $449                                                   | **3.2** / 12.0 GB  | Q4FitsQ8FitsFP16Fits      | 133–189    |
| [Radeon RX 6700 XT](https://whatcanirunai.vercel.app/hardware/rx-6700-xt/)GPU · $479                                                   | **3.2** / 12.0 GB  | Q4FitsQ8FitsFP16Fits      | 118–168    |
| [GeForce RTX 5070](https://whatcanirunai.vercel.app/hardware/rtx-5070/)GPU · $549                                                      | **3.2** / 12.0 GB  | Q4FitsQ8FitsFP16Fits      | 207–293    |
| [Radeon RX 6750 XT](https://whatcanirunai.vercel.app/hardware/rx-6750-xt/)GPU · $549                                                   | **3.2** / 12.0 GB  | Q4FitsQ8FitsFP16Fits      | 133–189    |
| [GeForce RTX 4070](https://whatcanirunai.vercel.app/hardware/rtx-4070/)GPU · $599                                                      | **3.2** / 12.0 GB  | Q4FitsQ8FitsFP16Fits      | 155–220    |
| [GeForce RTX 4070 Super](https://whatcanirunai.vercel.app/hardware/rtx-4070-super/)GPU · $599                                          | **3.2** / 12.0 GB  | Q4FitsQ8FitsFP16Fits      | 155–220    |
| [Mac mini M4 16GB](https://whatcanirunai.vercel.app/hardware/mac-mini-m4-16/)unified memory · $789                                     | **3.2** / 12.0 GB  | Q4FitsQ8FitsFP16Fits      | 28–49      |
| [GeForce RTX 3080 12GB](https://whatcanirunai.vercel.app/hardware/rtx-3080-12gb/)GPU · $799                                            | **3.2** / 12.0 GB  | Q4FitsQ8FitsFP16Fits      | 281–398    |
| [GeForce RTX 4070 Ti](https://whatcanirunai.vercel.app/hardware/rtx-4070-ti/)GPU · $799                                                | **3.2** / 12.0 GB  | Q4FitsQ8FitsFP16Fits      | 155–220    |
| [Mac mini M6 16GB](https://whatcanirunai.vercel.app/hardware/mac-mini-m6-16/)unified memory · $899                                     | **3.2** / 12.0 GB  | Q4FitsQ8FitsFP16Fits      | 35–63      |
| [GeForce RTX 3080 Ti](https://whatcanirunai.vercel.app/hardware/rtx-3080-ti/)GPU · $1,199                                              | **3.2** / 12.0 GB  | Q4FitsQ8FitsFP16Fits      | 281–398    |
| [Radeon RX 7600 XT](https://whatcanirunai.vercel.app/hardware/rx-7600-xt/)GPU · $329                                                   | **3.2** / 16.0 GB  | Q4FitsQ8FitsFP16Fits      | 89–126     |
| [Radeon RX 9060 XT 16GB](https://whatcanirunai.vercel.app/hardware/rx-9060-xt-16gb/)GPU · $349                                         | **3.2** / 16.0 GB  | Q4FitsQ8FitsFP16Fits      | 99–140     |
| [Intel Arc A770 16GB](https://whatcanirunai.vercel.app/hardware/arc-a770-16gb/)GPU · $349                                              | **3.2** / 16.0 GB  | Q4FitsQ8FitsFP16Fits      | 173–244    |
| [GeForce RTX 5060 Ti 16GB](https://whatcanirunai.vercel.app/hardware/rtx-5060-ti-16gb/)GPU · $429                                      | **3.2** / 16.0 GB  | Q4FitsQ8FitsFP16Fits      | 138–196    |
| [GeForce RTX 4060 Ti 16GB](https://whatcanirunai.vercel.app/hardware/rtx-4060-ti-16gb/)GPU · $499                                      | **3.2** / 16.0 GB  | Q4FitsQ8FitsFP16Fits      | 89–126     |
| [Radeon RX 7800 XT](https://whatcanirunai.vercel.app/hardware/rx-7800-xt/)GPU · $499                                                   | **3.2** / 16.0 GB  | Q4FitsQ8FitsFP16Fits      | 192–272    |
| [Radeon RX 7900 GRE](https://whatcanirunai.vercel.app/hardware/rx-7900-gre/)GPU · $549                                                 | **3.2** / 16.0 GB  | Q4FitsQ8FitsFP16Fits      | 177–251    |
| [Radeon RX 9070](https://whatcanirunai.vercel.app/hardware/rx-9070/)GPU · $549                                                         | **3.2** / 16.0 GB  | Q4FitsQ8FitsFP16Fits      | 197–279    |
| [Radeon RX 6800](https://whatcanirunai.vercel.app/hardware/rx-6800/)GPU · $579                                                         | **3.2** / 16.0 GB  | Q4FitsQ8FitsFP16Fits      | 158–223    |
| [Radeon RX 9070 XT](https://whatcanirunai.vercel.app/hardware/rx-9070-xt/)GPU · $599                                                   | **3.2** / 16.0 GB  | Q4FitsQ8FitsFP16Fits      | 197–279    |
| [Radeon RX 6800 XT](https://whatcanirunai.vercel.app/hardware/rx-6800-xt/)GPU · $649                                                   | **3.2** / 16.0 GB  | Q4FitsQ8FitsFP16Fits      | 158–223    |
| [NVIDIA RTX 2000 Ada 16GB](https://whatcanirunai.vercel.app/hardware/rtx-2000-ada/)GPU · $649                                          | **3.2** / 16.0 GB  | Q4FitsQ8FitsFP16Fits      | 69–98      |
| [GeForce RTX 5070 Ti](https://whatcanirunai.vercel.app/hardware/rtx-5070-ti/)GPU · $749                                                | **3.2** / 16.0 GB  | Q4FitsQ8FitsFP16Fits      | 276–391    |
| [GeForce RTX 4070 Ti Super](https://whatcanirunai.vercel.app/hardware/rtx-4070-ti-super/)GPU · $799                                    | **3.2** / 16.0 GB  | Q4FitsQ8FitsFP16Fits      | 207–293    |
| [GeForce RTX 4080 Super](https://whatcanirunai.vercel.app/hardware/rtx-4080-super/)GPU · $999                                          | **3.2** / 16.0 GB  | Q4FitsQ8FitsFP16Fits      | 227–321    |
| [GeForce RTX 5080](https://whatcanirunai.vercel.app/hardware/rtx-5080/)GPU · $999                                                      | **3.2** / 16.0 GB  | Q4FitsQ8FitsFP16Fits      | 296–419    |
| [Radeon RX 6900 XT](https://whatcanirunai.vercel.app/hardware/rx-6900-xt/)GPU · $999                                                   | **3.2** / 16.0 GB  | Q4FitsQ8FitsFP16Fits      | 158–223    |
| [NVIDIA RTX A4000](https://whatcanirunai.vercel.app/hardware/rtx-a4000/)GPU · $1,000                                                   | **3.2** / 16.0 GB  | Q4FitsQ8FitsFP16Fits      | 138–196    |
| [Radeon RX 6950 XT](https://whatcanirunai.vercel.app/hardware/rx-6950-xt/)GPU · $1,099                                                 | **3.2** / 16.0 GB  | Q4FitsQ8FitsFP16Fits      | 177–251    |
| [GeForce RTX 4080](https://whatcanirunai.vercel.app/hardware/rtx-4080/)GPU · $1,199                                                    | **3.2** / 16.0 GB  | Q4FitsQ8FitsFP16Fits      | 221–313    |
| [Mac mini M6 24GB](https://whatcanirunai.vercel.app/hardware/mac-mini-m6-24/)unified memory · $1,299                                   | **3.2** / 18.0 GB  | Q4FitsQ8FitsFP16Fits      | 39–70      |
| [Radeon RX 7900 XT](https://whatcanirunai.vercel.app/hardware/rx-7900-xt/)GPU · $899                                                   | **3.2** / 20.0 GB  | Q4FitsQ8FitsFP16Fits      | 246–349    |
| [NVIDIA RTX 4000 Ada 20GB](https://whatcanirunai.vercel.app/hardware/rtx-4000-ada/)GPU · $1,250                                        | **3.2** / 20.0 GB  | Q4FitsQ8FitsFP16Fits      | 111–157    |
| [NVIDIA Tesla P40 24GB](https://whatcanirunai.vercel.app/hardware/tesla-p40/)GPU                                                       | **3.2** / 24.0 GB  | Q4FitsQ8FitsFP16Fits      | 107–151    |
| [Radeon RX 7900 XTX](https://whatcanirunai.vercel.app/hardware/rx-7900-xtx/)GPU · $999                                                 | **3.2** / 24.0 GB  | Q4FitsQ8FitsFP16Fits      | 296–419    |
| [GeForce RTX 3090](https://whatcanirunai.vercel.app/hardware/rtx-3090/)GPU · $1,499                                                    | **3.2** / 24.0 GB  | Q4FitsQ8FitsFP16Fits      | 288–408    |
| [GeForce RTX 4090](https://whatcanirunai.vercel.app/hardware/rtx-4090/)GPU · $1,599                                                    | **3.2** / 24.0 GB  | Q4FitsQ8FitsFP16Fits      | 311–440    |
| [Mac mini M6 32GB](https://whatcanirunai.vercel.app/hardware/mac-mini-m6-32/)unified memory · $1,799                                   | **3.2** / 24.0 GB  | Q4FitsQ8FitsFP16Fits      | 39–70      |
| [GeForce RTX 3090 Ti](https://whatcanirunai.vercel.app/hardware/rtx-3090-ti/)GPU · $1,999                                              | **3.2** / 24.0 GB  | Q4FitsQ8FitsFP16Fits      | 311–440    |
| [NVIDIA RTX A5000](https://whatcanirunai.vercel.app/hardware/rtx-a5000/)GPU · $2,250                                                   | **3.2** / 24.0 GB  | Q4FitsQ8FitsFP16Fits      | 237–335    |
| [MacBook Pro M6 32GB](https://whatcanirunai.vercel.app/hardware/macbook-pro-m6-32/)unified memory · $2,399                             | **3.2** / 24.0 GB  | Q4FitsQ8FitsFP16Fits      | 39–70      |
| [NVIDIA Titan RTX](https://whatcanirunai.vercel.app/hardware/titan-rtx/)GPU · $2,499                                                   | **3.2** / 24.0 GB  | Q4FitsQ8FitsFP16Fits      | 207–293    |
| [NVIDIA Tesla V100 32GB (PCIe)](https://whatcanirunai.vercel.app/hardware/tesla-v100-32gb/)GPU                                         | **3.2** / 32.0 GB  | Q4FitsQ8FitsFP16Fits      | 277–393    |
| [AMD Radeon Instinct MI50 32GB](https://whatcanirunai.vercel.app/hardware/instinct-mi50-32gb/)GPU                                      | **3.2** / 32.0 GB  | Q4FitsQ8FitsFP16Fits      | 315–447    |
| [AMD Radeon Instinct MI60 32GB](https://whatcanirunai.vercel.app/hardware/instinct-mi60/)GPU                                           | **3.2** / 32.0 GB  | Q4FitsQ8FitsFP16Fits      | 315–447    |
| [Intel Arc Pro B70 32GB](https://whatcanirunai.vercel.app/hardware/arc-pro-b70/)GPU · $949                                             | **3.2** / 32.0 GB  | Q4FitsQ8FitsFP16Fits      | 187–265    |
| [GeForce RTX 5090](https://whatcanirunai.vercel.app/hardware/rtx-5090/)GPU · $1,999                                                    | **3.2** / 32.0 GB  | Q4FitsQ8FitsFP16Fits      | 552–782    |
| [Mac mini M5 Pro 64GB](https://whatcanirunai.vercel.app/hardware/mac-mini-m5-pro-64/)unified memory · $3,199                           | **3.2** / 48.0 GB  | Q4FitsQ8FitsFP16Fits      | 71–126     |
| [NVIDIA RTX A6000](https://whatcanirunai.vercel.app/hardware/rtx-a6000/)GPU · $4,649                                                   | **3.2** / 48.0 GB  | Q4FitsQ8FitsFP16Fits      | 237–335    |
| [NVIDIA DGX Spark 64GB](https://whatcanirunai.vercel.app/hardware/dgx-spark-64/)unified memory · $4,999                                | **3.2** / 48.0 GB  | Q4FitsQ8FitsFP16Fits      | 63–112     |
| [NVIDIA RTX 6000 Ada 48GB](https://whatcanirunai.vercel.app/hardware/rtx-6000-ada/)GPU · $6,799                                        | **3.2** / 48.0 GB  | Q4FitsQ8FitsFP16Fits      | 296–419    |
| [Framework Desktop (Ryzen AI Max+ 395) 128GB](https://whatcanirunai.vercel.app/hardware/framework-desktop-128/)unified memory · $3,449 | **3.2** / 96.0 GB  | Q4FitsQ8FitsFP16Fits      | 59–105     |
| [MacBook Pro M4 Max 128GB](https://whatcanirunai.vercel.app/hardware/macbook-pro-m4-max-128/)unified memory · $4,999                   | **3.2** / 96.0 GB  | Q4FitsQ8FitsFP16Fits      | 126–224    |
| [MacBook Pro M5 Max 128GB](https://whatcanirunai.vercel.app/hardware/macbook-pro-m5-max-128/)unified memory · $6,699                   | **3.2** / 96.0 GB  | Q4FitsQ8FitsFP16Fits      | 142–252    |
| [NVIDIA DGX Spark 128GB](https://whatcanirunai.vercel.app/hardware/dgx-spark-128/)unified memory · $6,950                              | **3.2** / 96.0 GB  | Q4FitsQ8FitsFP16Fits      | 63–112     |
| [NVIDIA RTX Pro 6000 Blackwell 96GB](https://whatcanirunai.vercel.app/hardware/rtx-pro-6000-blackwell/)GPU · $8,565                    | **3.2** / 96.0 GB  | Q4FitsQ8FitsFP16Fits      | 552–782    |
| [Mac Studio M5 Ultra 256GB](https://whatcanirunai.vercel.app/hardware/mac-studio-m5-ultra-256/)unified memory · $9,499                 | **3.2** / 192.0 GB | Q4FitsQ8FitsFP16Fits      | 277–493    |
| [Mac Studio M5 Ultra 512GB](https://whatcanirunai.vercel.app/hardware/mac-studio-m5-ultra-512/)unified memory · $22,299                | **3.2** / 384.0 GB | Q4FitsQ8FitsFP16Fits      | 277–493    |

## 2B–3B models we track

__Memory needed by each 2B–3B model we track__
| Model                                                                                                                | Q4\_K\_M | Q8\_0  | FP16   |
| -------------------------------------------------------------------------------------------------------------------- | -------- | ------ | ------ |
| [Qwen3-1.7B](https://whatcanirunai.vercel.app/models/qwen3-1.7b/)2.0B params · Qwen                                                                  | 2.4 GB   | 3.5 GB | 5.7 GB |
| [Hy-MT2-1.8B](https://whatcanirunai.vercel.app/models/hy-mt2-1.8b/)2.0B params · tencent                                                             | 2.4 GB   | 3.5 GB | 5.7 GB |
| [kanana-nano-2.1b-instruct](https://whatcanirunai.vercel.app/models/kanana-nano-2.1b-instruct/)2.1B params · kakaocorp                               | 2.5 GB   | 3.6 GB | 5.8 GB |
| [Qwen3-VL-2B-Instruct](https://whatcanirunai.vercel.app/models/qwen3-vl-2b-instruct/)2.1B params · Qwen                                              | 2.5 GB   | 3.6 GB | 5.9 GB |
| [granite-swash-2b](https://whatcanirunai.vercel.app/models/granite-swash-2b/)2.1B params · ibm-granite                                               | 2.5 GB   | 3.6 GB | 5.9 GB |
| [LFM3-Omni-2B](https://whatcanirunai.vercel.app/models/lfm3-omni-2b/)2.2B params · LiquidAI                                                          | 2.5 GB   | 3.6 GB | 6.0 GB |
| [Qwen2-VL-2B-Instruct](https://whatcanirunai.vercel.app/models/qwen2-vl-2b-instruct/)2.2B params · Qwen                                              | 2.5 GB   | 3.7 GB | 6.1 GB |
| [SmolVLM-Base](https://whatcanirunai.vercel.app/models/smolvlm-base/)2.2B params · HuggingFaceTB                                                     | 2.6 GB   | 3.7 GB | 6.2 GB |
| [SmolVLM2-2.2B-Instruct](https://whatcanirunai.vercel.app/models/smolvlm2-2.2b-instruct/)2.2B params · HuggingFaceTB                                 | 2.6 GB   | 3.7 GB | 6.2 GB |
| [salamandra-2b](https://whatcanirunai.vercel.app/models/salamandra-2b/)2.3B params · BSC-LT                                                          | 2.6 GB   | 3.8 GB | 6.2 GB |
| [salamandra-2b-instruct](https://whatcanirunai.vercel.app/models/salamandra-2b-instruct/)2.3B params · BSC-LT                                        | 2.6 GB   | 3.8 GB | 6.2 GB |
| [Qwen3.5-2B](https://whatcanirunai.vercel.app/models/qwen3.5-2b/)2.3B params · Qwen                                                                  | 2.6 GB   | 3.8 GB | 6.2 GB |
| [kanana-1.5-2.1b-instruct-2505](https://whatcanirunai.vercel.app/models/kanana-1.5-2.1b-instruct-2505/)2.3B params · kakaocorp                       | 2.6 GB   | 3.8 GB | 6.3 GB |
| [EXAONE-3.5-2.4B-Instruct](https://whatcanirunai.vercel.app/models/exaone-3.5-2.4b-instruct/)2.4B params · LGAI-EXAONE                               | 2.7 GB   | 3.9 GB | 6.5 GB |
| [EXAONE-Deep-2.4B](https://whatcanirunai.vercel.app/models/exaone-deep-2.4b/)2.4B params · LGAI-EXAONE                                               | 2.7 GB   | 3.9 GB | 6.5 GB |
| [bitnet-b1.58-2B-4T-bf16](https://whatcanirunai.vercel.app/models/bitnet-b1.58-2b-4t-bf16/)2.4B params · microsoft                                   | 2.7 GB   | 3.9 GB | 6.5 GB |
| [UI-Venus-1.5-2B](https://whatcanirunai.vercel.app/models/ui-venus-1.5-2b/)2.4B params · inclusionAI                                                 | 2.7 GB   | 4.0 GB | 6.6 GB |
| [WeVisOne-VL-2B](https://whatcanirunai.vercel.app/models/wevisone-vl-2b/)2.4B params · tencent                                                       | 2.7 GB   | 4.0 GB | 6.6 GB |
| [UI-TARS-2B-SFT](https://whatcanirunai.vercel.app/models/ui-tars-2b-sft/)2.4B params · ByteDance-Seed                                                | 2.7 GB   | 4.0 GB | 6.6 GB |
| [North-Micro-Vision-Instruct](https://whatcanirunai.vercel.app/models/north-micro-vision-instruct/)2.5B params · CohereLabs                          | 2.7 GB   | 4.0 GB | 6.7 GB |
| [sarvam-1-v0.5](https://whatcanirunai.vercel.app/models/sarvam-1-v0.5/)2.5B params · sarvamai                                                        | 2.7 GB   | 4.1 GB | 6.8 GB |
| [MiniCPM5-2B](https://whatcanirunai.vercel.app/models/minicpm5-2b/)2.5B params · openbmb                                                             | 2.8 GB   | 4.1 GB | 6.8 GB |
| [MiniCPM5-2B-SFT](https://whatcanirunai.vercel.app/models/minicpm5-2b-sft/)2.5B params · openbmb                                                     | 2.8 GB   | 4.1 GB | 6.8 GB |
| [MiniCPM5-2B-Midtrain](https://whatcanirunai.vercel.app/models/minicpm5-2b-midtrain/)2.5B params · openbmb                                           | 2.8 GB   | 4.1 GB | 6.8 GB |
| [granite-3.1-2b-instruct](https://whatcanirunai.vercel.app/models/granite-3.1-2b-instruct/)2.5B params · ibm-granite                                 | 2.8 GB   | 4.1 GB | 6.8 GB |
| [granite-3.2-2b-instruct](https://whatcanirunai.vercel.app/models/granite-3.2-2b-instruct/)2.5B params · ibm-granite                                 | 2.8 GB   | 4.1 GB | 6.8 GB |
| [granite-3.3-2b-instruct](https://whatcanirunai.vercel.app/models/granite-3.3-2b-instruct/)2.5B params · ibm-granite                                 | 2.8 GB   | 4.1 GB | 6.8 GB |
| [LFM2-2.6B](https://whatcanirunai.vercel.app/models/lfm2-2.6b/)2.6B params · LiquidAI                                                                | 2.8 GB   | 4.1 GB | 6.9 GB |
| [LFM2-2.6B-Exp](https://whatcanirunai.vercel.app/models/lfm2-2.6b-exp/)2.6B params · LiquidAI                                                        | 2.8 GB   | 4.1 GB | 6.9 GB |
| [LFM2-2.6B-Transcript](https://whatcanirunai.vercel.app/models/lfm2-2.6b-transcript/)2.6B params · LiquidAI                                          | 2.8 GB   | 4.1 GB | 6.9 GB |
| [Motif-2.6B](https://whatcanirunai.vercel.app/models/motif-2.6b/)2.6B params · Motif-Technologies                                                    | 2.8 GB   | 4.2 GB | 7.0 GB |
| [EuroMoE-2.6B-A0.6B-Instruct-Preview](https://whatcanirunai.vercel.app/models/euromoe-2.6b-a0.6b-instruct-preview/)2.6B params · utter-project · MoE | 2.8 GB   | 4.2 GB | 7.0 GB |
| [gemma-2-2b-it](https://whatcanirunai.vercel.app/models/gemma-2-2b-it/)2.6B params · google                                                          | 2.8 GB   | 4.2 GB | 7.0 GB |
| [gemma-2-2b](https://whatcanirunai.vercel.app/models/gemma-2-2b/)2.6B params · google                                                                | 2.8 GB   | 4.2 GB | 7.0 GB |
| [granite-3.0-2b-instruct](https://whatcanirunai.vercel.app/models/granite-3.0-2b-instruct/)2.6B params · ibm-granite                                 | 2.8 GB   | 4.2 GB | 7.1 GB |
| [Zamba2-2.7B](https://whatcanirunai.vercel.app/models/zamba2-2.7b/)2.7B params · Zyphra                                                              | 2.9 GB   | 4.3 GB | 7.1 GB |
| [Zamba2-2.7B-instruct](https://whatcanirunai.vercel.app/models/zamba2-2.7b-instruct/)2.7B params · Zyphra                                            | 2.9 GB   | 4.3 GB | 7.1 GB |
| [LFM2.5-2.6B](https://whatcanirunai.vercel.app/models/lfm2.5-2.6b/)2.7B params · LiquidAI                                                            | 2.9 GB   | 4.3 GB | 7.2 GB |
| [granite-vision-3.2-2b](https://whatcanirunai.vercel.app/models/granite-vision-3.2-2b/)3.0B params · ibm-granite                                     | 3.1 GB   | 4.6 GB | 7.8 GB |
| [granite-vision-3.1-2b-preview](https://whatcanirunai.vercel.app/models/granite-vision-3.1-2b-preview/)3.0B params · ibm-granite                     | 3.1 GB   | 4.6 GB | 7.8 GB |
| [granite-vision-3.3-2b-chart2csv-preview](https://whatcanirunai.vercel.app/models/granite-vision-3.3-2b-chart2csv-preview/)3.0B params · ibm-granite | 3.1 GB   | 4.6 GB | 7.8 GB |
| [granite-swash-3b-a600m](https://whatcanirunai.vercel.app/models/granite-swash-3b-a600m/)3.0B params · ibm-granite · MoE                             | 3.1 GB   | 4.7 GB | 7.9 GB |
| [AI21-Jamba2-3B](https://whatcanirunai.vercel.app/models/ai21-jamba2-3b/)3.0B params · ai21labs                                                      | 3.1 GB   | 4.7 GB | 8.0 GB |
| [SmolLM3-3B](https://whatcanirunai.vercel.app/models/smollm3-3b/)3.1B params · HuggingFaceTB                                                         | 3.1 GB   | 4.8 GB | 8.1 GB |
| [Qwen2.5-3B-Instruct](https://whatcanirunai.vercel.app/models/qwen2.5-3b-instruct/)3.1B params · Qwen                                                | 3.2 GB   | 4.8 GB | 8.1 GB |
| [Qwen2.5-Coder-3B-Instruct](https://whatcanirunai.vercel.app/models/qwen2.5-coder-3b-instruct/)3.1B params · Qwen                                    | 3.2 GB   | 4.8 GB | 8.1 GB |
| [Qwen2.5-3B](https://whatcanirunai.vercel.app/models/qwen2.5-3b/)3.1B params · Qwen                                                                  | 3.2 GB   | 4.8 GB | 8.1 GB |
| [Qwen2.5-Coder-3B](https://whatcanirunai.vercel.app/models/qwen2.5-coder-3b/)3.1B params · Qwen                                                      | 3.2 GB   | 4.8 GB | 8.1 GB |
| [d1-3B](https://whatcanirunai.vercel.app/models/d1-3b/)3.1B params · LiquidAI                                                                        | 3.2 GB   | 4.8 GB | 8.2 GB |
| [LFM2.5-VL-3B](https://whatcanirunai.vercel.app/models/lfm2.5-vl-3b/)3.1B params · LiquidAI                                                          | 3.2 GB   | 4.8 GB | 8.2 GB |
| [Falcon-H1-3B-Instruct](https://whatcanirunai.vercel.app/models/falcon-h1-3b-instruct/)3.1B params · tiiuae                                          | 3.2 GB   | 4.8 GB | 8.2 GB |
| [granite-4.0-h-micro](https://whatcanirunai.vercel.app/models/granite-4.0-h-micro/)3.2B params · ibm-granite                                         | 3.2 GB   | 4.9 GB | 8.3 GB |
| [AI21-Jamba-Reasoning-3B](https://whatcanirunai.vercel.app/models/ai21-jamba-reasoning-3b/)3.2B params · ai21labs                                    | 3.2 GB   | 4.9 GB | 8.4 GB |
| [Llama-3.2-3B-Instruct](https://whatcanirunai.vercel.app/models/llama-3.2-3b-instruct/)3.2B params · meta-llama                                      | 3.2 GB   | 4.9 GB | 8.4 GB |
| [Llama-3.2-3B](https://whatcanirunai.vercel.app/models/llama-3.2-3b/)3.2B params · meta-llama                                                        | 3.2 GB   | 4.9 GB | 8.4 GB |
| [Hermes-3-Llama-3.2-3B](https://whatcanirunai.vercel.app/models/hermes-3-llama-3.2-3b/)3.2B params · NousResearch                                    | 3.2 GB   | 4.9 GB | 8.4 GB |
| [DeepHermes-3-Llama-3-3B-Preview](https://whatcanirunai.vercel.app/models/deephermes-3-llama-3-3b-preview/)3.2B params · NousResearch                | 3.2 GB   | 4.9 GB | 8.4 GB |
| [Falcon3-3B-Instruct](https://whatcanirunai.vercel.app/models/falcon3-3b-instruct/)3.2B params · tiiuae                                              | 3.3 GB   | 4.9 GB | 8.4 GB |
| [granite-3.1-3b-a800m-instruct](https://whatcanirunai.vercel.app/models/granite-3.1-3b-a800m-instruct/)3.3B params · ibm-granite · MoE               | 3.3 GB   | 5.0 GB | 8.6 GB |
| [granite-4.1-3b](https://whatcanirunai.vercel.app/models/granite-4.1-3b/)3.4B params · ibm-granite                                                   | 3.4 GB   | 5.2 GB | 8.8 GB |
| [granite-4.0-micro](https://whatcanirunai.vercel.app/models/granite-4.0-micro/)3.4B params · ibm-granite                                             | 3.4 GB   | 5.2 GB | 8.8 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 2B–3B model?

About 3.2 GB at Q4\_K\_M, 4.9 GB at Q8\_0 and 8.4 GB at FP16, at a \~4k context. That is for Llama-3.2-3B-Instruct (3.2B parameters): weights plus about 15% for the KV cache and 1 GB for the runtime.

How much VRAM does a 2B–3B model need for 32k context?

About 6.5 GB at Q4\_K\_M with an FP16 KV cache, or 4.6 GB with a Q8 cache, based on Llama-3.2-3B-Instruct's published config. Models with different attention layouts need more or less.

What is the cheapest GPU for a 2B–3B 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 2B–3B 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/) · [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/) · [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/)

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Agent guide: https://whatcanirunai.vercel.app/llms.txt · JSON API: https://whatcanirunai.vercel.app/developers/