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Mistral AI / mixtral

Mixtral-8x7B-Instruct-v0.1

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

Open in the calculator →

Architecturepublished data
Quick answer
Mixtral-8x7B-Instruct-v0.1 needs about 30.1 GB at Q4_K_M — the format most people download — with 8,192 tokens of context. It fits entirely on 9 of 18 common devices, starting with the NVIDIA GeForce RTX 5090 (32 GB). What else fits in 32 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 NVIDIA GeForce RTX 5090, 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
FP1693.4 GB92.9 GB – 93.9 GBreconstructed size
Q8_049.6 GB49.4 GB – 49.9 GBreconstructed size
Q6_K38.3 GB38.1 GB – 38.5 GBreconstructed size
Q5_K_M33.1 GB33.0 GB – 33.3 GBreconstructed size
Q5_032.1 GB32.0 GB – 32.3 GBreconstructed size
Q4_K_M28.3 GB28.1 GB – 28.4 GBreconstructed size
Q4_026.3 GB26.2 GB – 26.4 GBreconstructed size
Q3_K_M23.1 GB21.5 GB – 24.7 GBreconstructed size
Q2_K16.9 GB16.3 GB – 17.6 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 · 9 of 18 devices hold it entirely
DeviceMemoryVerdictDecodeCalculator
NVIDIA GeForce RTX 507030.1 GB of 12 GB21 layers on system RAM~11 tok/s with offloadOpen →
AMD Radeon™ RX 9070 XT30.1 GB of 16 GB17 layers on system RAM~13 tok/s with offloadOpen →
NVIDIA GeForce RTX 408030.1 GB of 16 GB17 layers on system RAM~13 tok/s with offloadOpen →
NVIDIA GeForce RTX 508030.1 GB of 16 GB17 layers on system RAM~13 tok/s with offloadOpen →
NVIDIA GeForce RTX 5070 Ti30.1 GB of 16 GB17 layers on system RAM~13 tok/s with offloadOpen →
NVIDIA GeForce RTX 5060 Ti 16GB30.1 GB of 16 GB17 layers on system RAM~12 tok/s with offloadOpen →
NVIDIA GeForce RTX 409030.1 GB of 24 GB7 layers on system RAM~26 tok/s with offloadOpen →
AMD Radeon™ RX 7900 XTX30.1 GB of 24 GB7 layers on system RAM~27 tok/s with offloadOpen →
NVIDIA GeForce RTX 309030.1 GB of 24 GB7 layers on system RAM~25 tok/s with offloadOpen →
NVIDIA GeForce RTX 509030.1 GB of 32 GBfits~139 tok/sOpen →
NVIDIA RTX 6000 Ada Generation30.1 GB of 48 GBfits~74 tok/sOpen →
Apple M4 Pro30.1 GB of 64 GBfits~25 tok/sAbout reading paceOpen →
Apple M5 Pro30.1 GB of 64 GBfits~28 tok/sAbout reading paceOpen →
AMD Ryzen AI Max+ 395 with Radeon 8060S30.1 GB of 128 GBfits~23 tok/sAbout reading paceOpen →
Apple M3 Max30.1 GB of 128 GBfits~34 tok/sOpen →
Apple M4 Max30.1 GB of 128 GBfits~44 tok/sOpen →
NVIDIA DGX Spark30.1 GB of 128 GBfits~21 tok/sAbout reading paceOpen →
Apple M2 Ultra30.1 GB of 192 GBfits~57 tok/sOpen →

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-8x7B-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
23 configurations in stock · 10 out of stock
MachineSpeedPer hourPer M tokensWhereRent
4× RTX 306048 GBcheapest~111 tok/sFaster than you read$0.21$0.53Vast.aiMarketplace · 99.2% reliableRent on Vast.ai
2× RTX 4060 Ti 16GB32 GB~44 tok/sFaster than you read$0.22$1.40Vast.aiMarketplace · 99.7% reliableRent on Vast.ai
2× RTX 309048 GBrecommendedbest value~144 tok/sFaster than you read$0.24$0.46Vast.aiMarketplace · 99.5% reliableRent on Vast.ai
2× RTX 5060 Ti 16GB32 GB~69 tok/sFaster than you read$0.26$1.02Vast.aiMarketplace · 99.5% reliableRent on Vast.ai
1× RTX 6000 Ada48 GB~74 tok/sFaster than you read$0.26$0.98Vast.aiMarketplace · 99.9% reliableRunPod $0.84/h Rent on Vast.ai
1× RTX 509032 GB~138 tok/sFaster than you read$0.27$0.54Vast.aiMarketplace · 99.6% reliableRunPod $0.99/h Rent on Vast.ai
1× RTX A600048 GB~59 tok/sFaster than you read$0.28$1.32Vast.aiMarketplace · 99.6% reliableRunPod $0.53/h Rent on Vast.ai
1× RTX PRO 500048 GB~103 tok/sFaster than you read$0.51$1.36Vast.aiMarketplace · 98.1% reliableRunPod $0.82/h Rent on Vast.ai

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-8x7B-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-8x7B-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 2026-09-13 at pinned commit eba92302a286; parameter count from the safetensors index at the same revision.

Architecture
mixtral
Layers
32
Hidden size
4,096
Attention heads
32
KV heads
8
Feed-forward width
14,336
Vocabulary
32,000
Context ceiling
32,768
RoPE theta
1,000,000
Experts
8
Experts / token
2

Where the memory goes

tokenembedding× 32 decoder blocksGrouped-query attention32 query · 8 KV headsfull contextRouted experts2 of 8 per tokenall residentoutputprojectiongrows with contextcapacity ≠ traffic

Grouped-query attention shares each key/value head across 4 query heads, so the KV cache is 25% 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-8x7b-instruct-v0-1","quantization":"Q4_K_M","context":8192,"hardware":"nvidia-geforce-rtx-4090"}'

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On your page, as a widget
<script src="https://llmbottleneck.com/widget.js"
  data-model="mistralai-mixtral-8x7b-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-8x7B-Instruct-v0.1 VRAM and hardware requirements.” Architecture from mistralai/Mixtral-8x7B-Instruct-v0.1 at revision eba92302a286, retrieved 2026-09-13. https://llmbottleneck.com/models/mistralai-mixtral-8x7b-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