Maira 2 — Hardware Requirements & GPU Compatibility
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Maira 2 is a 6.9B-parameter open language model from Microsoft. At BF16 it needs about 15.14 GB of VRAM — see which GPUs and Macs can run it below.
Runs well on a 24 GB GPU
Runs from 16 GB (BF16 · 4K)Best on 24 GB (BF16 · ~43 tok/s · 32K)Macs from 24 GB (BF16 · ~14 tok/s)
Other
Reference GPU · RTX 4090 24 GB
Estimates, ±20% · how we estimate
Run Maira 2
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 microsoft/maira-2 --max-model-len 16384vLLM repo with its own quantisation
Download: Weights on Hugging Face
Ways to runOther ways
each opens step-by-stepRun in cloud
Runs on RTX 3090 (24 GB) (6 GB headroom) · BF16
- Generation speed
- ~40 tok/s
- generation speed
- Cost per 1M output tokens
- $1.26
- per 1M output tokens
How Much VRAM Does Maira 2 Need?
Select a quantization level; the listed hardware adheres to it.
| Quantization | VRAM | +32K | On RTX 4090 (reference) | |
|---|---|---|---|---|
| 15.1 GB | 20.4 GB | 16-bit · bfloat — Brain floating point 16 — preferred for training | Fits: 32K context · ~43 tok/s |
Selected BF16
Brain floating point 1617.6GBat 16K context
- Weights
- 14.5 GB
- KV cache
- 2.8 GB
- Overhead
- 0.3 GB
Fits RTX 4090 (reference) · 6.4 GB free · ~43 tok/s
≥50 tok/s needs ≥1.16 TB/s · RTX 5090 ~77
Which Hardware Can Run Maira 2?
BF16 · 15.1 GBMaira 2 (BF16) requires 15.1 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 20+ GB is recommended. 26 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti, NVIDIA GeForce RTX 5080. 122 devices with unified memory can run Maira 2, including Mac Studio (M3 Ultra, 256GB), Mac Studio (M3 Ultra, 512GB), Mac Mini M5 Pro (24 GB).
20 GB
fits · 2.4 GB free · up to 16K ctx
1 GPU
RX 7900 XT~32Simulate output on RX 7900 XT32–40 GB
fits · 14.4 GB free · up to 32K ctx
1 GPU · 3 Mac classes · 4 device families
RTX 5090~77Simulate output on RTX 5090M5 Max36 GB~21Simulate output on Mac Studio M5 Max (36 GB)M532 GB~7Simulate output on MacBook Air 13" M5 (32 GB)Orin~9Simulate output on NVIDIA Jetson AGX Orin 32GBSnapdragon X Elite (X1E-80-100)~6Simulate output on Snapdragon X Elite Copilot+ PC
By VRAM tier · typical GPUs and Macs at BF16
Similar models
6| Model | Score | VRAM | tok/s | Why it is similar |
|---|---|---|---|---|
| Olmo 3 7B InstructAllen AI | — | 8.4 GB | ~114 | same size |
| Mistral 7B Instruct v0.3Mistral AI | — | 6.8 GB | ~133 | same size |
| CodeQwen1.5 7BAlibaba | — | 5.7 GB | ~137 | same size |
| Llava 1.5 7B HFllava-hf | — | 5 GB | ~141 | same size |
| Baichuan2 7B Basebaichuan-inc | — | 5 GB | ~142 | same size |
| Internlm 7BInternLM | — | 15.4 GB | ~43 | same size |
Frequently Asked Questions
How much VRAM does Maira 2 need?
Maira 2 requires 15.1 GB of VRAM at BF16.
VRAM = Weights + KV Cache + Overhead
Weights = 6.9B × 16 bits ÷ 8 = 13.8 GB
KV Cache + Overhead ≈ 1.3 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.
VRAM usage by quantization
Can I run Maira 2 on a Mac?
Yes — Mac Mini M5 Pro (24 GB) and 106 other Macs can run Maira 2. Apple Silicon uses unified memory, so the model shares RAM with the system. At BF16 you need at least 15.1 GB of usable unified memory (RAM minus macOS overhead).
Can I run Maira 2 locally?
Yes — Maira 2 can run locally on consumer hardware. Popular tools include Ollama, LM Studio, and llama.cpp.
How fast is Maira 2?
At BF16, Maira 2 can reach ~317 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~43 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 ÷ 15.1 × 0.65 = ~344 tok/s
Estimated speed at BF16 (15.1 GB)
Real-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
What's the download size of Maira 2?
At BF16, the download is about 13.76 GB.
Which GPUs can run Maira 2?
26 consumer GPUs can run Maira 2 at BF16. Top options include AMD Radeon RX 7900 XTX, NVIDIA GeForce RTX 3090, NVIDIA GeForce RTX 3090 Ti, AMD Radeon RX 6800. 7 GPUs have plenty of headroom for comfortable inference.
Which devices can run Maira 2?
122 devices with unified memory can run Maira 2 at BF16 (15.1 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.