MAI DS R1 — Hardware Requirements & GPU Compatibility
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MAI DS R1 is a 671.0B-parameter open language model from Microsoft. It supports a context window of up to 163,840 tokens. At Q4_K_M it needs about 406.50 GB of VRAM — see which GPUs and Macs can run it below.
219 downloads
305 likesSpecifications
- Publisher
- Microsoft
- Parameters
- 671.0B
- Architecture
- DeepseekV3ForCausalLM
- Context Length
- 163,840 tokens
- Vocabulary Size
- 129,280
- Release Date
- 2025-04-16
- License
- MIT
Get Started
HuggingFace
How Much VRAM Does MAI DS R1 Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q2_Kest. | 3.40 | 289.1 GB | 572.0 GB | 285.19 GB | 2-bit quantization with K-quant improvements |
| Q3_K_Mest. | 3.90 | 331.0 GB | 614.0 GB | 327.13 GB | 3-bit medium quantization |
| Q4_K_Mest. | 4.80 | 406.5 GB | 689.5 GB | 402.62 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_Mest. | 5.70 | 482.0 GB | 765.0 GB | 478.11 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_Kest. | 6.60 | 557.5 GB | 840.5 GB | 553.60 GB | 6-bit quantization, very good quality |
| Q8_0est. | 8.00 | 674.9 GB | 957.9 GB | 671.03 GB | 8-bit quantization, near-lossless |
| BF16est. | 16.00 | 1345.9 GB | 1628.9 GB | 1342.05 GB | Brain floating point 16 — preferred for training |
est.= calculated VRAM estimate; no published GGUF file found for that quantization yet. Other rows are verified against real community uploads.
Which GPUs Can Run MAI DS R1?
Q4_K_M · 406.5 GBMAI DS R1 (Q4_K_M) requires 406.5 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 529+ GB is recommended. Using the full 164K context window can add up to 283.0 GB, bringing total usage to 689.5 GB. No single GPU has enough memory — multi-GPU or cluster setups are needed.
Which Devices Can Run MAI DS R1?
Q4_K_M · 406.5 GB2 devices with unified memory can run MAI DS R1, including NVIDIA DGX H100, NVIDIA DGX A100 640GB.
Runs great
— Plenty of headroomFrequently Asked Questions
- How much VRAM does MAI DS R1 need?
MAI DS R1 requires 406.5 GB of VRAM at Q4_K_M, or 1345.9 GB at BF16. Full 164K context adds up to 283.0 GB (689.5 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 671.0B × 4.8 bits ÷ 8 = 402.6 GB
KV Cache + Overhead ≈ 3.9 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 286.9 GB (at full 164K context)
VRAM usage by quantization
Q4_K_M406.5 GBQ4_K_M + full context689.5 GB- Can NVIDIA GeForce RTX 5090 run MAI DS R1?
No — MAI DS R1 requires at least 289.1 GB at Q2_K, which exceeds the NVIDIA GeForce RTX 5090's 32 GB of VRAM.
- What's the best quantization for MAI DS R1?
For MAI DS R1, Q4_K_M (406.5 GB) offers the best balance of quality and VRAM usage. Q5_K_M (482.0 GB) provides better quality if you have the VRAM. The smallest option is Q2_K at 289.1 GB.
VRAM requirement by quantization
Q2_K289.1 GBQ4_K_M ★406.5 GBQ5_K_M482.0 GBQ6_K557.5 GBQ8_0674.9 GBBF161345.9 GB★ Recommended — best balance of quality and VRAM usage.
- Can I run MAI DS R1 on a Mac?
MAI DS R1 requires at least 289.1 GB at Q2_K, which exceeds the unified memory of most consumer Macs. You would need a Mac Studio or Mac Pro with a high-memory configuration.
- Can I run MAI DS R1 locally?
Yes — MAI DS R1 can run locally on consumer hardware. At Q4_K_M quantization it needs 406.5 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- What's the download size of MAI DS R1?
At Q4_K_M, the download is about 402.62 GB. The full-precision BF16 version is 1342.05 GB. The smallest option (Q2_K) is 285.19 GB.
- Which GPUs can run MAI DS R1?
No single consumer GPU has enough VRAM to run MAI DS R1 at Q4_K_M (406.5 GB). Multi-GPU or professional hardware is required.
- Which devices can run MAI DS R1?
3 devices with unified memory can run MAI DS R1 at Q4_K_M (406.5 GB), including Mac Studio (M3 Ultra, 512GB), NVIDIA DGX A100 640GB, NVIDIA DGX H100. Apple Silicon Macs use unified memory shared between CPU and GPU, making them well-suited for local LLM inference.