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 |
|---|---|---|---|---|---|
| IQ2_XXS | 2.20 | 21.0 GB | 31.0 GB | 19.99 GB | Importance-weighted 2-bit, extreme compression — significant quality loss |
| IQ2_XS | 2.40 | 22.8 GB | 32.9 GB | 21.81 GB | Importance-weighted 2-bit, extra small |
| IQ2_M | 2.70 | 25.5 GB | 35.6 GB | 24.54 GB | Importance-weighted 2-bit, medium |
| IQ3_XXS | 3.10 | 29.1 GB | 39.2 GB | 28.17 GB | Importance-weighted 3-bit |
| IQ3_XS | 3.30 | 31.0 GB | 41.0 GB | 29.99 GB | Importance-weighted 3-bit, extra small |
| Q2_K | 3.40 | 31.9 GB | 41.9 GB | 30.90 GB | 2-bit quantization with K-quant improvements |
| IQ3_S | 3.40 | 31.9 GB | 41.9 GB | 30.90 GB | Importance-weighted 3-bit, small |
| Q3_K_S | 3.50 | 32.8 GB | 42.9 GB | 31.81 GB | 3-bit small quantization |
| IQ3_M | 3.60 | 33.7 GB | 43.8 GB | 32.72 GB | Importance-weighted 3-bit, medium |
| 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 |
| Q3_K_L | 4.10 | 38.2 GB | 48.3 GB | 37.26 GB | 3-bit large quantization |
| IQ4_XS | 4.30 | 40.0 GB | 50.1 GB | 39.08 GB | Importance-weighted 4-bit, compact |
| IQ4_NL | 4.50 | 41.9 GB | 51.9 GB | 40.90 GB | Importance-weighted 4-bit, non-linear |
| Q4_K_S | 4.50 | 41.9 GB | 51.9 GB | 40.90 GB | 4-bit small quantization |
| Q4_1 | 4.50 | 41.9 GB | 51.9 GB | 40.90 GB | 4-bit legacy quantization with offset |
| Q4_K_M | 4.80 | 44.6 GB | 54.7 GB | 43.62 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_0 | 5.00 | 46.4 GB | 56.5 GB | 45.44 GB | 5-bit legacy quantization |
| 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 GB~53%Q2_K31.9 GB~75%Q4_037.3 GB~85%Q4_141.9 GB~88%Q4_K_M ★44.6 GB~89%Q8_073.7 GB~99%★ 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)
AMD Instinct MI300X~65 tok/sNVIDIA H100 SXM~49 tok/sAMD Instinct MI250X~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.