LLMBOTTLENECK.COM
Mistral AI / ministral3

Ministral-3-8B-Instruct-2512

8.92 billion parameters.

Open in the calculator →

Architecturepublished data
Quick answer
Ministral-3-8B-Instruct-2512 needs about 7.14 GB at Q4_K_M — the format most people download — with 8,192 tokens of context. It fits entirely on 18 of 18 common devices, starting with the NVIDIA GeForce RTX 5070 (12 GB). What else fits in 12 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 5070, 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
FP1617.8 GB12.5 GB – 18.0 GBsize range
Q8_09.03 GBexactpublished data
Q6_K7.32 GB5.12 GB – 8.66 GBsize range
Q5_K_M6.06 GBexactpublished data
Q5_06.22 GB4.29 GB – 8.66 GBsize range
Q4_K_M5.20 GBexactpublished data
Q4_05.19 GB3.51 GB – 8.66 GBsize range
Q3_K_M4.46 GB2.68 GB – 8.66 GBsize range
Q2_K3.53 GB2.05 GB – 8.66 GBsize range

3 of these are published files. Open the pinned GGUF repository ↗

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 · 18 of 18 devices hold it entirely
DeviceMemoryVerdictDecodeCalculator
NVIDIA GeForce RTX 50707.14 GB of 12 GBfits~76 tok/sOpen →
AMD Radeon™ RX 9070 XT7.14 GB of 16 GBfits~83 tok/sOpen →
NVIDIA GeForce RTX 40807.14 GB of 16 GBfits~81 tok/sOpen →
NVIDIA GeForce RTX 50807.14 GB of 16 GBfits~108 tok/sOpen →
NVIDIA GeForce RTX 5070 Ti7.14 GB of 16 GBfits~101 tok/sOpen →
NVIDIA GeForce RTX 5060 Ti 16GB7.14 GB of 16 GBfits~50 tok/sOpen →
NVIDIA GeForce RTX 40907.14 GB of 24 GBfits~114 tok/sOpen →
AMD Radeon™ RX 7900 XTX7.14 GB of 24 GBfits~124 tok/sOpen →
NVIDIA GeForce RTX 30907.14 GB of 24 GBfits~105 tok/sOpen →
NVIDIA GeForce RTX 50907.14 GB of 32 GBfits~202 tok/sOpen →
NVIDIA RTX 6000 Ada Generation7.14 GB of 48 GBfits~108 tok/sOpen →
Apple M4 Pro7.14 GB of 64 GBfits~34 tok/sOpen →
Apple M5 Pro7.14 GB of 64 GBfits~37 tok/sOpen →
AMD Ryzen AI Max+ 395 with Radeon 8060S7.14 GB of 128 GBfits~33 tok/sOpen →
Apple M3 Max7.14 GB of 128 GBfits~46 tok/sOpen →
Apple M4 Max7.14 GB of 128 GBfits~57 tok/sOpen →
NVIDIA DGX Spark7.14 GB of 128 GBfits~31 tok/sOpen →
Apple M2 Ultra7.14 GB of 192 GBfits~73 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 Ministral-3-8B-Instruct-2512

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
28 configurations in stock · 5 out of stock
MachineSpeedPer hourPer M tokensWhereRent
1× RTX 306012 GBrecommendedcheapestbest value~40 tok/sFaster than you read$0.036$0.24Vast.aiMarketplace · 99.8% reliableRent on Vast.ai
1× RTX 308010 GB~85 tok/sFaster than you read$0.082$0.27Vast.aiMarketplace · 98.3% reliableRent on Vast.ai
1× RTX 30708 GB~50 tok/sFaster than you read$0.082$0.45Vast.aiMarketplace · 99.1% reliableRunPod $0.13/h Rent on Vast.ai
1× RTX 4060 Ti 16GB16 GB~32 tok/sFaster than you read$0.090$0.77Vast.aiMarketplace · 99.4% reliableRent on Vast.ai
1× RTX 407012 GB~56 tok/sFaster than you read$0.096$0.47Vast.aiMarketplace · 99.8% reliableRent on Vast.ai
1× RTX 309024 GB~105 tok/sFaster than you read$0.12$0.32Vast.aiMarketplace · 99.5% reliableRunPod $0.50/h Rent on Vast.ai
1× RTX 5060 Ti 16GB16 GB~50 tok/sFaster than you read$0.12$0.67Vast.aiMarketplace · 99.1% reliableRent on Vast.ai
1× RTX 3080 Ti12 GB~102 tok/sFaster than you read$0.14$0.37Vast.aiMarketplace · 99.9% reliableRent on Vast.ai

No GPU at all

Use Ministral-3-8B-Instruct-2512 by the token

For one person chatting, that is about 3.2× cheaper than the best-value rented card above ($0.24 per million tokens). A rented GPU pays off when you keep it busy — many requests batched together, long agent runs — or when the data must stay on a machine you control, or the exact file you want is not served anywhere.

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 Q4_K_M on it (llama.cpp)
Container image
ghcr.io/ggml-org/llama.cpp:server-cuda
Start command / arguments
-hf mistralai/Ministral-3-8B-Instruct-2512-GGUF:Q4_K_M -c 8192 -ngl 999 --host 0.0.0.0 --port 8080
Expose port
8080 — an OpenAI-compatible API at /v1

Starting from a saved llama.cpp template instead? Leave the arguments empty and set these variables — only the first two change between models:

LLAMA_ARG_HF_REPO=mistralai/Ministral-3-8B-Instruct-2512-GGUF:Q4_K_M
LLAMA_ARG_CTX_SIZE=8192
LLAMA_ARG_N_GPU_LAYERS=999
LLAMA_ARG_HOST=0.0.0.0
LLAMA_ARG_PORT=8080

Already have the runtime? llama-server -hf mistralai/Ministral-3-8B-Instruct-2512-GGUF:Q4_K_M -c 8192 -ngl 999 --host 0.0.0.0 --port 8080. Weights: mistralai/Ministral-3-8B-Instruct-2512-GGUF. The CUDA image is for NVIDIA cards; an AMD machine needs llama.cpp's ROCm build.

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 Ministral-3-8B-Instruct-2512 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 5b26027e7b19; parameter count from the safetensors index at the same revision.

Architecture
ministral3
Layers
34
Hidden size
4,096
Attention heads
32
KV heads
8
Head dimension
128
Feed-forward width
14,336
Vocabulary
131,072
Context ceiling
262,144
RoPE theta
1,000,000

Where the memory goes

tokenembedding× 34 decoder blocksGrouped-query attention32 query · 8 KV headsfull contextFeed-forwardone networkall activeoutputprojectiongrows with contextfixed per token

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.

This is a multimodal checkpoint (vision); the diagram and memory sizing above cover the text decoder. Encoder/audio/image activations are not included in the KV or weight figures.

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-2512Mistral-7B-Instruct-v0.2Mistral-7B-Instruct-v0.3Mistral-7B-v0.1

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-ministral-3-8b-instruct-2512","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-ministral-3-8b-instruct-2512" data-quantization="Q4_K_M"
  data-hardware="nvidia-geforce-rtx-4090" data-context="8192"></script>

No key needed. Unbranded, with your own buy button, on Pro and Business →

Citing this page

LLM Bottleneck. “Ministral-3-8B-Instruct-2512 VRAM and hardware requirements.” Architecture from mistralai/Ministral-3-8B-Instruct-2512 at revision 5b26027e7b19, retrieved 2026-09-01. https://llmbottleneck.com/models/mistralai-ministral-3-8b-instruct-2512

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

llmbottleneck
catalogue 2026-10-03models 327devices 135