MiroThinker 1.7 — Hardware Requirements & GPU Compatibility
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MiroThinker 1.7 is a 235.1B-parameter open language model from miromind-ai. It supports a context window of up to 262,144 tokens. At Q4_K_M it needs about 141.55 GB of VRAM — see which GPUs and Macs can run it below.
158 downloads
59 likesSpecifications
- Publisher
- miromind-ai
- Parameters
- 235.1B
- Architecture
- Qwen3MoeForCausalLM
- Context Length
- 262,144 tokens
- Vocabulary Size
- 151,936
- Release Date
- 2026-03-09
- License
- Apache 2.0
Get Started
HuggingFace
How Much VRAM Does MiroThinker 1.7 Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q2_Kest. | 3.40 | 100.4 GB | 125.5 GB | 99.91 GB | 2-bit quantization with K-quant improvements |
| Q3_K_Mest. | 3.90 | 115.1 GB | 140.1 GB | 114.61 GB | 3-bit medium quantization |
| Q4_K_Mest. | 4.80 | 141.6 GB | 166.6 GB | 141.06 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_Mest. | 5.70 | 168 GB | 193.0 GB | 167.50 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_Kest. | 6.60 | 194.4 GB | 219.5 GB | 193.95 GB | 6-bit quantization, very good quality |
| Q8_0est. | 8.00 | 235.6 GB | 260.6 GB | 235.09 GB | 8-bit quantization, near-lossless |
| BF16est. | 16.00 | 470.7 GB | 495.7 GB | 470.19 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 MiroThinker 1.7?
Q4_K_M · 141.6 GBMiroThinker 1.7 (Q4_K_M) requires 141.6 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 185+ GB is recommended. Using the full 262K context window can add up to 25.0 GB, bringing total usage to 166.6 GB. No single GPU has enough memory — multi-GPU or cluster setups are needed.
Which Devices Can Run MiroThinker 1.7?
Q4_K_M · 141.6 GB6 devices with unified memory can run MiroThinker 1.7, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, Mac Studio (M3 Ultra, 256GB).
Runs great
— Plenty of headroom
Decent
— Enough memory, may be tightRelated Models
Frequently Asked Questions
- How much VRAM does MiroThinker 1.7 need?
MiroThinker 1.7 requires 141.6 GB of VRAM at Q4_K_M, or 470.7 GB at BF16. Full 262K context adds up to 25.0 GB (166.6 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 235.1B × 4.8 bits ÷ 8 = 141.1 GB
KV Cache + Overhead ≈ 0.5 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 25.5 GB (at full 262K context)
VRAM usage by quantization
Q4_K_M141.6 GBQ4_K_M + full context166.6 GB- Can NVIDIA GeForce RTX 5090 run MiroThinker 1.7?
No — MiroThinker 1.7 requires at least 100.4 GB at Q2_K, which exceeds the NVIDIA GeForce RTX 5090's 32 GB of VRAM.
- What's the best quantization for MiroThinker 1.7?
For MiroThinker 1.7, Q4_K_M (141.6 GB) offers the best balance of quality and VRAM usage. Q5_K_M (168 GB) provides better quality if you have the VRAM. The smallest option is Q2_K at 100.4 GB.
VRAM requirement by quantization
Q2_K100.4 GBQ4_K_M ★141.6 GBQ5_K_M168.0 GBQ6_K194.4 GBQ8_0235.6 GBBF16470.7 GB★ Recommended — best balance of quality and VRAM usage.
- Can I run MiroThinker 1.7 on a Mac?
MiroThinker 1.7 requires at least 100.4 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 MiroThinker 1.7 locally?
Yes — MiroThinker 1.7 can run locally on consumer hardware. At Q4_K_M quantization it needs 141.6 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is MiroThinker 1.7?
At Q4_K_M, MiroThinker 1.7 can reach ~31 tok/s on AMD Instinct MI350X. 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 ÷ 141.6 × 0.65 = ~37 tok/s
Estimated speed at Q4_K_M (141.6 GB)
~37 tok/s~37 tok/s~31 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of MiroThinker 1.7?
At Q4_K_M, the download is about 141.06 GB. The full-precision BF16 version is 470.19 GB. The smallest option (Q2_K) is 99.91 GB.
- Which GPUs can run MiroThinker 1.7?
No single consumer GPU has enough VRAM to run MiroThinker 1.7 at Q4_K_M (141.6 GB). Multi-GPU or professional hardware is required.
- Which devices can run MiroThinker 1.7?
6 devices with unified memory can run MiroThinker 1.7 at Q4_K_M (141.6 GB), including Mac Pro M2 Ultra (192 GB), Mac Studio (M3 Ultra, 256GB), Mac Studio (M3 Ultra, 512GB), Mac Studio M2 Ultra (192 GB). Apple Silicon Macs use unified memory shared between CPU and GPU, making them well-suited for local LLM inference.