# How much VRAM do you need for a 4B–6B model?

> A 4B–6B LLM needs about 3.8 GB of VRAM at Q4, 5.9 GB at Q8 and 10.3 GB at FP16. Context-length table and the cheapest GPU and Mac that fit.

Source: https://whatcanirunai.vercel.app/how-much-vram/4b/

# How much VRAM do you need for a 4B–6B model?

Short answer: about **3.8 GB** at Q4\_K\_M, the usual quant for local use. Below: every quant, longer contexts, the cheapest hardware that fits, and every 4B–6B open model we track.

Q4\_K\_M

3.8 GB 2.4 GB weights

Q8\_0

5.9 GB 4.3 GB weights

FP16

10.3 GB 8.0 GB weights

Worked out for [Qwen3-4B](https://whatcanirunai.vercel.app/models/qwen3-4b/) (4.0B 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 4B–6B model at each quant__
| Setting                | Memory needed | Cheapest GPU                                              | Cheapest Mac / mini PC                                            |
| ---------------------- | ------------- | --------------------------------------------------------- | ----------------------------------------------------------------- |
| Q4\_K\_M, \~4k context | 3.8 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    | 5.9 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     | 10.3 GB       | [Intel Arc B580](https://whatcanirunai.vercel.app/hardware/arc-b580/) 12 GB usable · $249 | [Mac mini M4 16GB](https://whatcanirunai.vercel.app/hardware/mac-mini-m4-16/) 12 GB usable · $789 |
| Q4\_K\_M, 32k context  | 8.0 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 Qwen3-4B, from its published config; a Q8 KV cache halves the cache part.

__Memory needed for a 4B–6B model by context length__
| Context     | Q4\_K\_M, FP16 cache | Q4\_K\_M, Q8 cache | Q8\_0, FP16 cache |
| ----------- | -------------------- | ------------------ | ----------------- |
| 4k tokens   | 3.8 GB               | 3.8 GB             | 5.9 GB            |
| 8k tokens   | 4.4 GB               | 3.8 GB             | 6.5 GB            |
| 16k tokens  | 5.6 GB               | 4.4 GB             | 7.7 GB            |
| 32k tokens  | 8.0 GB               | 5.6 GB             | 10.1 GB           |
| 64k tokens  | 12.9 GB              | 8.0 GB             | 15.0 GB           |
| 128k tokens | 22.5 GB              | 12.9 GB            | 24.6 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 4B–6B 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                                                  | **3.8** / 6.0 GB   | Q4FitsQ8TightFP16Won’t fit | 83–117     |
| [GeForce RTX 3050 8GB](https://whatcanirunai.vercel.app/hardware/rtx-3050-8gb/)GPU · $249                                              | **3.8** / 8.0 GB   | Q4FitsQ8FitsFP16Won’t fit  | 55–78      |
| [Radeon RX 7600](https://whatcanirunai.vercel.app/hardware/rx-7600/)GPU · $269                                                         | **3.8** / 8.0 GB   | Q4FitsQ8FitsFP16Won’t fit  | 71–100     |
| [Intel Arc A750](https://whatcanirunai.vercel.app/hardware/arc-a750/)GPU · $289                                                        | **3.8** / 8.0 GB   | Q4FitsQ8FitsFP16Won’t fit  | 126–178    |
| [GeForce RTX 4060](https://whatcanirunai.vercel.app/hardware/rtx-4060/)GPU · $299                                                      | **3.8** / 8.0 GB   | Q4FitsQ8FitsFP16Won’t fit  | 67–95      |
| [GeForce RTX 5060](https://whatcanirunai.vercel.app/hardware/rtx-5060/)GPU · $299                                                      | **3.8** / 8.0 GB   | Q4FitsQ8FitsFP16Won’t fit  | 110–156    |
| [Radeon RX 9060 XT 8GB](https://whatcanirunai.vercel.app/hardware/rx-9060-xt-8gb/)GPU · $299                                           | **3.8** / 8.0 GB   | Q4FitsQ8FitsFP16Won’t fit  | 79–112     |
| [GeForce RTX 5060 Ti 8GB](https://whatcanirunai.vercel.app/hardware/rtx-5060-ti-8gb/)GPU · $379                                        | **3.8** / 8.0 GB   | Q4FitsQ8FitsFP16Won’t fit  | 110–156    |
| [GeForce RTX 3060 Ti](https://whatcanirunai.vercel.app/hardware/rtx-3060-ti/)GPU · $399                                                | **3.8** / 8.0 GB   | Q4FitsQ8FitsFP16Won’t fit  | 110–156    |
| [GeForce RTX 2060 Super](https://whatcanirunai.vercel.app/hardware/rtx-2060-super/)GPU · $399                                          | **3.8** / 8.0 GB   | Q4FitsQ8FitsFP16Won’t fit  | 110–156    |
| [GeForce RTX 4060 Ti 8GB](https://whatcanirunai.vercel.app/hardware/rtx-4060-ti-8gb/)GPU · $399                                        | **3.8** / 8.0 GB   | Q4FitsQ8FitsFP16Won’t fit  | 71–100     |
| [GeForce RTX 3070](https://whatcanirunai.vercel.app/hardware/rtx-3070/)GPU · $499                                                      | **3.8** / 8.0 GB   | Q4FitsQ8FitsFP16Won’t fit  | 110–156    |
| [GeForce RTX 2070](https://whatcanirunai.vercel.app/hardware/rtx-2070/)GPU · $499                                                      | **3.8** / 8.0 GB   | Q4FitsQ8FitsFP16Won’t fit  | 110–156    |
| [GeForce RTX 2070 Super](https://whatcanirunai.vercel.app/hardware/rtx-2070-super/)GPU · $499                                          | **3.8** / 8.0 GB   | Q4FitsQ8FitsFP16Won’t fit  | 110–156    |
| [GeForce RTX 3070 Ti](https://whatcanirunai.vercel.app/hardware/rtx-3070-ti/)GPU · $599                                                | **3.8** / 8.0 GB   | Q4FitsQ8FitsFP16Won’t fit  | 150–212    |
| [GeForce RTX 2080](https://whatcanirunai.vercel.app/hardware/rtx-2080/)GPU · $699                                                      | **3.8** / 8.0 GB   | Q4FitsQ8FitsFP16Won’t fit  | 110–156    |
| [GeForce RTX 2080 Super](https://whatcanirunai.vercel.app/hardware/rtx-2080-super/)GPU · $699                                          | **3.8** / 8.0 GB   | Q4FitsQ8FitsFP16Won’t fit  | 122–173    |
| [Intel Arc B570](https://whatcanirunai.vercel.app/hardware/arc-b570/)GPU · $219                                                        | **3.8** / 10.0 GB  | Q4FitsQ8FitsFP16Won’t fit  | 93–132     |
| [GeForce RTX 3080 10GB](https://whatcanirunai.vercel.app/hardware/rtx-3080-10gb/)GPU · $699                                            | **3.8** / 10.0 GB  | Q4FitsQ8FitsFP16Won’t fit  | 187–265    |
| [GeForce RTX 2080 Ti](https://whatcanirunai.vercel.app/hardware/rtx-2080-ti/)GPU · $999                                                | **3.8** / 11.0 GB  | Q4FitsQ8FitsFP16Tight      | 152–215    |
| [GeForce RTX 2060 12GB](https://whatcanirunai.vercel.app/hardware/rtx-2060-12gb/)GPU                                                   | **3.8** / 12.0 GB  | Q4FitsQ8FitsFP16Fits       | 83–117     |
| [Intel Arc B580](https://whatcanirunai.vercel.app/hardware/arc-b580/)GPU · $249                                                        | **3.8** / 12.0 GB  | Q4FitsQ8FitsFP16Fits       | 112–159    |
| [GeForce RTX 3060 12GB](https://whatcanirunai.vercel.app/hardware/rtx-3060-12gb/)GPU · $329                                            | **3.8** / 12.0 GB  | Q4FitsQ8FitsFP16Fits       | 89–125     |
| [Radeon RX 7700 XT](https://whatcanirunai.vercel.app/hardware/rx-7700-xt/)GPU · $449                                                   | **3.8** / 12.0 GB  | Q4FitsQ8FitsFP16Fits       | 106–151    |
| [Radeon RX 6700 XT](https://whatcanirunai.vercel.app/hardware/rx-6700-xt/)GPU · $479                                                   | **3.8** / 12.0 GB  | Q4FitsQ8FitsFP16Fits       | 94–134     |
| [GeForce RTX 5070](https://whatcanirunai.vercel.app/hardware/rtx-5070/)GPU · $549                                                      | **3.8** / 12.0 GB  | Q4FitsQ8FitsFP16Fits       | 165–234    |
| [Radeon RX 6750 XT](https://whatcanirunai.vercel.app/hardware/rx-6750-xt/)GPU · $549                                                   | **3.8** / 12.0 GB  | Q4FitsQ8FitsFP16Fits       | 106–151    |
| [GeForce RTX 4070](https://whatcanirunai.vercel.app/hardware/rtx-4070/)GPU · $599                                                      | **3.8** / 12.0 GB  | Q4FitsQ8FitsFP16Fits       | 124–176    |
| [GeForce RTX 4070 Super](https://whatcanirunai.vercel.app/hardware/rtx-4070-super/)GPU · $599                                          | **3.8** / 12.0 GB  | Q4FitsQ8FitsFP16Fits       | 124–176    |
| [Mac mini M4 16GB](https://whatcanirunai.vercel.app/hardware/mac-mini-m4-16/)unified memory · $789                                     | **3.8** / 12.0 GB  | Q4FitsQ8FitsFP16Fits       | 22–39      |
| [GeForce RTX 3080 12GB](https://whatcanirunai.vercel.app/hardware/rtx-3080-12gb/)GPU · $799                                            | **3.8** / 12.0 GB  | Q4FitsQ8FitsFP16Fits       | 224–318    |
| [GeForce RTX 4070 Ti](https://whatcanirunai.vercel.app/hardware/rtx-4070-ti/)GPU · $799                                                | **3.8** / 12.0 GB  | Q4FitsQ8FitsFP16Fits       | 124–176    |
| [Mac mini M6 16GB](https://whatcanirunai.vercel.app/hardware/mac-mini-m6-16/)unified memory · $899                                     | **3.8** / 12.0 GB  | Q4FitsQ8FitsFP16Fits       | 28–50      |
| [GeForce RTX 3080 Ti](https://whatcanirunai.vercel.app/hardware/rtx-3080-ti/)GPU · $1,199                                              | **3.8** / 12.0 GB  | Q4FitsQ8FitsFP16Fits       | 224–318    |
| [Radeon RX 7600 XT](https://whatcanirunai.vercel.app/hardware/rx-7600-xt/)GPU · $329                                                   | **3.8** / 16.0 GB  | Q4FitsQ8FitsFP16Fits       | 71–100     |
| [Radeon RX 9060 XT 16GB](https://whatcanirunai.vercel.app/hardware/rx-9060-xt-16gb/)GPU · $349                                         | **3.8** / 16.0 GB  | Q4FitsQ8FitsFP16Fits       | 79–112     |
| [Intel Arc A770 16GB](https://whatcanirunai.vercel.app/hardware/arc-a770-16gb/)GPU · $349                                              | **3.8** / 16.0 GB  | Q4FitsQ8FitsFP16Fits       | 138–195    |
| [GeForce RTX 5060 Ti 16GB](https://whatcanirunai.vercel.app/hardware/rtx-5060-ti-16gb/)GPU · $429                                      | **3.8** / 16.0 GB  | Q4FitsQ8FitsFP16Fits       | 110–156    |
| [GeForce RTX 4060 Ti 16GB](https://whatcanirunai.vercel.app/hardware/rtx-4060-ti-16gb/)GPU · $499                                      | **3.8** / 16.0 GB  | Q4FitsQ8FitsFP16Fits       | 71–100     |
| [Radeon RX 7800 XT](https://whatcanirunai.vercel.app/hardware/rx-7800-xt/)GPU · $499                                                   | **3.8** / 16.0 GB  | Q4FitsQ8FitsFP16Fits       | 154–217    |
| [Radeon RX 7900 GRE](https://whatcanirunai.vercel.app/hardware/rx-7900-gre/)GPU · $549                                                 | **3.8** / 16.0 GB  | Q4FitsQ8FitsFP16Fits       | 142–201    |
| [Radeon RX 9070](https://whatcanirunai.vercel.app/hardware/rx-9070/)GPU · $549                                                         | **3.8** / 16.0 GB  | Q4FitsQ8FitsFP16Fits       | 157–223    |
| [Radeon RX 6800](https://whatcanirunai.vercel.app/hardware/rx-6800/)GPU · $579                                                         | **3.8** / 16.0 GB  | Q4FitsQ8FitsFP16Fits       | 126–178    |
| [Radeon RX 9070 XT](https://whatcanirunai.vercel.app/hardware/rx-9070-xt/)GPU · $599                                                   | **3.8** / 16.0 GB  | Q4FitsQ8FitsFP16Fits       | 157–223    |
| [Radeon RX 6800 XT](https://whatcanirunai.vercel.app/hardware/rx-6800-xt/)GPU · $649                                                   | **3.8** / 16.0 GB  | Q4FitsQ8FitsFP16Fits       | 126–178    |
| [NVIDIA RTX 2000 Ada 16GB](https://whatcanirunai.vercel.app/hardware/rtx-2000-ada/)GPU · $649                                          | **3.8** / 16.0 GB  | Q4FitsQ8FitsFP16Fits       | 55–78      |
| [GeForce RTX 5070 Ti](https://whatcanirunai.vercel.app/hardware/rtx-5070-ti/)GPU · $749                                                | **3.8** / 16.0 GB  | Q4FitsQ8FitsFP16Fits       | 220–312    |
| [GeForce RTX 4070 Ti Super](https://whatcanirunai.vercel.app/hardware/rtx-4070-ti-super/)GPU · $799                                    | **3.8** / 16.0 GB  | Q4FitsQ8FitsFP16Fits       | 165–234    |
| [GeForce RTX 4080 Super](https://whatcanirunai.vercel.app/hardware/rtx-4080-super/)GPU · $999                                          | **3.8** / 16.0 GB  | Q4FitsQ8FitsFP16Fits       | 181–257    |
| [GeForce RTX 5080](https://whatcanirunai.vercel.app/hardware/rtx-5080/)GPU · $999                                                      | **3.8** / 16.0 GB  | Q4FitsQ8FitsFP16Fits       | 236–335    |
| [Radeon RX 6900 XT](https://whatcanirunai.vercel.app/hardware/rx-6900-xt/)GPU · $999                                                   | **3.8** / 16.0 GB  | Q4FitsQ8FitsFP16Fits       | 126–178    |
| [NVIDIA RTX A4000](https://whatcanirunai.vercel.app/hardware/rtx-a4000/)GPU · $1,000                                                   | **3.8** / 16.0 GB  | Q4FitsQ8FitsFP16Fits       | 110–156    |
| [Radeon RX 6950 XT](https://whatcanirunai.vercel.app/hardware/rx-6950-xt/)GPU · $1,099                                                 | **3.8** / 16.0 GB  | Q4FitsQ8FitsFP16Fits       | 142–201    |
| [GeForce RTX 4080](https://whatcanirunai.vercel.app/hardware/rtx-4080/)GPU · $1,199                                                    | **3.8** / 16.0 GB  | Q4FitsQ8FitsFP16Fits       | 176–250    |
| [Mac mini M6 24GB](https://whatcanirunai.vercel.app/hardware/mac-mini-m6-24/)unified memory · $1,299                                   | **3.8** / 18.0 GB  | Q4FitsQ8FitsFP16Fits       | 31–56      |
| [Radeon RX 7900 XT](https://whatcanirunai.vercel.app/hardware/rx-7900-xt/)GPU · $899                                                   | **3.8** / 20.0 GB  | Q4FitsQ8FitsFP16Fits       | 197–279    |
| [NVIDIA RTX 4000 Ada 20GB](https://whatcanirunai.vercel.app/hardware/rtx-4000-ada/)GPU · $1,250                                        | **3.8** / 20.0 GB  | Q4FitsQ8FitsFP16Fits       | 89–125     |
| [NVIDIA Tesla P40 24GB](https://whatcanirunai.vercel.app/hardware/tesla-p40/)GPU                                                       | **3.8** / 24.0 GB  | Q4FitsQ8FitsFP16Fits       | 85–121     |
| [Radeon RX 7900 XTX](https://whatcanirunai.vercel.app/hardware/rx-7900-xtx/)GPU · $999                                                 | **3.8** / 24.0 GB  | Q4FitsQ8FitsFP16Fits       | 236–335    |
| [GeForce RTX 3090](https://whatcanirunai.vercel.app/hardware/rtx-3090/)GPU · $1,499                                                    | **3.8** / 24.0 GB  | Q4FitsQ8FitsFP16Fits       | 230–326    |
| [GeForce RTX 4090](https://whatcanirunai.vercel.app/hardware/rtx-4090/)GPU · $1,599                                                    | **3.8** / 24.0 GB  | Q4FitsQ8FitsFP16Fits       | 248–351    |
| [Mac mini M6 32GB](https://whatcanirunai.vercel.app/hardware/mac-mini-m6-32/)unified memory · $1,799                                   | **3.8** / 24.0 GB  | Q4FitsQ8FitsFP16Fits       | 31–56      |
| [GeForce RTX 3090 Ti](https://whatcanirunai.vercel.app/hardware/rtx-3090-ti/)GPU · $1,999                                              | **3.8** / 24.0 GB  | Q4FitsQ8FitsFP16Fits       | 248–351    |
| [NVIDIA RTX A5000](https://whatcanirunai.vercel.app/hardware/rtx-a5000/)GPU · $2,250                                                   | **3.8** / 24.0 GB  | Q4FitsQ8FitsFP16Fits       | 189–268    |
| [MacBook Pro M6 32GB](https://whatcanirunai.vercel.app/hardware/macbook-pro-m6-32/)unified memory · $2,399                             | **3.8** / 24.0 GB  | Q4FitsQ8FitsFP16Fits       | 31–56      |
| [NVIDIA Titan RTX](https://whatcanirunai.vercel.app/hardware/titan-rtx/)GPU · $2,499                                                   | **3.8** / 24.0 GB  | Q4FitsQ8FitsFP16Fits       | 165–234    |
| [NVIDIA Tesla V100 32GB (PCIe)](https://whatcanirunai.vercel.app/hardware/tesla-v100-32gb/)GPU                                         | **3.8** / 32.0 GB  | Q4FitsQ8FitsFP16Fits       | 221–314    |
| [AMD Radeon Instinct MI50 32GB](https://whatcanirunai.vercel.app/hardware/instinct-mi50-32gb/)GPU                                      | **3.8** / 32.0 GB  | Q4FitsQ8FitsFP16Fits       | 252–357    |
| [AMD Radeon Instinct MI60 32GB](https://whatcanirunai.vercel.app/hardware/instinct-mi60/)GPU                                           | **3.8** / 32.0 GB  | Q4FitsQ8FitsFP16Fits       | 252–357    |
| [Intel Arc Pro B70 32GB](https://whatcanirunai.vercel.app/hardware/arc-pro-b70/)GPU · $949                                             | **3.8** / 32.0 GB  | Q4FitsQ8FitsFP16Fits       | 150–212    |
| [GeForce RTX 5090](https://whatcanirunai.vercel.app/hardware/rtx-5090/)GPU · $1,999                                                    | **3.8** / 32.0 GB  | Q4FitsQ8FitsFP16Fits       | 441–625    |
| [Mac mini M5 Pro 64GB](https://whatcanirunai.vercel.app/hardware/mac-mini-m5-pro-64/)unified memory · $3,199                           | **3.8** / 48.0 GB  | Q4FitsQ8FitsFP16Fits       | 57–101     |
| [NVIDIA RTX A6000](https://whatcanirunai.vercel.app/hardware/rtx-a6000/)GPU · $4,649                                                   | **3.8** / 48.0 GB  | Q4FitsQ8FitsFP16Fits       | 189–268    |
| [NVIDIA DGX Spark 64GB](https://whatcanirunai.vercel.app/hardware/dgx-spark-64/)unified memory · $4,999                                | **3.8** / 48.0 GB  | Q4FitsQ8FitsFP16Fits       | 50–90      |
| [NVIDIA RTX 6000 Ada 48GB](https://whatcanirunai.vercel.app/hardware/rtx-6000-ada/)GPU · $6,799                                        | **3.8** / 48.0 GB  | Q4FitsQ8FitsFP16Fits       | 236–335    |
| [Framework Desktop (Ryzen AI Max+ 395) 128GB](https://whatcanirunai.vercel.app/hardware/framework-desktop-128/)unified memory · $3,449 | **3.8** / 96.0 GB  | Q4FitsQ8FitsFP16Fits       | 47–84      |
| [MacBook Pro M4 Max 128GB](https://whatcanirunai.vercel.app/hardware/macbook-pro-m4-max-128/)unified memory · $4,999                   | **3.8** / 96.0 GB  | Q4FitsQ8FitsFP16Fits       | 101–179    |
| [MacBook Pro M5 Max 128GB](https://whatcanirunai.vercel.app/hardware/macbook-pro-m5-max-128/)unified memory · $6,699                   | **3.8** / 96.0 GB  | Q4FitsQ8FitsFP16Fits       | 113–201    |
| [NVIDIA DGX Spark 128GB](https://whatcanirunai.vercel.app/hardware/dgx-spark-128/)unified memory · $6,950                              | **3.8** / 96.0 GB  | Q4FitsQ8FitsFP16Fits       | 50–90      |
| [NVIDIA RTX Pro 6000 Blackwell 96GB](https://whatcanirunai.vercel.app/hardware/rtx-pro-6000-blackwell/)GPU · $8,565                    | **3.8** / 96.0 GB  | Q4FitsQ8FitsFP16Fits       | 441–625    |
| [Mac Studio M5 Ultra 256GB](https://whatcanirunai.vercel.app/hardware/mac-studio-m5-ultra-256/)unified memory · $9,499                 | **3.8** / 192.0 GB | Q4FitsQ8FitsFP16Fits       | 221–394    |
| [Mac Studio M5 Ultra 512GB](https://whatcanirunai.vercel.app/hardware/mac-studio-m5-ultra-512/)unified memory · $22,299                | **3.8** / 384.0 GB | Q4FitsQ8FitsFP16Fits       | 221–394    |

## 4B–6B models we track

__Memory needed by each 4B–6B model we track__
| Model                                                                                                              | Q4\_K\_M | Q8\_0  | FP16    |
| ------------------------------------------------------------------------------------------------------------------ | -------- | ------ | ------- |
| [kanana-2-3b-instruct](https://whatcanirunai.vercel.app/models/kanana-2-3b-instruct/)3.5B params · kakaocorp                                       | 3.4 GB   | 5.3 GB | 9.1 GB  |
| [granite-4.2-3b](https://whatcanirunai.vercel.app/models/granite-4.2-3b/)3.7B params · ibm-granite                                                 | 3.6 GB   | 5.5 GB | 9.4 GB  |
| [kanana-1.5-v-3b-instruct](https://whatcanirunai.vercel.app/models/kanana-1.5-v-3b-instruct/)3.7B params · kakaocorp                               | 3.6 GB   | 5.5 GB | 9.4 GB  |
| [HyperCLOVAX-SEED-Vision-Instruct-3B](https://whatcanirunai.vercel.app/models/hyperclovax-seed-vision-instruct-3b/)3.7B params · naver-hyperclovax | 3.6 GB   | 5.5 GB | 9.6 GB  |
| [CapRL-3B](https://whatcanirunai.vercel.app/models/caprl-3b/)3.8B params · internlm                                                                | 3.6 GB   | 5.6 GB | 9.6 GB  |
| [Phi-tiny-MoE-instruct](https://whatcanirunai.vercel.app/models/phi-tiny-moe-instruct/)3.8B params · microsoft · MoE                               | 3.6 GB   | 5.6 GB | 9.6 GB  |
| [HY-Embodied-0.5](https://whatcanirunai.vercel.app/models/hy-embodied-0.5/)3.8B params · tencent                                                   | 3.6 GB   | 5.6 GB | 9.7 GB  |
| [Phi-3.5-mini-instruct](https://whatcanirunai.vercel.app/models/phi-3.5-mini-instruct/)3.8B params · microsoft                                     | 3.7 GB   | 5.7 GB | 9.8 GB  |
| [MediPhi-Instruct](https://whatcanirunai.vercel.app/models/mediphi-instruct/)3.8B params · microsoft                                               | 3.7 GB   | 5.7 GB | 9.8 GB  |
| [MediPhi](https://whatcanirunai.vercel.app/models/mediphi/)3.8B params · microsoft                                                                 | 3.7 GB   | 5.7 GB | 9.8 GB  |
| [MediPhi-Clinical](https://whatcanirunai.vercel.app/models/mediphi-clinical/)3.8B params · microsoft                                               | 3.7 GB   | 5.7 GB | 9.8 GB  |
| [MediPhi-PubMed](https://whatcanirunai.vercel.app/models/mediphi-pubmed/)3.8B params · microsoft                                                   | 3.7 GB   | 5.7 GB | 9.8 GB  |
| [Nemotron-Labs-Diffusion-3B](https://whatcanirunai.vercel.app/models/nemotron-labs-diffusion-3b/)3.8B params · nvidia                              | 3.7 GB   | 5.7 GB | 9.8 GB  |
| [Phi-4-mini-instruct](https://whatcanirunai.vercel.app/models/phi-4-mini-instruct/)3.8B params · microsoft                                         | 3.7 GB   | 5.7 GB | 9.8 GB  |
| [Phi-4-mini-reasoning](https://whatcanirunai.vercel.app/models/phi-4-mini-reasoning/)3.8B params · microsoft                                       | 3.7 GB   | 5.7 GB | 9.8 GB  |
| [Phi-4-mini-flash-reasoning](https://whatcanirunai.vercel.app/models/phi-4-mini-flash-reasoning/)3.9B params · microsoft                           | 3.7 GB   | 5.7 GB | 9.9 GB  |
| [Nanbeige4.1-3B](https://whatcanirunai.vercel.app/models/nanbeige4.1-3b/)3.9B params · Nanbeige                                                    | 3.7 GB   | 5.8 GB | 10.0 GB |
| [Nanbeige4-3B-Thinking-2511](https://whatcanirunai.vercel.app/models/nanbeige4-3b-thinking-2511/)3.9B params · Nanbeige                            | 3.7 GB   | 5.8 GB | 10.0 GB |
| [NVIDIA-Nemotron-3-Nano-4B-BF16](https://whatcanirunai.vercel.app/models/nvidia-nemotron-3-nano-4b-bf16/)4.0B params · nvidia                      | 3.8 GB   | 5.9 GB | 10.1 GB |
| [Nemotron Labs Dory 1.0 4B](https://whatcanirunai.vercel.app/models/nemotron-labs-dory1.0-4b/)4.0B params · nvidia                                 | 3.8 GB   | 5.9 GB | 10.1 GB |
| [granite-vision-4.1-4b](https://whatcanirunai.vercel.app/models/granite-vision-4.1-4b/)4.0B params · ibm-granite                                   | 3.8 GB   | 5.9 GB | 10.2 GB |
| [granite-4.0-3b-vision](https://whatcanirunai.vercel.app/models/granite-4.0-3b-vision/)4.0B params · ibm-granite                                   | 3.8 GB   | 5.9 GB | 10.2 GB |
| [Mellum-4b-base](https://whatcanirunai.vercel.app/models/mellum-4b-base/)4.0B params · JetBrains                                                   | 3.8 GB   | 5.9 GB | 10.2 GB |
| [Qwen3-4B](https://whatcanirunai.vercel.app/models/qwen3-4b/)4.0B params · Qwen                                                                    | 3.8 GB   | 5.9 GB | 10.3 GB |
| [Qwen3-4B-Instruct-2507](https://whatcanirunai.vercel.app/models/qwen3-4b-instruct-2507/)4.0B params · Qwen                                        | 3.8 GB   | 5.9 GB | 10.3 GB |
| [Qwen3-4B-Thinking-2507](https://whatcanirunai.vercel.app/models/qwen3-4b-thinking-2507/)4.0B params · Qwen                                        | 3.8 GB   | 5.9 GB | 10.3 GB |
| [MiniCPM-V-4](https://whatcanirunai.vercel.app/models/minicpm-v-4/)4.1B params · openbmb                                                           | 3.8 GB   | 6.0 GB | 10.3 GB |
| [POINTS-Reader](https://whatcanirunai.vercel.app/models/points-reader/)4.1B params · tencent                                                       | 3.9 GB   | 6.0 GB | 10.5 GB |
| [Spark-X2.6-4B](https://whatcanirunai.vercel.app/models/spark-x2.6-4b/)4.1B params · XHToken                                                       | 3.9 GB   | 6.0 GB | 10.5 GB |
| [Spark-X2.5-4B](https://whatcanirunai.vercel.app/models/spark-x2.5-4b/)4.1B params · XHToken                                                       | 3.9 GB   | 6.0 GB | 10.5 GB |
| [Phi-3.5-vision-instruct](https://whatcanirunai.vercel.app/models/phi-3.5-vision-instruct/)4.1B params · microsoft                                 | 3.9 GB   | 6.1 GB | 10.5 GB |
| [Nanbeige4.2-3B](https://whatcanirunai.vercel.app/models/nanbeige4.2-3b/)4.2B params · Nanbeige                                                    | 3.9 GB   | 6.1 GB | 10.6 GB |
| [gemma-3-4b-it](https://whatcanirunai.vercel.app/models/gemma-3-4b-it/)4.3B params · google                                                        | 4.0 GB   | 6.3 GB | 10.9 GB |
| [medgemma-4b-it](https://whatcanirunai.vercel.app/models/medgemma-4b-it/)4.3B params · google                                                      | 4.0 GB   | 6.3 GB | 10.9 GB |
| [medgemma-1.5-4b-it](https://whatcanirunai.vercel.app/models/medgemma-1.5-4b-it/)4.3B params · google                                              | 4.0 GB   | 6.3 GB | 10.9 GB |
| [gemma-3-4b-pt](https://whatcanirunai.vercel.app/models/gemma-3-4b-pt/)4.3B params · google                                                        | 4.0 GB   | 6.3 GB | 10.9 GB |
| [glm-edge-4b-chat](https://whatcanirunai.vercel.app/models/glm-edge-4b-chat/)4.3B params · zai-org                                                 | 4.0 GB   | 6.3 GB | 11.0 GB |
| [Qwen3-VL-4B-Instruct](https://whatcanirunai.vercel.app/models/qwen3-vl-4b-instruct/)4.4B params · Qwen                                            | 4.1 GB   | 6.4 GB | 11.2 GB |
| [CapRL-Qwen3VL-4B](https://whatcanirunai.vercel.app/models/caprl-qwen3vl-4b/)4.4B params · internlm                                                | 4.1 GB   | 6.4 GB | 11.2 GB |
| [Llama-3.1-Minitron-4B-Width-Base](https://whatcanirunai.vercel.app/models/llama-3.1-minitron-4b-width-base/)4.5B params · nvidia                  | 4.1 GB   | 6.5 GB | 11.4 GB |
| [AFM-4.5B](https://whatcanirunai.vercel.app/models/afm-4.5b/)4.6B params · arcee-ai                                                                | 4.2 GB   | 6.6 GB | 11.6 GB |
| [Qwen3.5-4B](https://whatcanirunai.vercel.app/models/qwen3.5-4b/)4.7B params · Qwen                                                                | 4.2 GB   | 6.7 GB | 11.7 GB |
| [S5-nano](https://whatcanirunai.vercel.app/models/s5-nano/)4.7B params · maritaca-ai                                                               | 4.2 GB   | 6.7 GB | 11.7 GB |
| [Mage-VL](https://whatcanirunai.vercel.app/models/mage-vl/)4.7B params · microsoft                                                                 | 4.3 GB   | 6.8 GB | 11.9 GB |
| [Mage-VL-Cowork](https://whatcanirunai.vercel.app/models/mage-vl-cowork/)4.7B params · microsoft                                                   | 4.3 GB   | 6.8 GB | 11.9 GB |
| [WeVisOne-VL-4B](https://whatcanirunai.vercel.app/models/wevisone-vl-4b/)4.8B params · tencent                                                     | 4.4 GB   | 6.9 GB | 12.1 GB |
| [Apriel-5B-Instruct](https://whatcanirunai.vercel.app/models/apriel-5b-instruct/)4.8B params · ServiceNow-AI                                       | 4.4 GB   | 6.9 GB | 12.1 GB |
| [Molmo2-ER](https://whatcanirunai.vercel.app/models/molmo2-er/)4.9B params · allenai                                                               | 4.4 GB   | 6.9 GB | 12.2 GB |
| [Gemma 4 E2B](https://whatcanirunai.vercel.app/models/gemma-4-e2b-it/)5.1B params · google                                                         | 4.6 GB   | 7.3 GB | 12.8 GB |
| [Youtu-VL-4B-Instruct](https://whatcanirunai.vercel.app/models/youtu-vl-4b-instruct/)5.3B params · tencent                                         | 4.7 GB   | 7.5 GB | 13.3 GB |
| [gemma-3n-E2B-it](https://whatcanirunai.vercel.app/models/gemma-3n-e2b-it/)5.4B params · google                                                    | 4.8 GB   | 7.6 GB | 13.5 GB |
| [Trinity-Nano-Preview](https://whatcanirunai.vercel.app/models/trinity-nano-preview/)6.1B params · arcee-ai · MoE                                  | 5.3 GB   | 8.5 GB | 15.1 GB |
| [Trinity-Nano-Base](https://whatcanirunai.vercel.app/models/trinity-nano-base/)6.1B params · arcee-ai · MoE                                        | 5.3 GB   | 8.5 GB | 15.1 GB |
| [YuE-s1-7B-anneal-en-cot](https://whatcanirunai.vercel.app/models/yue-s1-7b-anneal-en-cot/)6.2B params · m-a-p                                     | 5.3 GB   | 8.6 GB | 15.3 GB |

Have a different amount of memory? Try the [VRAM calculator](https://whatcanirunai.vercel.app/vram-calculator/) or see [best LLMs by VRAM size](https://whatcanirunai.vercel.app/best-llm-for/).

## Quick answers

How much VRAM do I need for a 4B–6B model?

About 3.8 GB at Q4\_K\_M, 5.9 GB at Q8\_0 and 10.3 GB at FP16, at a \~4k context. That is for Qwen3-4B (4.0B parameters): weights plus about 15% for the KV cache and 1 GB for the runtime.

How much VRAM does a 4B–6B model need for 32k context?

About 8.0 GB at Q4\_K\_M with an FP16 KV cache, or 5.6 GB with a Q8 cache, based on Qwen3-4B's published config. Models with different attention layouts need more or less.

What is the cheapest GPU for a 4B–6B 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 4B–6B 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/) · [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/)

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