How much VRAM does Mixtral 8x22B need?
Mixtral 8x22B needs about 84.3 GB of VRAM at Q4_K_M, or 147.2 GB at Q8_0 and 276.5 GB at FP16. No consumer card holds it at Q4_K_M. The smallest fit in our set is the H200 141GB (141 GB). Weights plus runtime overhead; KV cache for your context length is on top.
Weights-plus-runtime footprint across the six most common quantizations. KV cache is context-dependent and comes on top. At 32K it is gigabytes on its own, before the weights; size it precisely in the GPU Memory Calculator.
This is a mixture-of-experts model: all 141B parameters must sit in memory, but only about 39B are active per token — so it loads like a 141B model and runs closer to a 39B one.
This is the base checkpoint: pretrained on raw text and not tuned to follow instructions, so it continues text rather than answering prompts. That makes it the right starting point if you plan to fine-tune your own model. For chat or assistant work, use Mixtral 8x22B Instruct — same parameter count, so the memory figures below are identical.
Can your GPU run Mixtral 8x22B?
Pick your card — the answer below is computed by the same engine that produces every figure on this page.
H200 141GB runs Mixtral 8x22B at Q4_K_M with 49.6 GB to spare; comfortable at this size, where most cards cannot hold the weights at all; as a mixture-of-experts model all 141B parameters must be resident even though only 39B are active per token.
Weights plus runtime overhead. KV cache is not included — it depends on context length and this model’s published architecture.
| Weights (Q4_K_M) | 79.6 GB |
| Runtime overhead | 4.7 GB |
| Estimated total | 84.3 GB |
| Usable VRAM (95% of 141 GB) | 133.9 GB |
Across every GPU we track
- RTX 3060 12GBNot enough-72.9 GB
- RTX 4060 Ti 16GBNot enough-69.1 GB
- RTX 4070 Ti SUPERNot enough-69.1 GB
- RTX 3090Not enough-61.5 GB
- RTX 4090Not enough-61.5 GB
- RTX 5090Not enough-53.9 GB
- A100 40GBNot enough-46.3 GB
- RTX A6000Not enough-38.7 GB
- L40SNot enough-38.7 GB
- A100 80GBNot enough-8.3 GB
- H100 80GBNot enough-8.3 GB
- H200 141GBRecommended+49.6 GB
Estimates, not guarantees: real usage moves with runtime, driver, batch size and context. Size a specific context window in the GPU Memory Calculator.
How was this number calculated? Every figure here comes from Bitpute’s documented calculation methodology — parameters, precision, quantization, runtime overhead and usable VRAM, each formula written out in full.
Memory by quantization
| Quant | Weights | + runtime overhead |
|---|---|---|
| FP16 | 262.6 GB | 276.5 GB |
| Q8_0 | 139.5 GB | 147.2 GB |
| Q6_K | 107.7 GB | 113.8 GB |
| Q5_K_M | 93.4 GB | 98.8 GB |
| Q4_K_M | 79.6 GB | 84.3 GB |
| Q4_0 | 73.9 GB | 78.3 GB |
Overhead = 0.75 GB + 5% of weights (CUDA context, buffers). Add KV cache on top. Weights dominate at this size, but a 32K window still costs gigabytes.
Which quantization should you actually run?
Mixtral 8x22B is too large for a 12–16 GB card. On H200 141GB, Q4_K_M (84.3 GB) fits with plenty of headroom for long context — the realistic single-GPU entry point. Higher quality (Q6_K 113.8 GB, FP16 276.5 GB) needs a 48 GB+ card or multiple GPUs — for occasional use, renting is usually cheaper than buying. As a mixture-of-experts model it must hold all 141B in VRAM but activates only about 39B per token, so it runs much faster than its footprint suggests — memory is the limit here, not speed.
Single-GPU fit at Q4_K_M (84.3 GB + KV)
RTX 3060 12GBRTX 4060 Ti 16GBRTX 4070 Ti SUPERRTX 3090RTX 4090RTX 5090A100 40GBRTX A6000L40SA100 80GBH100 80GBH200 141GB
Where the memory goes
Weights and overhead are exact for Q4_K_M. The KV bar is a generic 32-layer transformer at 8K; at this scale the weights dominate, but long context still adds gigabytes — size it in the calculator is for.
Common questions
How much VRAM does Mixtral 8x22B need?
Mixtral 8x22B needs about 84.3 GB of VRAM at Q4_K_M, 147.2 GB at Q8_0, or 276.5 GB at FP16. That is weights plus about 0.75 GB of runtime overhead and 5% of weight size; KV cache is additional and depends on context length.
What GPU can run Mixtral 8x22B?
At Q4_K_M the smallest card in our set that fits is the H200 141GB (141 GB usable at 95%). At FP16 no single GPU in our set is sufficient.
Can Mixtral 8x22B run on 24 GB?
Not at Q4_K_M: it needs about 84.3 GB, more than the ~22.8 GB usable on a 24 GB card. Use a larger card, multiple GPUs, or offload.
Same memory footprint as 1 other checkpoint
Mixtral 8x22B has an identical parameter count to this, so every figure on this page applies to it unchanged. What varies is different checkpoint roles (instruction-tuned) — not the size. Choose on capability and licence; the memory budget is identical.