MiniCPM MoE 8x2B — Hardware Requirements & GPU Compatibility
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MiniCPM MoE 8x2B is a 8x2B-parameter open language model from OpenBMB in the MiniCPM family. It supports a context window of up to 4,096 tokens. At Q4_K_M it needs about 10.65 GB of VRAM — see which GPUs and Macs can run it below.
Runs great on a 24 GB GPU
Runs from 12 GB (Q4_K_M · 4K)Best on 24 GB (Q4_K_M · ~114 tok/s · 4K)Macs from 16 GB (Q4_K_M · ~26 tok/s)
Reference GPU · RTX 4090 24 GB
Estimates, ±20% · how we estimate
Run MiniCPM MoE 8x2B
One recommended way. Every other method, OS and quant is in the step-by-step guide.
Recommended
vllm serve openbmb/MiniCPM-MoE-8x2B --max-model-len 4096vLLM repo with its own quantisation
Download: Weights on Hugging Face
Other ways
each opens step-by-stepRun in cloud
Fits on RTX 5060 Ti 16GB (4 GB headroom) · Q4_K_M
- Generation speed
- ~59 tok/s
- generation speed
- Cost per 1M output tokens
- $0.77
- per 1M output tokens
How Much VRAM Does MiniCPM MoE 8x2B Need?
Select a quantization level; the listed hardware adheres to it.
| Quantization | VRAM | +32K | On RTX 4090 (reference) | |
|---|---|---|---|---|
| 7.8 GB | — | 2-bit · K-quant — 2-bit quantization with K-quant improvements | Fits: 4K context · ~142 tok/s | |
| 8.8 GB | — | 3-bit medium — 3-bit medium quantization | Fits: 4K context · ~131 tok/s | |
| 10.7 GB | — | 4-bit medium — 4-bit medium quantization — most popular sweet spot | Fits: 4K context · ~114 tok/s | |
| 12.4 GB | — | 5-bit medium — 5-bit medium quantization — good quality/size tradeoff | Fits: 4K context · ~102 tok/s | |
| 14.3 GB | — | 6-bit, very good — 6-bit quantization, very good quality | Fits: 4K context · ~91 tok/s | |
| 17.1 GB | — | 8-bit, near-lossless — 8-bit quantization, near-lossless | Fits: 4K context · ~79 tok/s | |
| 33.0 GB | — | 16-bit · bfloat — Brain floating point 16 — preferred for training | Does not fit: needs 33.1 GB |
Selected Q4_K_M
4-bit medium quantization11.4GBat 4K context
- Weights
- 9.6 GB
- KV cache
- 1.5 GB
- Overhead
- 0.3 GB
Fits RTX 4090 (reference) · 12.6 GB free · ~114 tok/s
≥50 tok/s needs ≥370 GB/s · RTX 5090 ~170, RTX 3090 Ti ~114
Which Hardware Can Run MiniCPM MoE 8x2B?
Q4_K_M · 10.7 GBMiniCPM MoE 8x2B (Q4_K_M) requires 10.7 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 14+ GB is recommended. Using the full 4K context window can add up to 0.8 GB, bringing total usage to 11.4 GB. 38 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti, NVIDIA GeForce RTX 3080 Ti. 150 devices with unified memory can run MiniCPM MoE 8x2B, including Mac Studio (M3 Ultra, 256GB), Mac Studio (M3 Ultra, 512GB), Mac Mini M2 Pro (16 GB).
12 GB
12 GB tier →fits · 0.6 GB free · up to 4K ctx
10 GPUs
RTX 3080 Ti~106Simulate output on RTX 3080 TiRTX 5070~83Simulate output on RTX 5070RTX 4070~65Simulate output on RTX 407020 GB
fits · 8.6 GB free · up to 4K ctx
1 GPU
RX 7900 XT~59Simulate output on RX 7900 XT32–40 GB
fits · 20.6 GB free · up to 4K ctx
1 GPU · 3 Mac classes · 4 device families
RTX 5090~170Simulate output on RTX 5090M5 Max36 GB~47Simulate output on Mac Studio M5 Max (36 GB)M532 GB~21Simulate output on MacBook Air 13" M5 (32 GB)Orin~29Simulate output on NVIDIA Jetson AGX Orin 32GBSnapdragon X Elite (X1E-80-100)~19Simulate output on Snapdragon X Elite Copilot+ PC
By VRAM tier · typical GPUs and Macs at Q4_K_M
Alternatives to MiniCPM MoE 8x2B
MiniCPM family · 16 models →Similar models
6| Model | Score | VRAM | tok/s | Why it is similar |
|---|---|---|---|---|
| Apriel 1.6 15B ThinkerServiceNow-AI | — | 13.2 GB | ~67 | same size |
| Kimi VL A3B InstructMoonshot AI | — | 10.7 GB | ~208 | same size |
| LLaDA2.1 MiniInclusion AI | — | 33.5 GB | —needs > 24 GB | same size |
| LLaDA2.0 MiniInclusion AI | — | 10.7 GB | ~406 | same size |
| Moonlight 16B A3BMoonshot AI | — | 10.1 GB | ~229 | same size |
| DeepSeek v2 LiteDeepSeek | — | 10.2 GB | ~243 | same size |
Specifications
- Publisher
- OpenBMB
- Family
- MiniCPM
- Parameters
- 8x2B
- Active parameters
- 6.4B per token
- Architecture
- MiniCPMForCausalLM
- Context Length
- 4,096 tokens
- Vocabulary Size
- 122,753
- Release Date
- 2024-04-07
Frequently Asked Questions
How much VRAM does MiniCPM MoE 8x2B need?
MiniCPM MoE 8x2B requires 10.7 GB of VRAM at Q4_K_M, or 33.0 GB at BF16. Full 4K context adds up to 0.8 GB (11.4 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 8x2B × 4.8 bits ÷ 8 = 9.6 GB
KV Cache + Overhead ≈ 1.1 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 ≈ 1.8 GB (at full 4K context)
VRAM usage by quantization
Can NVIDIA GeForce RTX 4090 run MiniCPM MoE 8x2B?
Yes, at Q8_0 (17.1 GB) or lower. Higher quantizations like BF16 (33.0 GB) exceed the NVIDIA GeForce RTX 4090's 24 GB.
What's the best quantization for MiniCPM MoE 8x2B?
For MiniCPM MoE 8x2B, Q4_K_M (10.7 GB) offers the best balance of quality and VRAM usage. Q5_K_M (12.4 GB) provides better quality if you have the VRAM. The smallest option is Q2_K at 7.8 GB.
VRAM requirement by quantization
★ Recommended — best balance of quality and VRAM usage.
Can I run MiniCPM MoE 8x2B on a Mac?
Yes — Mac Mini M2 Pro (16 GB) and 133 other Macs can run MiniCPM MoE 8x2B. Apple Silicon uses unified memory, so the model shares RAM with the system. At Q4_K_M you need at least 10.7 GB of usable unified memory (RAM minus macOS overhead).
Can I run MiniCPM MoE 8x2B locally?
Yes — MiniCPM MoE 8x2B can run locally on consumer hardware. Popular tools include Ollama, LM Studio, and llama.cpp.
How fast is MiniCPM MoE 8x2B?
At Q4_K_M, MiniCPM MoE 8x2B can reach ~112 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~114 tok/s. Speed depends mainly on GPU memory bandwidth. Real-world results typically within ±20%.
tok/s = 1000 ÷ (active GB ÷ (bandwidth GB/s × efficiency) × 1000 + layers × routing ms)
Mixture-of-Experts: just the active experts get read for each token, along with a fixed routing cost per layer.
Example: NVIDIA B200 → 4.3 GB active ÷ (8000 × 0.65) = 0.82 ms, plus 40 layers × 0.055 ms = 2.20 ms, so 1000 ÷ 3.02 ms = ~331 tok/s
Estimated speed at Q4_K_M (10.7 GB)
Real-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
What's the download size of MiniCPM MoE 8x2B?
At Q4_K_M, the download is about 9.60 GB. The full-precision BF16 version is 32.00 GB. The smallest option (Q2_K) is 6.80 GB.
Which GPUs can run MiniCPM MoE 8x2B?
38 consumer GPUs can run MiniCPM MoE 8x2B at Q4_K_M. Top options include AMD Radeon RX 6800, AMD Radeon RX 6800 XT, AMD Radeon RX 6900 XT, AMD Radeon RX 6700 XT. 26 GPUs have plenty of headroom for comfortable inference.
Which devices can run MiniCPM MoE 8x2B?
150 devices with unified memory can run MiniCPM MoE 8x2B at Q4_K_M (10.7 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.