# How much VRAM do you need for a 200B–300B model?

> A 200B–300B LLM needs about 164.9 GB of VRAM at Q4, 288.3 GB at Q8 and 541.7 GB at FP16. Context-length table and the cheapest GPU and Mac that fit.

Source: https://whatcanirunai.vercel.app/how-much-vram/235b/

# How much VRAM do you need for a 200B–300B model?

Short answer: about **164.9 GB** at Q4\_K\_M, the usual quant for local use. Below: every quant, longer contexts, the cheapest hardware that fits, and every 200B–300B open model we track.

Q4\_K\_M

164.9 GB 142.5 GB weights

Q8\_0

288.3 GB 249.8 GB weights

FP16

541.7 GB 470.2 GB weights

Worked out for [Qwen3-235B-A22B](https://whatcanirunai.vercel.app/models/qwen3-235b-a22b/) (235.1B parameters, from the Hugging Face API). Formula: weights × 1.15 + 1 GB at a \~4k context ([how we calculate](https://whatcanirunai.vercel.app/method/)).

No single consumer GPU we track fits it at Q4\. Cheapest Mac or mini PC: **Mac Studio M5 Ultra 256GB** ($9,499).

## 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 200B–300B model at each quant__
| Setting                | Memory needed | Cheapest GPU  | Cheapest Mac / mini PC                                                                  |
| ---------------------- | ------------- | ------------- | --------------------------------------------------------------------------------------- |
| Q4\_K\_M, \~4k context | 164.9 GB      | None we track | [Mac Studio M5 Ultra 256GB](https://whatcanirunai.vercel.app/hardware/mac-studio-m5-ultra-256/) 192 GB usable · $9,499  |
| Q8\_0, \~4k context    | 288.3 GB      | None we track | [Mac Studio M5 Ultra 512GB](https://whatcanirunai.vercel.app/hardware/mac-studio-m5-ultra-512/) 384 GB usable · $22,299 |
| FP16, \~4k context     | 541.7 GB      | None we track | None we track                                                                           |
| Q4\_K\_M, 32k context  | 170.4 GB      | None we track | [Mac Studio M5 Ultra 256GB](https://whatcanirunai.vercel.app/hardware/mac-studio-m5-ultra-256/) 192 GB usable · $9,499  |

No single consumer GPU we track holds a 200B–300B model at Q4\. Your options: two or more GPUs (see the [ready-made builds](https://whatcanirunai.vercel.app/builds/) or the [planner](https://whatcanirunai.vercel.app/planner/)), a large unified-memory machine (see the [best Mac for local LLMs](https://whatcanirunai.vercel.app/best-mac-for-local-llm/)), or a smaller quant with lower quality.

## Memory by context length

Each extra token of context adds to the KV cache. Numbers for Qwen3-235B-A22B, from its published config; a Q8 KV cache halves the cache part.

__Memory needed for a 200B–300B model by context length__
| Context     | Q4\_K\_M, FP16 cache | Q4\_K\_M, Q8 cache | Q8\_0, FP16 cache |
| ----------- | -------------------- | ------------------ | ----------------- |
| 4k tokens   | 164.9 GB             | 164.9 GB           | 288.3 GB          |
| 8k tokens   | 165.7 GB             | 164.9 GB           | 289.0 GB          |
| 16k tokens  | 167.3 GB             | 165.7 GB           | 290.6 GB          |
| 32k tokens  | 170.4 GB             | 167.3 GB           | 293.8 GB          |
| 64k tokens  | 176.7 GB             | 170.4 GB           | 300.1 GB          |
| 128k tokens | 189.3 GB             | 176.7 GB           | 312.7 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)); MoE shows an upper bound only.FitsTight

__Tracked hardware that runs a 200B–300B model at Q4__
| Hardware                                                                                | Q4 need vs. usable   | Q4 / Q8 / FP16                 | Est. tok/s |
| --------------------------------------------------------------------------------------- | -------------------- | ------------------------------ | ---------- |
| [Mac Studio M5 Ultra 256GB](https://whatcanirunai.vercel.app/hardware/mac-studio-m5-ultra-256/)unified memory · $9,499  | **164.9** / 192.0 GB | Q4FitsQ8Won’t fitFP16Won’t fit | up to 72   |
| [Mac Studio M5 Ultra 512GB](https://whatcanirunai.vercel.app/hardware/mac-studio-m5-ultra-512/)unified memory · $22,299 | **164.9** / 384.0 GB | Q4FitsQ8FitsFP16Won’t fit      | up to 72   |

## 200B–300B models we track

__Memory needed by each 200B–300B model we track__
| Model                                                                                                        | Q4\_K\_M | Q8\_0    | FP16     |
| ------------------------------------------------------------------------------------------------------------ | -------- | -------- | -------- |
| [Step-3.7-Flash](https://whatcanirunai.vercel.app/models/step-3.7-flash/)201.4B params · stepfun-ai · MoE                                    | 141.4 GB | 247.0 GB | 464.1 GB |
| [command-a-plus-05-2026-bf16](https://whatcanirunai.vercel.app/models/command-a-plus-05-2026-bf16/)218.8B params · CohereLabs · MoE          | 153.5 GB | 268.3 GB | 504.1 GB |
| [MiniMax-M2](https://whatcanirunai.vercel.app/models/minimax-m2/)228.7B params · MiniMaxAI · MoE                                             | 160.4 GB | 280.4 GB | 527.0 GB |
| [MiniMax-M2.5](https://whatcanirunai.vercel.app/models/minimax-m2.5/)228.7B params · MiniMaxAI · MoE                                         | 160.4 GB | 280.4 GB | 527.0 GB |
| [MiniMax-M2.7](https://whatcanirunai.vercel.app/models/minimax-m2.7/)228.7B params · MiniMaxAI · MoE                                         | 160.4 GB | 280.4 GB | 527.0 GB |
| [MiniMax-M2.1](https://whatcanirunai.vercel.app/models/minimax-m2.1/)228.7B params · MiniMaxAI · MoE                                         | 160.4 GB | 280.4 GB | 527.0 GB |
| [Qwen3-235B-A22B](https://whatcanirunai.vercel.app/models/qwen3-235b-a22b/)235.1B params · Qwen · MoE                                        | 164.9 GB | 288.3 GB | 541.7 GB |
| [Qwen3-235B-A22B-Instruct-2507](https://whatcanirunai.vercel.app/models/qwen3-235b-a22b-instruct-2507/)235.1B params · Qwen · MoE            | 164.9 GB | 288.3 GB | 541.7 GB |
| [Qwen3-235B-A22B-Thinking-2507](https://whatcanirunai.vercel.app/models/qwen3-235b-a22b-thinking-2507/)235.1B params · Qwen · MoE            | 164.9 GB | 288.3 GB | 541.7 GB |
| [Qwen3-VL-235B-A22B-Instruct](https://whatcanirunai.vercel.app/models/qwen3-vl-235b-a22b-instruct/)235.7B params · Qwen · MoE                | 165.3 GB | 289.0 GB | 543.0 GB |
| [DeepSeek-V2.5](https://whatcanirunai.vercel.app/models/deepseek-v2.5/)235.7B params · deepseek-ai · MoE                                     | 165.4 GB | 289.0 GB | 543.2 GB |
| [DeepSeek-Coder-V2-Instruct](https://whatcanirunai.vercel.app/models/deepseek-coder-v2-instruct/)235.7B params · deepseek-ai · MoE           | 165.4 GB | 289.0 GB | 543.2 GB |
| [DeepSeek-V2.5-1210](https://whatcanirunai.vercel.app/models/deepseek-v2.5-1210/)235.7B params · deepseek-ai · MoE                           | 165.4 GB | 289.0 GB | 543.2 GB |
| [DeepSeek-V2-Chat-0628](https://whatcanirunai.vercel.app/models/deepseek-v2-chat-0628/)235.7B params · deepseek-ai · MoE                     | 165.4 GB | 289.0 GB | 543.2 GB |
| [DeepSeek-Coder-V2-Instruct-0724](https://whatcanirunai.vercel.app/models/deepseek-coder-v2-instruct-0724/)235.7B params · deepseek-ai · MoE | 165.4 GB | 289.0 GB | 543.2 GB |
| [K-EXAONE-236B-A23B](https://whatcanirunai.vercel.app/models/k-exaone-236b-a23b/)237.1B params · LGAI-EXAONE · MoE                           | 166.3 GB | 290.7 GB | 546.3 GB |
| [Intern-S1](https://whatcanirunai.vercel.app/models/intern-s1/)240.7B params · internlm · MoE                                                | 168.8 GB | 295.1 GB | 554.6 GB |
| [Solar-Open2-250B](https://whatcanirunai.vercel.app/models/solar-open2-250b/)250.3B params · upstage · MoE                                   | 175.5 GB | 306.8 GB | 576.7 GB |
| [Llama-3\_1-Nemotron-Ultra-253B-v1](https://whatcanirunai.vercel.app/models/llama-3-1-nemotron-ultra-253b-v1/)253.4B params · nvidia         | 177.7 GB | 310.6 GB | 583.8 GB |
| [Inkling-Small](https://whatcanirunai.vercel.app/models/inkling-small/)266.0B params · thinkingmachines · MoE                                | 186.4 GB | 326.0 GB | 612.7 GB |
| [ERNIE-5.5-Flash-Base](https://whatcanirunai.vercel.app/models/ernie-5.5-flash-base/)286.2B params · baidu · MoE                             | 200.6 GB | 350.8 GB | 659.4 GB |
| [MemT-1.0-Flash](https://whatcanirunai.vercel.app/models/memt-1.0-flash/)287.9B params · MemTensor · MoE                                     | 201.8 GB | 352.8 GB | 663.3 GB |
| [DeepSeek-V4-Flash](https://whatcanirunai.vercel.app/models/deepseek-v4-flash/)290.9B params · deepseek-ai · MoE                             | 203.8 GB | 356.5 GB | 670.2 GB |
| [Ling-plus](https://whatcanirunai.vercel.app/models/ling-plus/)292.5B params · inclusionAI · MoE                                             | 205.0 GB | 358.4 GB | 673.8 GB |
| [Hy3](https://whatcanirunai.vercel.app/models/hy3/)298.8B params · tencent · MoE                                                             | 209.3 GB | 366.1 GB | 688.2 GB |
| [Hy3-preview](https://whatcanirunai.vercel.app/models/hy3-preview/)298.8B params · tencent · MoE                                             | 209.3 GB | 366.1 GB | 688.2 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 200B–300B model?

About 164.9 GB at Q4\_K\_M, 288.3 GB at Q8\_0 and 541.7 GB at FP16, at a \~4k context. That is for Qwen3-235B-A22B (235.1B parameters): weights plus about 15% for the KV cache and 1 GB for the runtime.

How much VRAM does a 200B–300B model need for 32k context?

About 170.4 GB at Q4\_K\_M with an FP16 KV cache, or 167.3 GB with a Q8 cache, based on Qwen3-235B-A22B's published config. Models with different attention layouts need more or less.

What is the cheapest GPU for a 200B–300B model?

None of the single consumer GPUs we track hold it at Q4\. Use several GPUs, a large unified-memory machine, or CPU offload (slow).

Can a Mac run a 200B–300B model?

Yes. The cheapest Mac or unified-memory mini PC we track that runs it at Q4 is the Mac Studio M5 Ultra 256GB (256 GB, about 192.0 GB usable, $9,499).

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

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

Other sizes: [1B–2B](https://whatcanirunai.vercel.app/how-much-vram/1b/) · [2B–3B](https://whatcanirunai.vercel.app/how-much-vram/3b/) · [4B–6B](https://whatcanirunai.vercel.app/how-much-vram/4b/) · [7B–8B](https://whatcanirunai.vercel.app/how-much-vram/8b/) · [12B–14B](https://whatcanirunai.vercel.app/how-much-vram/14b/) · [16B–21B](https://whatcanirunai.vercel.app/how-much-vram/20b/) · [24B–27B](https://whatcanirunai.vercel.app/how-much-vram/27b/) · [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/) · [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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