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Mistral AI / mixtral

Mixtral-8x22B-Instruct-v0.1

140.6 billion parameters, routing 2 of 8 experts per token.

Open in the calculator →

Architecturepublished data
Quick answer
Mixtral-8x22B-Instruct-v0.1 needs about 87.7 GB at Q4_K_M — the format most people download — with 8,192 tokens of context. It fits entirely on 5 of 18 common devices, starting with the AMD Ryzen AI Max+ 395 with Radeon 8060S (128 GB). What else fits in 128 GB. No hardware for it? Rent a GPU that runs it, priced live.

Sized at 8,192 tokens of context with the whole model in device memory, across 18 common devices. Speeds are estimates, not benchmarks.

Find the AMD Ryzen AI Max+ 395 with Radeon 8060S, the smallest card listed here that holds it: Amazon ↗ · eBay (new and used) ↗Store links may pay us a commission. They never decide which card is suggested — the memory arithmetic does.

Weight-file size by format

FormatSizeRangeBasis
FP16281.3 GB279.9 GB – 282.7 GBreconstructed size
Q8_0149.4 GB148.7 GB – 150.2 GBreconstructed size
Q6_K115.4 GB114.8 GB – 116.0 GBreconstructed size
Q5_K_M99.7 GB99.2 GB – 100.2 GBreconstructed size
Q5_096.7 GB96.2 GB – 97.2 GBreconstructed size
Q4_K_M85.0 GB84.6 GB – 85.5 GBreconstructed size
Q4_079.2 GB78.8 GB – 79.6 GBreconstructed size
Q3_K_M69.4 GB64.5 GB – 74.3 GBreconstructed size
Q2_K50.9 GB48.8 GB – 52.9 GBreconstructed size

A file size is not the memory a run needs: the KV cache and the runtime reserve come on top, and the calculator adds both.

What the labels mean
published data
Read from a published source — a file's byte count, a model's configuration or a manufacturer's specification — or exact arithmetic on such values. Not a measurement on a machine.
reconstructed size
Weight size reconstructed from the pinned architecture, because no published file exists.
size range
Only a lower and an upper bound are claimed for this weight size.

Hardware ladder

Q4_K_M at 8,192 tokens · 5 of 18 devices hold it entirely
DeviceMemoryVerdictDecodeCalculator
NVIDIA GeForce RTX 507087.7 GB of 12 GB50 layers on system RAM~2.8 tok/s with offloadOpen →
AMD Radeon™ RX 9070 XT87.7 GB of 16 GB48 layers on system RAM~2.9 tok/s with offloadOpen →
NVIDIA GeForce RTX 408087.7 GB of 16 GB48 layers on system RAM~2.9 tok/s with offloadOpen →
NVIDIA GeForce RTX 508087.7 GB of 16 GB48 layers on system RAM~2.9 tok/s with offloadOpen →
NVIDIA GeForce RTX 5070 Ti87.7 GB of 16 GB48 layers on system RAM~2.9 tok/s with offloadOpen →
NVIDIA GeForce RTX 5060 Ti 16GB87.7 GB of 16 GB48 layers on system RAM~2.8 tok/s with offloadOpen →
NVIDIA GeForce RTX 409087.7 GB of 24 GB42 layers on system RAM~3.3 tok/s with offloadOpen →
AMD Radeon™ RX 7900 XTX87.7 GB of 24 GB42 layers on system RAM~3.3 tok/s with offloadOpen →
NVIDIA GeForce RTX 309087.7 GB of 24 GB42 layers on system RAM~3.3 tok/s with offloadOpen →
NVIDIA GeForce RTX 509087.7 GB of 32 GB37 layers on system RAM~3.8 tok/s with offloadOpen →
NVIDIA RTX 6000 Ada Generation87.7 GB of 48 GB27 layers on system RAM~4.8 tok/s with offloadOpen →
Apple M4 Pro87.7 GB of 64 GBdoes not fitnot calibratedOpen →
Apple M5 Pro87.7 GB of 64 GBdoes not fitnot calibratedOpen →
AMD Ryzen AI Max+ 395 with Radeon 8060S87.7 GB of 128 GBfits~7.9 tok/sSlowOpen →
Apple M3 Max87.7 GB of 128 GBfits~14 tok/sAbout reading paceOpen →
Apple M4 Max87.7 GB of 128 GBfits~18 tok/sAbout reading paceOpen →
NVIDIA DGX Spark87.7 GB of 128 GBfits~7.3 tok/sSlowOpen →
Apple M2 Ultra87.7 GB of 192 GBfits~25 tok/sAbout reading paceOpen →

Decode is a calibrated estimate from the published calibration; capacity is the manufacturer's published ceiling, not guaranteed free memory. Fitting in memory is not the same as loading: whether the runtime and version you have supports this architecture and format on that machine has not been tested here. Offloaded rows assume there is enough system RAM for the overflow — the calculator checks that against the RAM you declare. Each “Open” link keeps this model, format and context.

Run it in the cloud

Rent a GPU that runs Mixtral-8x22B-Instruct-v0.1

Every rentable machine that holds the whole model, from Vast.ai and RunPod, with this site's speed estimate for each and the provider's own price, read live. Cheapest first; sort by cost per token or speed instead, or switch the format.

Prices · 18:30 UTC, 22 Sept
16 configurations in stock · 15 out of stock
MachineSpeedPer hourPer M tokensWhereRent
8× RTX 306096 GBcheapest~77 tok/sFaster than you read$0.59$2.12Vast.aiMarketplace · 99.7% reliableRent on Vast.ai
4× RTX 309096 GB~100 tok/sFaster than you read$0.60$1.65Vast.aiMarketplace · 99.4% reliableRent on Vast.ai
4× RTX 5090128 GBrecommendedbest value~192 tok/sFaster than you read$1.07$1.55Vast.aiMarketplace · 99.6% reliableRent on Vast.ai
2× RTX 6000 Ada96 GB~51 tok/sFaster than you read$1.07$5.80Vast.aiMarketplace · 99.4% reliableRent on Vast.ai
1× RTX PRO 600096 GB~48 tok/sFaster than you read$1.14$6.57Vast.aiMarketplace · 98.8% reliableRunPod $1.69/h Rent on Vast.ai
2× RTX A600096 GB~41 tok/sFaster than you read$1.20$8.14Vast.aiMarketplace · 99.4% reliableRent on Vast.ai
4× L496 GB~32 tok/sFaster than you read$1.29$11.12Vast.aiMarketplace · 99.4% reliableRent on Vast.ai
4× RTX 409096 GB~108 tok/sFaster than you read$1.36$3.50RunPodCommunity CloudVast.ai $1.60/h Rent on RunPod

No GPU at all

Use Mixtral-8x22B-Instruct-v0.1 by the token

Here the rented card is as cheap as the API or cheaper per token ($1.55 per million), and it keeps your data on a machine you control. The API is still the easier start: nothing to set up and nothing to switch off.

First time renting a GPU? How it works, in four steps
  1. Create an account and add credit. Both providers are prepaid: $10 is enough to try any card on this page for hours. A new RunPod account that arrives through a referral link gets a one-time $5 credit when it first adds $10.
  2. Pick the card and the count shown here, and a template: llama.cpp or vLLM for an OpenAI-compatible API, or one with a web chat if you only want to talk to the model. “How to launch it” below gives the exact image and command.
  3. Wait for the download. The weights are fetched on the machine; tens of gigabytes take a few minutes on a data-centre connection. You pay by the second from the moment it starts.
  4. Stop it when you are done. A running machine bills even when idle. A stopped one costs nothing per hour, though its disk is usually still billed until you delete it.
How to launch it

No Q4_K_M file of Mixtral-8x22B-Instruct-v0.1 is catalogued here, so there is no exact command to vouch for. Start the machine from the llama.cpp server image ghcr.io/ggml-org/llama.cpp:server-cuda and point -hf at a Q4_K_M build from the publisher or a quantizer you trust — search Hugging Face for one. Check its file size against the memory figure on this page before you rent.

Every machine holds the whole model at Q4_K_M and 8,192 tokens of context, no offload. Speeds are this site’s single-stream decode estimates; prices are what each provider’s own API quoted, on-demand, for the whole machine. Vast hosts below 98% measured reliability are left out.

Referral links Vast.ai, RunPod and Novita pay us a share of what you spend if you sign up through these buttons. It costs you nothing, and it never decides an order or a recommendation: both are computed from the live price and the speed, and options that pay us nothing are listed and recommended on the same terms. How we rank

Buying a card instead, or paying by the token? Compare a year of Mixtral-8x22B-Instruct-v0.1 three ways →

Under the hood

Architecture, read from the publisher’s file

The numbers every figure above is computed from, with the file they came from.

Architecture

✓ Architecture read from the published config.json

Retrieved 2026-09-01 at pinned commit cc88a6cc19fb; parameter count from the safetensors index at the same revision.

Architecture
mixtral
Layers
56
Hidden size
6,144
Attention heads
48
KV heads
8
Feed-forward width
16,384
Vocabulary
32,768
Context ceiling
65,536
RoPE theta
1,000,000
Experts
8
Experts / token
2

Where the memory goes

tokenembedding× 56 decoder blocksGrouped-query attention48 query · 8 KV headsfull contextRouted experts2 of 8 per tokenall residentoutputprojectiongrows with contextcapacity ≠ traffic

Grouped-query attention shares each key/value head across 6 query heads, so the KV cache is 17% of what multi-head attention would need at the same context.

Open the exact official config.json ↗

More from Mistral AI

Devstral-2-123B-Instruct-2512Devstral-Small-2507Magistral-Small-2509Ministral-3-14B-Instruct-2512Ministral-3-3B-Instruct-2512Ministral-3-8B-Instruct-2512Mistral-7B-Instruct-v0.2Mistral-7B-Instruct-v0.3

Use this answer in your own product

As JSON, from the API
curl -s https://llmbottleneck.com/v1/analyze \
  -H "Authorization: Bearer $LLMB_KEY" -H "content-type: application/json" \
  -d '{"model":"mistralai-mixtral-8x22b-instruct-v0-1","quantization":"Q4_K_M","context":8192,"hardware":"nvidia-geforce-rtx-4090"}'

Same engine, same evidence, every field sourced. Free key in one step →

On your page, as a widget
<script src="https://llmbottleneck.com/widget.js"
  data-model="mistralai-mixtral-8x22b-instruct-v0-1" data-quantization="Q4_K_M"
  data-hardware="nvidia-geforce-rtx-4090" data-context="8192"></script>

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Citing this page

LLM Bottleneck. “Mixtral-8x22B-Instruct-v0.1 VRAM and hardware requirements.” Architecture from mistralai/Mixtral-8x22B-Instruct-v0.1 at revision cc88a6cc19fb, retrieved 2026-09-01. https://llmbottleneck.com/models/mistralai-mixtral-8x22b-instruct-v0-1

Every figure above is either the published value or a reconstruction whose measured error is on the accuracy page.

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catalogue 2026-10-03models 327devices 135