Qwen2.5 72B Instruct — Hardware Requirements & GPU Compatibility
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Qwen2.5 72B Instruct is the flagship model of the Qwen 2.5 series from Alibaba Cloud, with 72.7 billion parameters. It is instruction-tuned for conversational use and excels across reasoning, coding, mathematics, and multilingual tasks. Qwen2.5 72B delivers performance competitive with leading open-weight 70B-class models while supporting a 128K token context window and structured output generation. The model uses a Transformer architecture with grouped-query attention and was pretrained on a diverse multilingual corpus of over 18 trillion tokens. Running it locally requires high-VRAM GPUs or multi-GPU setups, though quantized formats make it accessible on workstation-class hardware. Released under the Apache 2.0 license.
733.3K downloads
917 likesSpecifications
- Publisher
- Alibaba
- Family
- Qwen 2.5
- Parameters
- 72.7B
- Architecture
- Qwen2ForCausalLM
- Context Length
- 32,768 tokens
- Vocabulary Size
- 152,064
- Release Date
- 2025-01-12
- License
- Other
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HuggingFace
How Much VRAM Does Qwen2.5 72B Instruct Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q2_K | 3.40 | 31.9 GB | 41.9 GB | 30.90 GB | 2-bit quantization with K-quant improvements |
| Q3_K_S | 3.50 | 32.8 GB | 42.9 GB | 31.81 GB | 3-bit small quantization |
| Q3_K_M | 3.90 | 36.4 GB | 46.5 GB | 35.44 GB | 3-bit medium quantization |
| Q4_0 | 4.00 | 37.3 GB | 47.4 GB | 36.35 GB | 4-bit legacy quantization |
| Q4_K_M | 4.80 | 44.6 GB | 54.7 GB | 43.62 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_M | 5.70 | 52.8 GB | 62.8 GB | 51.80 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_K | 6.60 | 61.0 GB | 71.0 GB | 59.98 GB | 6-bit quantization, very good quality |
| Q8_0 | 8.00 | 73.7 GB | 83.7 GB | 72.71 GB | 8-bit quantization, near-lossless |
Which GPUs Can Run Qwen2.5 72B Instruct?
Q4_K_M · 44.6 GBQwen2.5 72B Instruct (Q4_K_M) requires 44.6 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 58+ GB is recommended. Using the full 33K context window can add up to 10.1 GB, bringing total usage to 54.7 GB. No single GPU has enough memory — multi-GPU or cluster setups are needed.
Which Devices Can Run Qwen2.5 72B Instruct?
Q4_K_M · 44.6 GB11 devices with unified memory can run Qwen2.5 72B Instruct, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, Mac Studio M4 Max (64 GB).
Runs great
— Plenty of headroom
Related Models
Derivatives (6)
Frequently Asked Questions
- How much VRAM does Qwen2.5 72B Instruct need?
Qwen2.5 72B Instruct requires 44.6 GB of VRAM at Q4_K_M, or 73.7 GB at Q8_0. Full 33K context adds up to 10.1 GB (54.7 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 72.7B × 4.8 bits ÷ 8 = 43.6 GB
KV Cache + Overhead ≈ 1 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 11.1 GB (at full 33K context)
VRAM usage by quantization
Q4_K_M44.6 GBQ4_K_M + full context54.7 GB- Can NVIDIA GeForce RTX 4090 run Qwen2.5 72B Instruct?
Yes, at IQ2_XS (22.8 GB) or lower. Higher quantizations like IQ2_M (25.5 GB) exceed the NVIDIA GeForce RTX 4090's 24 GB.
- What's the best quantization for Qwen2.5 72B Instruct?
For Qwen2.5 72B Instruct, Q4_K_M (44.6 GB) offers the best balance of quality and VRAM usage. Q5_0 (46.4 GB) provides better quality if you have the VRAM. The smallest option is IQ2_XXS at 21.0 GB.
VRAM requirement by quantization
IQ2_XXS21.0 GBQ2_K31.9 GBQ4_037.3 GBQ4_141.9 GBQ4_K_M ★44.6 GBQ8_073.7 GB★ Recommended — best balance of quality and VRAM usage.
- Can I run Qwen2.5 72B Instruct on a Mac?
Qwen2.5 72B Instruct requires at least 21.0 GB at IQ2_XXS, 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 Qwen2.5 72B Instruct locally?
Yes — Qwen2.5 72B Instruct can run locally on consumer hardware. At Q4_K_M quantization it needs 44.6 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Qwen2.5 72B Instruct?
At Q4_K_M, Qwen2.5 72B Instruct can reach ~65 tok/s on AMD Instinct MI300X. Speed depends mainly on GPU memory bandwidth. Real-world results typically within ±20%.
tok/s = (bandwidth GB/s ÷ model GB) × efficiency
Example: AMD Instinct MI300X → 5300 ÷ 44.6 × 0.55 = ~65 tok/s
Estimated speed at Q4_K_M (44.6 GB)
~65 tok/s~49 tok/s~40 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of Qwen2.5 72B Instruct?
At Q4_K_M, the download is about 43.62 GB. The full-precision Q8_0 version is 72.71 GB. The smallest option (IQ2_XXS) is 19.99 GB.
- Which GPUs can run Qwen2.5 72B Instruct?
No single consumer GPU has enough VRAM to run Qwen2.5 72B Instruct at Q4_K_M (44.6 GB). Multi-GPU or professional hardware is required.
- Which devices can run Qwen2.5 72B Instruct?
11 devices with unified memory can run Qwen2.5 72B Instruct at Q4_K_M (44.6 GB), including Mac Mini M4 Pro (48 GB), Mac Pro M2 Ultra (192 GB), Mac Studio M2 Ultra (192 GB), Mac Studio M4 Max (128 GB). Apple Silicon Macs use unified memory shared between CPU and GPU, making them well-suited for local LLM inference.