Tools / LLM GPU calculator / Mixtral 8x7B

Mixtral 8x7B VRAM requirements

GPU memory for the weights and KV cache at FP16, FP8 and 4-bit, and how many GPUs that takes. Figures from the model's config.json.

Size your own scenario

Short answer

Mixtral 8x7B needs about 105 GB of GPU memory at FP16/BF16, 53 GB at FP8 and 30 GB at 4-bit to serve one user at an 8K-token context. Its 46.7B parameters take 93.4 GB at 16-bit, and that conversation adds 1.1 GB of KV cache. It is a mixture-of-experts model: 12.9B parameters are active per token, but all 46.7B must be in GPU memory. On NVIDIA H100 80 GB GPUs that is 2 GPUs (tensor parallel 2) at FP16 and 1 GPU at FP8; for a desktop, at 4-bit it fits a single 32 GB GeForce RTX 5090, though not a 24 GB RTX 4090.

Updated October 16, 2026. KV cache at FP16, plus 10% headroom for activations and the runtime.

GPU memory by precision

Weights	Weight memory	1 user, 8K context	32 users, 8K context	1 user, 32K context
FP16/BF16	93.4 GB	105 GB	142 GB	109 GB
FP8	46.7 GB	53 GB	90 GB	57 GB
4-bit (INT4)	25.7 GB	30 GB	67 GB	33 GB

Required memory = (weights + KV cache) ÷ 0.9. An FP8 KV cache halves the cache share; set it in the calculator.

How many GPUs for one user at 8K context

GPU	Memory	FP16/BF16	FP8	4-bit (INT4)
Data center · tensor parallel
NVIDIA L40S	48 GB	4 GPUs · TP4	2 GPUs · TP2	1 GPU
NVIDIA RTX PRO 6000 Blackwell Server Edition	96 GB	2 GPUs · TP2	1 GPU	1 GPU
NVIDIA H100 SXM	80 GB	2 GPUs · TP2	1 GPU	1 GPU
NVIDIA H100 NVL	94 GB	2 GPUs · TP2	1 GPU	1 GPU
NVIDIA H200	141 GB	1 GPU	1 GPU	1 GPU
NVIDIA B200	180 GB	1 GPU	1 GPU	1 GPU
NVIDIA B300	288 GB	1 GPU	1 GPU	1 GPU
AMD Instinct MI300X	192 GB	1 GPU	1 GPU	1 GPU
AMD Instinct MI355X	288 GB	1 GPU	1 GPU	1 GPU
Workstation & consumer · layer split
NVIDIA RTX PRO 6000 Blackwell Workstation Edition	96 GB	2 cards	1 card	1 card
NVIDIA GeForce RTX 3090	24 GB	5 cards	3 cards	2 cards
NVIDIA GeForce RTX 4090	24 GB	5 cards	3 cards	2 cards
NVIDIA GeForce RTX 5090	32 GB	4 cards	2 cards	1 card
Apple Mac Studio M3 Ultra / M5 Ultra	96 GB (72 usable)	2 Macs	1 Mac	1 Mac
Apple Mac Studio M3 Ultra / M5 Ultra	256 GB (192 usable)	1 Mac	1 Mac	1 Mac
Apple Mac Studio M5 Ultra	512 GB (384 usable)	1 Mac	1 Mac	1 Mac

Data center GPUs are counted at a tensor-parallel size (TP) that divides the model’s attention heads, within one 8-GPU server. Workstation and consumer cards are counted for a layer split, which works with any number of cards. Mac Studio counts about 75% of unified memory as usable by the GPU. Capacity only: speed depends on memory bandwidth and the serving stack.

Memory is step one. Throughput, latency, power and the network decide the real build.

Size my actual workload

Mixtral 8x7B architecture

Parameters: 46.7B total, 12.9B active per token (mixture of experts)
Layers with a KV cache: 32
Attention heads / KV heads: 32 / 8
Head dimension: 128
Attention: Grouped-query attention (4 query heads per KV head)
Maximum context: 32,768 tokens
KV cache per token (FP16): 2 × 32 layers × 8 KV heads × 128 head dim × 2 bytes = 131,072 bytes (0.13 MB) per token

Source: mistralai/Mixtral-8x7B-Instruct-v0.1 config.json on Hugging Face. Parameter counts are the Hub’s count of the repo’s safetensors files, or the model card’s figure.

Questions about Mixtral 8x7B

How much VRAM does Mixtral 8x7B need?

About 105 GB at FP16/BF16, 53 GB at FP8 and 30 GB at 4-bit, for one user at an 8K-token context. That is 93.4 GB, 46.7 GB or 25.7 GB of weights, plus 1.1 GB of KV cache, plus 10% headroom for activations and the runtime. Serving 32 users at 8K each takes 142 GB at FP16 or 90 GB at FP8, because every conversation keeps its own KV cache.

How many H100 GPUs does Mixtral 8x7B need?

At FP16/BF16 it takes 2 GPUs (tensor parallel 2), for one user at 8K or for 32 users. At FP8, 1 GPU for one user at 8K and 2 GPUs (tensor parallel 2) for 32 users. One user at FP8 takes 1 GPU on H200 (141 GB) and 1 GPU on B200 (180 GB).

Can Mixtral 8x7B run on an RTX 4090 or RTX 5090?

At 4-bit it needs about 30 GB for one user at 8K: 2 RTX 4090s or a single RTX 5090 (32 GB). At FP8 (53 GB) it takes 3 RTX 4090s or 2 RTX 5090s. Several cards work with a layer split, as in llama.cpp, which divides the model's layers between them.

Can Mixtral 8x7B run on a Mac Studio?

macOS lets the GPU use about 75% of unified memory by default: about 72 GB of a 96 GB, 192 GB of a 256 GB and 384 GB of a 512 GB Mac Studio. At 4-bit Mixtral 8x7B needs 30 GB for one user at 8K, so it fits one 96 GB Mac Studio; at FP8 (53 GB) it fits one 96 GB Mac Studio.

How large is Mixtral 8x7B's KV cache?

At FP16, 2 × 32 layers × 8 KV heads × 128 head dim × 2 bytes = 131,072 bytes (0.13 MB) per token. It uses grouped-query attention: 4 query heads share each KV head, which makes the cache 4× smaller than full multi-head attention. An 8K-token conversation holds 1.1 GB at FP16, and a 32K-token one holds 4.3 GB.

Does Mixtral 8x7B need memory for all its experts?

Yes. Mixtral 8x7B is a mixture-of-experts model with 46.7B parameters, of which about 12.9B are active for each token. Any token can be routed to any expert, so all of them must be in GPU memory: memory follows total parameters, speed follows active ones.

What is Mixtral 8x7B's maximum context length?

32,768 tokens, according to its Hugging Face configuration. One user at that full length holds 4.3 GB of KV cache at FP16, for 57 GB in total with FP8 weights, or 54 GB with an FP8 KV cache as well.

Other models

Not listed? The LLM GPU calculator sizes any Hugging Face model from its ID, link or config.json.

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