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XiaomiMiMo / mimo_v2

MiMo-V2.6-Pro-RL

1.02T total · 42B active parameters, routing 8 of 384 experts per token.

The official MiMo-V2.6 card declares 1.02T total and 42B activated parameters for the flagship checkpoint. Its expert tensors are stored as mxfp4 U8 blocks (two four-bit values per element, with their own scales), so the safetensors element total (524.1B) counts storage words rather than logical parameters and is not shown as a model total. Open the official parameter note ↗

Open in the calculator →

Architecturepublished data
Quick answer
MiMo-V2.6-Pro-RL needs about 629.1 GB at Q4_K_M — the format most people download — with 8,192 tokens of context. None of the 18 common devices listed below holds it entirely at this setting. 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.

Official open weights of the MiMo-V2.6 release (2026-09-22), the checkpoint Xiaomi serves as mimo-v2.6-pro; the card titles it MiMo-V2.6-Pro-RL. Sparse MoE, 1.02T total / 42B activated parameters, 1M context, native text/image/video/audio. The checkpoint stores its expert tensors as mxfp4 U8 blocks (quantization_config.store_dtype), so the safetensors element total is a storage-word count; the card total is recorded in data/model-parameter-facts.json. Licence declared on the repository: mit.

Weight-file size by format

FormatSizeRangeBasis
FP162078.7 GB2031.1 GB – 2126.3 GBreconstructed size
Q8_01104.6 GB1079.3 GB – 1129.9 GBreconstructed size
Q6_K853.0 GB833.5 GB – 872.5 GBreconstructed size
Q5_K_M737.0 GB720.1 GB – 753.9 GBreconstructed size
Q5_0715.1 GB698.8 GB – 731.5 GBreconstructed size
Q4_K_M627.8 GB613.4 GB – 642.2 GBreconstructed size
Q4_0585.4 GB572.0 GB – 598.8 GBreconstructed size
Q3_K_M513.1 GB468.0 GB – 558.2 GBreconstructed size
Q2_K376.5 GB354.7 GB – 398.3 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 · 0 of 18 devices hold it entirely
DeviceMemoryVerdictDecodeCalculator
NVIDIA GeForce RTX 5070629.1 GB of 12 GB69 layers on system RAM~2.3 tok/s with offloadOpen →
AMD Radeon™ RX 9070 XT629.1 GB of 16 GB69 layers on system RAM~2.3 tok/s with offloadOpen →
NVIDIA GeForce RTX 4080629.1 GB of 16 GB69 layers on system RAM~2.3 tok/s with offloadOpen →
NVIDIA GeForce RTX 5080629.1 GB of 16 GB69 layers on system RAM~2.3 tok/s with offloadOpen →
NVIDIA GeForce RTX 5070 Ti629.1 GB of 16 GB69 layers on system RAM~2.3 tok/s with offloadOpen →
NVIDIA GeForce RTX 5060 Ti 16GB629.1 GB of 16 GB69 layers on system RAM~2.2 tok/s with offloadOpen →
NVIDIA GeForce RTX 4090629.1 GB of 24 GB68 layers on system RAM~2.3 tok/s with offloadOpen →
AMD Radeon™ RX 7900 XTX629.1 GB of 24 GB68 layers on system RAM~2.3 tok/s with offloadOpen →
NVIDIA GeForce RTX 3090629.1 GB of 24 GB68 layers on system RAM~2.3 tok/s with offloadOpen →
NVIDIA GeForce RTX 5090629.1 GB of 32 GB67 layers on system RAM~2.3 tok/s with offloadOpen →
NVIDIA RTX 6000 Ada Generation629.1 GB of 48 GB65 layers on system RAM~2.4 tok/s with offloadOpen →
Apple M4 Pro629.1 GB of 64 GBdoes not fitnot calibratedOpen →
Apple M5 Pro629.1 GB of 64 GBdoes not fitnot calibratedOpen →
AMD Ryzen AI Max+ 395 with Radeon 8060S629.1 GB of 128 GBdoes not fitnot calibratedOpen →
Apple M3 Max629.1 GB of 128 GBdoes not fitnot calibratedOpen →
Apple M4 Max629.1 GB of 128 GBdoes not fitnot calibratedOpen →
NVIDIA DGX Spark629.1 GB of 128 GBdoes not fitnot calibratedOpen →
Apple M2 Ultra629.1 GB of 192 GBdoes not fitnot calibratedOpen →

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 MiMo-V2.6-Pro-RL

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
4 configurations in stock · 6 out of stock
MachineSpeedPer hourPer M tokensWhereRent
8× RTX PRO 6000768 GBcheapest~355 tok/sFaster than you read$16.54$12.92Vast.aiMarketplace · 98.4% reliableRent on Vast.ai
8× H100 PCIe640 GB~396 tok/sFaster than you read$19.16$13.41Vast.aiMarketplace · 98.3% reliableRent on Vast.ai
4× B200720 GB~764 tok/sFaster than you read$25.00$9.09Vast.aiMarketplace · 99.6% reliableRent on Vast.ai
8× H2001128 GBrecommendedbest valuefastest~952 tok/sFaster than you read$26.52$7.73Vast.aiMarketplace · 99.3% reliableRent 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 MiMo-V2.6-Pro-RL 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 MiMo-V2.6-Pro-RL 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 2026-09-22 at pinned commit 73875d00b30a; the native packed/fused checkpoint metadata is not used as a logical parameter total.

Architecture
mimo_v2
Layers
70
Hidden size
6,144
Attention heads
128
KV heads
8
Head dimension
192
Feed-forward width
16,384
Vocabulary
152,576
Context ceiling
1,048,576
Sliding window
128
RoPE theta
10,000,000
Experts
384
Experts / token
8
Expert width
2,048

Where the memory goes

tokenembedding× 70 decoder blocksGrouped-query attention128 query · 8 KV headswindow 128Routed experts8 of 384 per tokenall residentoutputprojectiongrows with contextcapacity ≠ traffic

Grouped-query attention shares each key/value head across 16 query heads, so the KV cache is 6% of what multi-head attention would need at the same context.

This is a multimodal checkpoint (vision + audio); 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 XiaomiMiMo

MiMo-V2.5MiMo-V2.5-ProMiMo-V2.6-Distill-Qwen-9BMiMo-V2.6-Flash-MOPDMiMo-V2.6-Flash-RLMiMo-V2.6-Pro-MOPDMiMo-V2-Flash

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  -H "Authorization: Bearer $LLMB_KEY" -H "content-type: application/json" \
  -d '{"model":"xiaomimimo-mimo-v2-6-pro-rl","quantization":"Q4_K_M","context":8192,"hardware":"nvidia-geforce-rtx-4090"}'

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  data-model="xiaomimimo-mimo-v2-6-pro-rl" data-quantization="Q4_K_M"
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Citing this page

LLM Bottleneck. “MiMo-V2.6-Pro-RL VRAM and hardware requirements.” Architecture from XiaomiMiMo/MiMo-V2.6-Pro-RL at revision 73875d00b30a, retrieved 2026-09-22. https://llmbottleneck.com/models/xiaomimimo-mimo-v2-6-pro-rl

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