MiMo V2.6 Pro RL — Hardware Requirements & GPU Compatibility
ChatFunctions
MiMo V2.6 Pro RL is Xiaomi's largest open-weight language model, a sparse mixture-of-experts design with roughly 1.02 trillion total parameters and about 42 billion active per token. It is tuned for chat and function-calling, with heavy reinforcement-learning post-training behind its name. Only the active parameters compute per token, keeping decoding efficient, but the full weight set must fit in memory, putting it in server-class territory best reached through a hosted endpoint. The model supports a 1,048,576 token context window, suited to very long documents or agent sessions. It is released under the MIT license, one of the most permissive open licenses, with no restrictions on commercial use. Published in September 2026, its post-training relied on large-scale, fully asynchronous reinforcement learning (GRPO).
MiMo V2.6 Pro RL runs on a multi-GPU server
Q4_K_M needs ~614.1 GB · about 5× H200 SXM 141 GB · or Mac Studio (M3 Ultra, 512GB) at Q3_K_M (~20 tok/s)
Macs from 512 GB (Q3_K_M · ~20 tok/s)
Score 133.3#3 of 106 open#2 of 102 that fit ≤640 GBMIT
Not set up for it?
Reference GPU · RTX 4090 24 GB
Estimates, ±20% · how we estimate
llmrun Composite Score →
133.3
- 90% range 130.4–136.2
- #3 of 106 open models
- #2 of 102 that fit ≤640 GB
Run MiMo V2.6 Pro RL
One recommended way. Every other method, OS and quant is in the step-by-step guide.Each way opens a step-by-step guide for your OS and quant.
Recommended
vllm serve XiaomiMiMo/MiMo-V2.6-Pro-RL --max-model-len 16384 --tensor-parallel-size 8Size the nodes from the table above
Download: MiMo-V2.6-Pro-RL-Q4_K_M.gguf612.00 GBggml-org
Ways to runOther ways
each opens step-by-stepRun in cloud
Rent 5× H200 SXM · Q4_K_M
- Estimated hourly price
- ~$17.95/hr
- est., whole server
- GPUs
- 5 GPUs
- 705 GB of VRAM
$5+ bonus for new users
How Much VRAM Does MiMo V2.6 Pro RL Need?
Select a quantization level; the listed hardware adheres to it.
| Quantization | VRAM | +32K | Runs on | |
|---|---|---|---|---|
| 434.0 GB | 437.3 GB | 2-bit · K-quant — 2-bit quantization with K-quant improvements | Runs on 7× H100 SXM 80 GB | |
| 497.8 GB | 501.1 GB | 3-bit medium — 3-bit medium quantization | Runs on 7× H100 SXM 80 GB | |
| 510.5 GB | 513.8 GB | 4-bit · legacy — 4-bit legacy quantization | Runs on 8× H100 SXM 80 GB | |
| 612.5 GB | 615.8 GB | 4-bit medium — 4-bit medium quantization — most popular sweet spot | Runs on 5× H200 SXM 141 GB | |
| 727.3 GB | 730.6 GB | 5-bit medium — 5-bit medium quantization — good quality/size tradeoff | Runs on 6× H200 SXM 141 GB | |
| 842.0 GB | 845.3 GB | 6-bit, very good — 6-bit quantization, very good quality | Runs on 7× H200 SXM 141 GB | |
| 1020.5 GB | 1023.8 GB | 8-bit, near-lossless — 8-bit quantization, near-lossless | Runs on 6× B200 192 GB | |
| 2040.5 GB | 2043.8 GB | 16-bit · bfloat — Brain floating point 16 — preferred for training | Runs on 4 nodes of 8× H100 SXM |
Selected Q4_K_M
4-bit medium quantization614.1GBat 16K context
- Weights
- 612.0 GB
- KV cache
- 1.8 GB
- Overhead
- 0.3 GB
Server-class: ~614.1 GB at 16K context · about 5× H200 SXM 141 GB
Which Hardware Can Run MiMo V2.6 Pro RL?
Q4_K_M · 612.5 GBMiMo V2.6 Pro RL (Q4_K_M) requires 612.5 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 797+ GB is recommended. Using the full 1049K context window can add up to 112.5 GB, bringing total usage to 725.0 GB. No consumer GPU has enough memory.
Needs server-class hardware
MiMo V2.6 Pro RL runs on a multi-GPU server · Q4_K_M needs ~614.1 GB
| Hardware | Quant | Memory | Speed | Context | Hardware page | |
|---|---|---|---|---|---|---|
| Mac 512 GB · Mac Studio (M3 Ultra, 512GB) | Q3_K_M | 499.3 GB | ~20 tok/s | 64K ctx | Mac Studio → | Q3_K_M · 499.3 GB · ~20 tok/s · 64K ctx |
| 8× H100 SXM 80 GB · 640 GB | Q4_0 | 512.1 GB | — | 512K ctx | H100 SXM → | Q4_0 · 512.1 GB · 512K ctx |
| 5× H200 SXM 141 GB · 705 GB | Q4_K_M | 614.1 GB | — | 128K ctx | H200 SXM → | Q4_K_M · 614.1 GB · 128K ctx |
| 4× B200 192 GB · 768 GB | Q4_K_M | 614.1 GB | — | 512K ctx | B200 → | Q4_K_M · 614.1 GB · 512K ctx |
Also: NVIDIA DGX A100 640GB · NVIDIA DGX H100
By VRAM tier · typical GPUs and Macs at Q4_K_M
Consumer GPUs (up to 32 GB) and Macs up to 256 GB: too small for this model (needs ≥ 435.6 GB)8 GB tier →12 GB tier →16 GB tier →24 GB tier →Macs →
How good is MiMo V2.6 Pro RL?
The llmrun Score merges 8 public benchmark boards into a single figure, where 100 represents the average for open models.
Score vs VRAM in the ≤640 GB tier
- MiMo V2.6 Pro RL
- Other models
- Best Score for its VRAM
#2 of 102 that fit ≤640 GB
Each dot is a model at the best quant that fits ≤640 GB. The line steps up to the best Score available at each amount of VRAM; a dot below it is beaten by a model that needs no more memory.
Full ≤640 GB ranking →Benchmarks
The 6 boards that weigh most in the llmrun Score, of 14 with a result for this model; badges show who ran the test.
- LMArena Text
LMArena · Oct 2026Arena
Arena1479.7
#3/188 open
- LMArena WebDev
LMArena · Oct 2026Arena
Arena1625.4
#6/55 open
- SciCode
Epoch AI · Sep 2026Lab-reported
Lab-reported60.9%
#1/72 open
- LiveBench 2026 Coding
LiveBench · Oct 2026Independent
Independent81.5
#7/19 open
- LiveBench 2026 Long-Horizon
LiveBench · Oct 2026Independent
Independent47.5
#2/19 open
- LiveBench 2026 Design
LiveBench · Oct 2026Independent
Independent78.5
#3/19 open
In the ≤640 GB class
102 models fit ≤640 GBSame Score, only models that fit ≤640 GB at their best quant: this model's peers by memory need.
- #1
DeepSeek V4.1 Flash 1017DeepSeek
134.4
458.4 GB
- #3
MiMo V2.6 Flash RLXiaomi
131.9
186.5 GB
- #4
GLM 5.3Z.ai
130.1
453.8 GB
- #5
DeepSeek V4.1 FlashDeepSeek
129.4
458.4 GB
- #6
Step 5 PreviewStepFun
128.0
407.5 GB
Alternatives to MiMo V2.6 Pro RL
MiMo family · 8 models →Official models with an llmrun Score, each answering one question; sizes are each model's default quant (usually Q4_K_M) with a 16K-token context.
Runner-up at this size
Muse Spark Open 816B A42B
Meta
- 6.5 lower Score
- 62.1 GB less VRAM
Score 126.8 · 551.9 GB
Fits in less VRAM
DeepSeek V4.1 Flash 1017
DeepSeek
- 1.1 higher Score
- 155.6 GB less VRAM
Score 134.4 · 458.4 GB
Other vendor
GLM 5.3
Z.ai
- 3.2 lower Score
- 160.3 GB less VRAM
Score 130.1 · 453.8 GB
Similar models
10| Model | Score | VRAM | tok/s | Why it is similar |
|---|---|---|---|---|
| DeepSeek V4.1 FlashDeepSeek | 129.4 | 458.4 GB | —needs > 24 GB | similar score |
| DeepSeek V4 Pro 0813DeepSeek | 129.2 | 990.8 GB | —needs > 24 GB | similar score |
| Step 5 PreviewStepFun | 128.0 | 407.5 GB | —needs > 24 GB | similar score |
| DeepSeek V4 Flash 0731DeepSeek | 127.6 | 183 GB | —needs > 24 GB | similar score |
| GLM 5.3 FlashZ.ai | 124.1 | 205.2 GB | —needs > 24 GB | similar score |
| GLM 5.2Z.ai | 122.9 | 453.8 GB | —needs > 24 GB | similar score |
| Kimi K2.6Moonshot AI | 120.9 | 617.6 GB | —needs > 24 GB | similar score |
| Mistral Large 4 1T A52BMistral AI | 118.5 | 629.3 GB | —needs > 24 GB | similar score |
| MiMo V2.5 ProXiaomi | 116.4 | 616 GB | —needs > 24 GB | same size |
| InklingThinking Machines | 115.8 | 575.1 GB | —needs > 24 GB | same size |
Fine-tunes & variants(1)
Specifications
- Publisher
- Xiaomi
- Family
- MiMo
- Parameters
- 1020B
- Active parameters
- 42B per token
- Architecture
- MiMoV2ForCausalLM
- Context Length
- 1,048,576 tokens
- Vocabulary Size
- 152,576
- Release Date
- 2026-09-21
- License
- MIT
Frequently Asked Questions
How much VRAM does MiMo V2.6 Pro RL need?
MiMo V2.6 Pro RL requires 612.5 GB of VRAM at Q4_K_M, or 2040.5 GB at BF16. Full 1049K context adds up to 112.5 GB (725.0 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 1020B × 4.8 bits ÷ 8 = 612 GB
KV Cache + Overhead ≈ 0.5 GB (at 2K context + ~0.3 GB framework)
Fit ratings and hardware model lists check this model with room for a 16K-token context, which needs a little more memory.
KV Cache + Overhead ≈ 113 GB (at full 1049K context)
VRAM usage by quantization
Can NVIDIA GeForce RTX 5090 run MiMo V2.6 Pro RL?
No — MiMo V2.6 Pro RL requires at least 434.0 GB at Q2_K, which exceeds the NVIDIA GeForce RTX 5090's 32 GB of VRAM.
What's the best quantization for MiMo V2.6 Pro RL?
For MiMo V2.6 Pro RL, Q4_K_M (612.5 GB) offers the best balance of quality and VRAM usage. Q5_K_M (727.3 GB) provides better quality if you have the VRAM. The smallest option is Q2_K at 434.0 GB.
VRAM requirement by quantization
★ Recommended — best balance of quality and VRAM usage.
Can I run MiMo V2.6 Pro RL on a Mac?
Yes, but only at lower quantizations. The smallest Mac that can run MiMo V2.6 Pro RL is Mac Studio (M3 Ultra, 512GB) at Q3_K_M; 1 of the 146 Macs we list can run it at some quantization. For Q4_K_M (612.5 GB) you need a Mac with more unified memory.
Can I run MiMo V2.6 Pro RL locally?
Yes — MiMo V2.6 Pro RL can run locally on consumer hardware. Popular tools include Ollama, LM Studio, and llama.cpp.
What's the download size of MiMo V2.6 Pro RL?
At Q4_K_M, the download is about 612.00 GB. The full-precision BF16 version is 2040.00 GB. The smallest option (Q2_K) is 433.50 GB.
Which GPUs can run MiMo V2.6 Pro RL?
No single consumer GPU has enough VRAM to run MiMo V2.6 Pro RL at Q4_K_M (612.5 GB). Multi-GPU or professional hardware is required.
Which devices can run MiMo V2.6 Pro RL?
5 GB).