MiMo 7B Base — Hardware Requirements & GPU Compatibility
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MiMo 7B Base is a 7.8B-parameter open language model from Xiaomi. It supports a context window of up to 32,768 tokens. At Q4_K_M it needs about 5.30 GB of VRAM — see which GPUs and Macs can run it below.
Runs great on a 24 GB GPU
Runs from 8 GB (Q4_K_M · 16K)Best on 24 GB (Q4_K_M · ~124 tok/s · 32K)Macs from 16 GB (Q4_K_M · ~26 tok/s)
MIT
Reference GPU · RTX 4090 24 GB
Estimates, ±20% · how we estimate
Run MiMo 7B Base
One recommended way. Every other method, OS and quant is in the step-by-step guide.
Recommended
vllm serve XiaomiMiMo/MiMo-7B-Base --max-model-len 16384vLLM repo with its own quantisation
Download: Weights on Hugging Face
Other ways
each opens step-by-stepRun in cloud
Fits on RTX 3060 12GB (4 GB headroom) · Q4_K_M
- Generation speed
- ~44 tok/s
- generation speed
- Cost per 1M output tokens
- $0.52
- per 1M output tokens
How Much VRAM Does MiMo 7B Base Need?
Select a quantization level; the listed hardware adheres to it.
| Quantization | VRAM | +32K | On RTX 4090 (reference) | |
|---|---|---|---|---|
| 3.9 GB | 8.5 GB | 2-bit · K-quant — 2-bit quantization with K-quant improvements | Fits: 32K context · ~167 tok/s | |
| 4.4 GB | 9 GB | 3-bit medium — 3-bit medium quantization | Fits: 32K context · ~148 tok/s | |
| 5.3 GB | 9.8 GB | 4-bit medium — 4-bit medium quantization — most popular sweet spot | Fits: 32K context · ~124 tok/s | |
| 6.2 GB | 10.7 GB | 5-bit medium — 5-bit medium quantization — good quality/size tradeoff | Fits: 32K context · ~106 tok/s | |
| 7.1 GB | 11.6 GB | 6-bit, very good — 6-bit quantization, very good quality | Fits: 32K context · ~93 tok/s | |
| 8.4 GB | 13 GB | 8-bit, near-lossless — 8-bit quantization, near-lossless | Fits: 32K context · ~78 tok/s | |
| 16.3 GB | 20.8 GB | 16-bit · bfloat — Brain floating point 16 — preferred for training | Fits: 32K context · ~40 tok/s |
Selected Q4_K_M
4-bit medium quantization7.4GBat 16K context
- Weights
- 4.7 GB
- KV cache
- 2.4 GB
- Overhead
- 0.3 GB
Fits RTX 4090 (reference) · 16.6 GB free · ~124 tok/s
≥50 tok/s needs ≥407 GB/s · RTX 5090 ~220, RTX 3090 Ti ~124
Which Hardware Can Run MiMo 7B Base?
Q4_K_M · 5.3 GBMiMo 7B Base (Q4_K_M) requires 5.3 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 7+ GB is recommended. Using the full 33K context window can add up to 4.5 GB, bringing total usage to 9.8 GB. 52 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti. 151 devices with unified memory can run MiMo 7B Base, including Mac Studio (M3 Ultra, 256GB), Mac Studio (M3 Ultra, 512GB).
≤ 11 GB
≤ 11 GB tier →fits · 0.6 GB free · up to 16K ctx
16 GPUs · 1 device family
RTX 3080~93Simulate output on RTX 3080RTX 2080 Ti~76Simulate output on RTX 2080 TiRTX 3070 Ti~75Simulate output on RTX 3070 TiOrin Nano~13Simulate output on NVIDIA Jetson Orin Nano 8GB (Super)20 GB
fits · 12.6 GB free · up to 32K ctx
1 GPU
RX 7900 XT~91Simulate output on RX 7900 XT32–40 GB
fits · 24.6 GB free · up to 32K ctx
1 GPU · 3 Mac classes · 4 device families
RTX 5090~220Simulate output on RTX 5090M5 Max36 GB~61Simulate output on Mac Studio M5 Max (36 GB)M532 GB~20Simulate output on MacBook Air 13" M5 (32 GB)Orin~25Simulate output on NVIDIA Jetson AGX Orin 32GBSnapdragon X Elite (X1E-80-100)~17Simulate output on Snapdragon X Elite Copilot+ PC
By VRAM tier · typical GPUs and Macs at Q4_K_M
How good is MiMo 7B Base?
llmrun Score blends public benchmark boards into one number; 100 = the average open model.
Not enough data yet for an llmrun Score.
A Score needs results on enough independent boards. How the Composite Score is built
Benchmarks
This is the single board showing results for this model; badges show who ran the test.
- MMLU-Pro
TIGER-LabIndependent
Independent41.9%
#96/145 open
Similar models
6| Model | Score | VRAM | tok/s | Why it is similar |
|---|---|---|---|---|
| Ling 3.0 TinyInclusion AI | — | 5.5 GB | ~364 | same size |
| BitCPM CANN 8BOpenBMB | — | 5.6 GB | ~127 | same size |
| Gemma 4 E4BGoogle | — | 5.6 GB | ~123 | same size |
| Olmo 3 7B InstructAllen AI | — | 8.4 GB | ~114 | same size |
| Phi Mini MoE InstructMicrosoft | — | 5.4 GB | ~239 | same size |
| Gemma 3n E4B ITGoogle | — | 7.7 GB | ~127 | same size |
Specifications
- Publisher
- Xiaomi
- Parameters
- 7.8B
- Architecture
- MiMoForCausalLM
- Context Length
- 32,768 tokens
- Vocabulary Size
- 151,680
- Release Date
- 2025-04-29
- License
- MIT
Frequently Asked Questions
How much VRAM does MiMo 7B Base need?
MiMo 7B Base requires 5.3 GB of VRAM at Q4_K_M, or 16.3 GB at BF16. Full 33K context adds up to 4.5 GB (9.8 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 7.8B × 4.8 bits ÷ 8 = 4.7 GB
KV Cache + Overhead ≈ 0.6 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 ≈ 5.1 GB (at full 33K context)
VRAM usage by quantization
What's the best quantization for MiMo 7B Base?
For MiMo 7B Base, Q4_K_M (5.3 GB) offers the best balance of quality and VRAM usage. Q5_K_M (6.2 GB) provides better quality if you have the VRAM. The smallest option is Q2_K at 3.9 GB.
VRAM requirement by quantization
★ Recommended — best balance of quality and VRAM usage.
Can I run MiMo 7B Base on a Mac?
Yes — Mac Mini M2 Pro (16 GB) and 133 other Macs can run MiMo 7B Base. Apple Silicon uses unified memory, so the model shares RAM with the system. At Q4_K_M you need at least 5.3 GB of usable unified memory (RAM minus macOS overhead).
Can I run MiMo 7B Base locally?
Yes — MiMo 7B Base can run locally on consumer hardware. Popular tools include Ollama, LM Studio, and llama.cpp.
How fast is MiMo 7B Base?
At Q4_K_M, MiMo 7B Base can reach ~906 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~124 tok/s. Speed depends mainly on GPU memory bandwidth. Real-world results typically within ±20%.
tok/s = (bandwidth GB/s ÷ model GB) × efficiency
Example: NVIDIA B200 → 8000 ÷ 5.3 × 0.65 = ~981 tok/s
Estimated speed at Q4_K_M (5.3 GB)
Real-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
What's the download size of MiMo 7B Base?
At Q4_K_M, the download is about 4.70 GB. The full-precision BF16 version is 15.67 GB. The smallest option (Q2_K) is 3.33 GB.
Which GPUs can run MiMo 7B Base?
52 consumer GPUs can run MiMo 7B Base at Q4_K_M. Top options include AMD Radeon RX 6700 XT, AMD Radeon RX 6800, AMD Radeon RX 6800 XT. 52 GPUs have plenty of headroom for comfortable inference.
Which devices can run MiMo 7B Base?
151 devices with unified memory can run MiMo 7B Base at Q4_K_M (5.3 GB), including AMD Ryzen AI 9 HX 370 (Strix Point) Laptop, ASUS Ascent GX10, Beelink GTR9 Pro (Ryzen AI Max+ 395, 128 GB), Framework Desktop (Ryzen AI Max+ 395, 128 GB). Apple Silicon Macs use unified memory shared between CPU and GPU, making them well-suited for local LLM inference.