# How much VRAM do you need for a 24B–27B model?

> A 24B–27B LLM needs about 20.4 GB of VRAM at Q4, 34.9 GB at Q8 and 64.9 GB at FP16. Context-length table and the cheapest GPU and Mac that fit.

Source: https://whatcanirunai.vercel.app/how-much-vram/27b/

# How much VRAM do you need for a 24B–27B model?

Short answer: about **20.4 GB** at Q4\_K\_M, the usual quant for local use. Below: every quant, longer contexts, the cheapest hardware that fits, and every 24B–27B open model we track.

Q4\_K\_M

20.4 GB 16.8 GB weights

Q8\_0

34.9 GB 29.5 GB weights

FP16

64.9 GB 55.6 GB weights

Worked out for [Qwen3.8-27B](https://whatcanirunai.vercel.app/models/qwen3.8-27b/) (27.8B parameters, from the Hugging Face API). Formula: weights × 1.15 + 1 GB at a \~4k context ([how we calculate](https://whatcanirunai.vercel.app/method/)).

Cheapest GPU that fits at Q4: **Intel Arc Pro B70 32GB** (32 GB, $949). Cheapest Mac or mini PC: **Mac mini M6 32GB** ($1,799).

## 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 24B–27B model at each quant__
| Setting                | Memory needed | Cheapest GPU                                                                                  | Cheapest Mac / mini PC                                                                                |
| ---------------------- | ------------- | --------------------------------------------------------------------------------------------- | ----------------------------------------------------------------------------------------------------- |
| Q4\_K\_M, \~4k context | 20.4 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                                   |
| Q8\_0, \~4k context    | 34.9 GB       | [NVIDIA RTX A6000](https://whatcanirunai.vercel.app/hardware/rtx-a6000/) 48 GB usable · $4,649                                | [Mac mini M5 Pro 64GB](https://whatcanirunai.vercel.app/hardware/mac-mini-m5-pro-64/) 48 GB usable · $3,199                           |
| FP16, \~4k context     | 64.9 GB       | [NVIDIA RTX Pro 6000 Blackwell 96GB](https://whatcanirunai.vercel.app/hardware/rtx-pro-6000-blackwell/) 96 GB usable · $8,565 | [Framework Desktop (Ryzen AI Max+ 395) 128GB](https://whatcanirunai.vercel.app/hardware/framework-desktop-128/) 96 GB usable · $3,449 |
| Q4\_K\_M, 32k context  | 22.2 GB       | [Intel Arc Pro B70 32GB](https://whatcanirunai.vercel.app/hardware/arc-pro-b70/) 32 GB usable · $949                          | [Mac mini M5 Pro 64GB](https://whatcanirunai.vercel.app/hardware/mac-mini-m5-pro-64/) 48 GB usable · $3,199                           |

## Memory by context length

Each extra token of context adds to the KV cache. Numbers for Qwen3.8-27B, from its published config; a Q8 KV cache halves the cache part.

__Memory needed for a 24B–27B model by context length__
| Context     | Q4\_K\_M, FP16 cache | Q4\_K\_M, Q8 cache | Q8\_0, FP16 cache |
| ----------- | -------------------- | ------------------ | ----------------- |
| 4k tokens   | 20.4 GB              | 20.4 GB            | 34.9 GB           |
| 8k tokens   | 20.6 GB              | 20.4 GB            | 35.2 GB           |
| 16k tokens  | 21.2 GB              | 20.6 GB            | 35.8 GB           |
| 32k tokens  | 22.2 GB              | 21.2 GB            | 36.8 GB           |
| 64k tokens  | 24.4 GB              | 22.2 GB            | 39.0 GB           |
| 128k tokens | 28.7 GB              | 24.4 GB            | 43.3 GB           |

## Hardware that runs it

Bar: Q4 memory needed vs. usable memory. Speed is estimated decode at Q4 ([how we estimate](https://whatcanirunai.vercel.app/method/#speed)).FitsTight

__Tracked hardware that runs a 24B–27B model at Q4__
| Hardware                                                                                               | Q4 need vs. usable  | Q4 / Q8 / FP16                 | Est. tok/s |
| ------------------------------------------------------------------------------------------------------ | ------------------- | ------------------------------ | ---------- |
| [NVIDIA Tesla P40 24GB](https://whatcanirunai.vercel.app/hardware/tesla-p40/)GPU                                                       | **20.4** / 24.0 GB  | Q4FitsQ8Won’t fitFP16Won’t fit | 12–17      |
| [Radeon RX 7900 XTX](https://whatcanirunai.vercel.app/hardware/rx-7900-xtx/)GPU · $999                                                 | **20.4** / 24.0 GB  | Q4FitsQ8Won’t fitFP16Won’t fit | 34–48      |
| [GeForce RTX 3090](https://whatcanirunai.vercel.app/hardware/rtx-3090/)GPU · $1,499                                                    | **20.4** / 24.0 GB  | Q4FitsQ8Won’t fitFP16Won’t fit | 33–47      |
| [GeForce RTX 4090](https://whatcanirunai.vercel.app/hardware/rtx-4090/)GPU · $1,599                                                    | **20.4** / 24.0 GB  | Q4FitsQ8Won’t fitFP16Won’t fit | 36–51      |
| [Mac mini M6 32GB](https://whatcanirunai.vercel.app/hardware/mac-mini-m6-32/)unified memory · $1,799                                   | **20.4** / 24.0 GB  | Q4FitsQ8Won’t fitFP16Won’t fit | 5–8        |
| [GeForce RTX 3090 Ti](https://whatcanirunai.vercel.app/hardware/rtx-3090-ti/)GPU · $1,999                                              | **20.4** / 24.0 GB  | Q4FitsQ8Won’t fitFP16Won’t fit | 36–51      |
| [NVIDIA RTX A5000](https://whatcanirunai.vercel.app/hardware/rtx-a5000/)GPU · $2,250                                                   | **20.4** / 24.0 GB  | Q4FitsQ8Won’t fitFP16Won’t fit | 27–39      |
| [MacBook Pro M6 32GB](https://whatcanirunai.vercel.app/hardware/macbook-pro-m6-32/)unified memory · $2,399                             | **20.4** / 24.0 GB  | Q4FitsQ8Won’t fitFP16Won’t fit | 5–8        |
| [NVIDIA Titan RTX](https://whatcanirunai.vercel.app/hardware/titan-rtx/)GPU · $2,499                                                   | **20.4** / 24.0 GB  | Q4FitsQ8Won’t fitFP16Won’t fit | 24–34      |
| [NVIDIA Tesla V100 32GB (PCIe)](https://whatcanirunai.vercel.app/hardware/tesla-v100-32gb/)GPU                                         | **20.4** / 32.0 GB  | Q4FitsQ8Won’t fitFP16Won’t fit | 32–45      |
| [AMD Radeon Instinct MI50 32GB](https://whatcanirunai.vercel.app/hardware/instinct-mi50-32gb/)GPU                                      | **20.4** / 32.0 GB  | Q4FitsQ8Won’t fitFP16Won’t fit | 36–52      |
| [AMD Radeon Instinct MI60 32GB](https://whatcanirunai.vercel.app/hardware/instinct-mi60/)GPU                                           | **20.4** / 32.0 GB  | Q4FitsQ8Won’t fitFP16Won’t fit | 36–52      |
| [Intel Arc Pro B70 32GB](https://whatcanirunai.vercel.app/hardware/arc-pro-b70/)GPU · $949                                             | **20.4** / 32.0 GB  | Q4FitsQ8Won’t fitFP16Won’t fit | 22–31      |
| [GeForce RTX 5090](https://whatcanirunai.vercel.app/hardware/rtx-5090/)GPU · $1,999                                                    | **20.4** / 32.0 GB  | Q4FitsQ8Won’t fitFP16Won’t fit | 64–90      |
| [Mac mini M5 Pro 64GB](https://whatcanirunai.vercel.app/hardware/mac-mini-m5-pro-64/)unified memory · $3,199                           | **20.4** / 48.0 GB  | Q4FitsQ8FitsFP16Won’t fit      | 8–15       |
| [NVIDIA RTX A6000](https://whatcanirunai.vercel.app/hardware/rtx-a6000/)GPU · $4,649                                                   | **20.4** / 48.0 GB  | Q4FitsQ8FitsFP16Won’t fit      | 27–39      |
| [NVIDIA DGX Spark 64GB](https://whatcanirunai.vercel.app/hardware/dgx-spark-64/)unified memory · $4,999                                | **20.4** / 48.0 GB  | Q4FitsQ8FitsFP16Won’t fit      | 7–13       |
| [NVIDIA RTX 6000 Ada 48GB](https://whatcanirunai.vercel.app/hardware/rtx-6000-ada/)GPU · $6,799                                        | **20.4** / 48.0 GB  | Q4FitsQ8FitsFP16Won’t fit      | 34–48      |
| [Framework Desktop (Ryzen AI Max+ 395) 128GB](https://whatcanirunai.vercel.app/hardware/framework-desktop-128/)unified memory · $3,449 | **20.4** / 96.0 GB  | Q4FitsQ8FitsFP16Fits           | 7–12       |
| [MacBook Pro M4 Max 128GB](https://whatcanirunai.vercel.app/hardware/macbook-pro-m4-max-128/)unified memory · $4,999                   | **20.4** / 96.0 GB  | Q4FitsQ8FitsFP16Fits           | 15–26      |
| [MacBook Pro M5 Max 128GB](https://whatcanirunai.vercel.app/hardware/macbook-pro-m5-max-128/)unified memory · $6,699                   | **20.4** / 96.0 GB  | Q4FitsQ8FitsFP16Fits           | 16–29      |
| [NVIDIA DGX Spark 128GB](https://whatcanirunai.vercel.app/hardware/dgx-spark-128/)unified memory · $6,950                              | **20.4** / 96.0 GB  | Q4FitsQ8FitsFP16Fits           | 7–13       |
| [NVIDIA RTX Pro 6000 Blackwell 96GB](https://whatcanirunai.vercel.app/hardware/rtx-pro-6000-blackwell/)GPU · $8,565                    | **20.4** / 96.0 GB  | Q4FitsQ8FitsFP16Fits           | 64–90      |
| [Mac Studio M5 Ultra 256GB](https://whatcanirunai.vercel.app/hardware/mac-studio-m5-ultra-256/)unified memory · $9,499                 | **20.4** / 192.0 GB | Q4FitsQ8FitsFP16Fits           | 32–57      |
| [Mac Studio M5 Ultra 512GB](https://whatcanirunai.vercel.app/hardware/mac-studio-m5-ultra-512/)unified memory · $22,299                | **20.4** / 384.0 GB | Q4FitsQ8FitsFP16Fits           | 32–57      |

## 24B–27B models we track

__Memory needed by each 24B–27B model we track__
| Model                                                                                                       | Q4\_K\_M | Q8\_0   | FP16    |
| ----------------------------------------------------------------------------------------------------------- | -------- | ------- | ------- |
| [solar-pro-preview-instruct](https://whatcanirunai.vercel.app/models/solar-pro-preview-instruct/)22.1B params · upstage                     | 16.4 GB  | 28.1 GB | 51.9 GB |
| [EuroLLM-22B-Instruct-2512](https://whatcanirunai.vercel.app/models/eurollm-22b-instruct-2512/)22.6B params · utter-project                 | 16.8 GB  | 28.7 GB | 53.1 GB |
| [EuroLLM-22B-2512](https://whatcanirunai.vercel.app/models/eurollm-22b-2512/)22.6B params · utter-project                                   | 16.8 GB  | 28.7 GB | 53.1 GB |
| [Mistral-Small-24B-Instruct-2501](https://whatcanirunai.vercel.app/models/mistral-small-24b-instruct-2501/)23.6B params · mistralai         | 17.4 GB  | 29.8 GB | 55.2 GB |
| [LFM2-24B-A2B](https://whatcanirunai.vercel.app/models/lfm2-24b-a2b/)23.8B params · LiquidAI · MoE                                          | 17.6 GB  | 30.1 GB | 55.8 GB |
| [LFM2.5-24B-A2B](https://whatcanirunai.vercel.app/models/lfm2.5-24b-a2b/)24.0B params · LiquidAI · MoE                                      | 17.7 GB  | 30.3 GB | 56.1 GB |
| [Mistral-Small-3.2-24B-Instruct-2506](https://whatcanirunai.vercel.app/models/mistral-small-3.2-24b-instruct-2506/)24.0B params · mistralai | 17.7 GB  | 30.3 GB | 56.2 GB |
| [Mistral-Small-3.1-24B-Instruct-2503](https://whatcanirunai.vercel.app/models/mistral-small-3.1-24b-instruct-2503/)24.0B params · mistralai | 17.7 GB  | 30.3 GB | 56.2 GB |
| [Voxtral-Small-24B-2507](https://whatcanirunai.vercel.app/models/voxtral-small-24b-2507/)24.3B params · mistralai                           | 17.9 GB  | 30.6 GB | 56.8 GB |
| [Gemma 4 26B A4B](https://whatcanirunai.vercel.app/models/gemma-4-26b-a4b-it/)25.8B params · google · MoE                                   | 19.0 GB  | 32.5 GB | 60.4 GB |
| [gemma-4-26B-A4B](https://whatcanirunai.vercel.app/models/gemma-4-26b-a4b/)25.8B params · google · MoE                                      | 19.0 GB  | 32.5 GB | 60.4 GB |
| [diffusiongemma-26B-A4B-it](https://whatcanirunai.vercel.app/models/diffusiongemma-26b-a4b-it/)25.8B params · google · MoE                  | 19.0 GB  | 32.6 GB | 60.4 GB |
| [Trinity-Mini](https://whatcanirunai.vercel.app/models/trinity-mini/)26.1B params · arcee-ai · MoE                                          | 19.2 GB  | 32.9 GB | 61.1 GB |
| [Hemmingway-1.5](https://whatcanirunai.vercel.app/models/hemmingway-1.5/)26.9B params · HemmingwayAI                                        | 19.8 GB  | 33.9 GB | 62.9 GB |
| [medgemma-27b-text-it](https://whatcanirunai.vercel.app/models/medgemma-27b-text-it/)27.0B params · google                                  | 19.8 GB  | 34.0 GB | 63.1 GB |
| [Qwen3.8-27B-Longbook27-v2](https://whatcanirunai.vercel.app/models/qwen3.8-27b-longbook27-v2/)27.1B params · summerAIO                     | 19.9 GB  | 34.1 GB | 63.3 GB |
| [gemma-2-27b-it](https://whatcanirunai.vercel.app/models/gemma-2-27b-it/)27.2B params · google                                              | 20.0 GB  | 34.3 GB | 63.6 GB |
| [gemma-2-27b](https://whatcanirunai.vercel.app/models/gemma-2-27b/)27.2B params · google                                                    | 20.0 GB  | 34.3 GB | 63.6 GB |
| [Arete-27B](https://whatcanirunai.vercel.app/models/arete-27b/)27.4B params · NousResearch                                                  | 20.1 GB  | 34.4 GB | 63.9 GB |
| [gemma-3-27b-it](https://whatcanirunai.vercel.app/models/gemma-3-27b-it/)27.4B params · google                                              | 20.1 GB  | 34.5 GB | 64.1 GB |
| [Qwen3.8-27B](https://whatcanirunai.vercel.app/models/qwen3.8-27b/)27.8B params · Qwen                                                      | 20.4 GB  | 34.9 GB | 64.9 GB |
| [Tess-4.5-27B](https://whatcanirunai.vercel.app/models/tess-4.5-27b/)27.8B params · migtissera                                              | 20.4 GB  | 34.9 GB | 64.9 GB |
| [Qwen3.6-27B](https://whatcanirunai.vercel.app/models/qwen3.6-27b/)27.8B params · Qwen                                                      | 20.4 GB  | 34.9 GB | 64.9 GB |
| [Qwen3.5-27B](https://whatcanirunai.vercel.app/models/qwen3.5-27b/)27.8B params · Qwen                                                      | 20.4 GB  | 34.9 GB | 64.9 GB |
| [Raon-1 mini preview](https://whatcanirunai.vercel.app/models/raon-1-mini-preview/)28.8B params · KRAFTON · MoE                             | 21.1 GB  | 36.2 GB | 67.2 GB |
| [granite-4.1-30b](https://whatcanirunai.vercel.app/models/granite-4.1-30b/)28.9B params · ibm-granite                                       | 21.1 GB  | 36.3 GB | 67.4 GB |
| [granite-4.2-30b](https://whatcanirunai.vercel.app/models/granite-4.2-30b/)29.3B params · ibm-granite                                       | 21.4 GB  | 36.8 GB | 68.3 GB |
| [ERNIE-4.5-VL-28B-A3B-PT](https://whatcanirunai.vercel.app/models/ernie-4.5-vl-28b-a3b-pt/)29.4B params · baidu · MoE                       | 21.5 GB  | 36.9 GB | 68.6 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 24B–27B model?

About 20.4 GB at Q4\_K\_M, 34.9 GB at Q8\_0 and 64.9 GB at FP16, at a \~4k context. That is for Qwen3.8-27B (27.8B parameters): weights plus about 15% for the KV cache and 1 GB for the runtime.

How much VRAM does a 24B–27B model need for 32k context?

About 22.2 GB at Q4\_K\_M with an FP16 KV cache, or 21.2 GB with a Q8 cache, based on Qwen3.8-27B's published config. Models with different attention layouts need more or less.

What is the cheapest GPU for a 24B–27B model?

Of the GPUs we track, the Intel Arc Pro B70 32GB (32 GB, $949 list price) is the cheapest that runs it fully on the GPU at Q4.

Can a Mac run a 24B–27B model?

Yes. The cheapest Mac or unified-memory mini PC we track that runs it at Q4 is the Mac mini M6 32GB (32 GB, about 24.0 GB usable, $1,799).

Does a MoE model of the same size need less VRAM?

No. A mixture-of-experts model still has to hold all of its weights in memory, so it needs about the same VRAM as a dense model with the same total parameters. It runs faster, because only a few experts are read per token.

Other sizes: [1B–2B](https://whatcanirunai.vercel.app/how-much-vram/1b/) · [2B–3B](https://whatcanirunai.vercel.app/how-much-vram/3b/) · [4B–6B](https://whatcanirunai.vercel.app/how-much-vram/4b/) · [7B–8B](https://whatcanirunai.vercel.app/how-much-vram/8b/) · [12B–14B](https://whatcanirunai.vercel.app/how-much-vram/14b/) · [16B–21B](https://whatcanirunai.vercel.app/how-much-vram/20b/) · [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/