Qwen2 72B Instruct — Hardware Requirements & GPU Compatibility
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Qwen2-72B-Instruct is the instruction-tuned, chat-ready 72.7-billion-parameter model from Alibaba's Qwen2 series, built on a Transformer architecture with SwiGLU activation, QKV attention bias, and grouped-query attention, and trained with supervised fine-tuning plus direct preference optimization. It generally surpassed the prior Qwen1.5 line and competed with proprietary models on language understanding, generation, multilingual tasks, coding, and math benchmarks at release, though it has since been superseded by Qwen2.5-72B-Instruct. At 72.7 billion parameters, it needs a multi-GPU workstation to run in full precision. Context length is natively 32,768 tokens, extendable to 131,072 tokens using YaRN scaling, as documented on the model card. It is released under Alibaba's Tongyi Qianwen custom license, which permits commercial use but requires a separate license once a deployment passes 100 million monthly active users. It was published in May 2024.
22.6K downloads
719 likesSpecifications
- Publisher
- Alibaba
- Family
- Qwen 2
- Parameters
- 72.7B
- Architecture
- Qwen2ForCausalLM
- Context Length
- 32,768 tokens
- Vocabulary Size
- 152,064
- Release Date
- 2024-05-28
- License
- Other
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HuggingFace
How Much VRAM Does Qwen2 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_Mest. | 5.70 | 52.8 GB | 62.8 GB | 51.80 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_Kest. | 6.60 | 61.0 GB | 71.0 GB | 59.98 GB | 6-bit quantization, very good quality |
| Q8_0est. | 8.00 | 73.7 GB | 83.7 GB | 72.71 GB | 8-bit quantization, near-lossless |
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 Qwen2 72B Instruct?
Q4_K_M · 44.6 GBQwen2 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 72B Instruct?
Q4_K_M · 44.6 GB26 devices with unified memory can run Qwen2 72B Instruct, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, Mac Studio M4 Max (64 GB).
Runs great
— Plenty of headroom
Where to Download Qwen2 72B Instruct
Community quantizations of this model — GGUF for llama.cpp, Ollama, and LM Studio, plus AWQ/MLX variants where available.
Benchmarks
Benchmark details →Related Models
Frequently Asked Questions
- How much VRAM does Qwen2 72B Instruct need?
Qwen2 72B Instruct requires 44.6 GB of VRAM at Q4_K_M, or 146.4 GB at BF16. 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 72B Instruct?
Yes, at IQ2_S (23.7 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 72B Instruct?
For Qwen2 72B Instruct, Q4_K_M (44.6 GB) offers the best balance of quality and VRAM usage. Q5_K_M (52.8 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 GBIQ3_XS31.0 GBQ3_K_M36.4 GBQ4_K_S41.9 GBQ4_K_M ★44.6 GBBF16146.4 GB★ Recommended — best balance of quality and VRAM usage.
- Can I run Qwen2 72B Instruct on a Mac?
Qwen2 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 72B Instruct locally?
Yes — Qwen2 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 72B Instruct?
At Q4_K_M, Qwen2 72B Instruct can reach ~108 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 ÷ 44.6 × 0.65 = ~117 tok/s
Estimated speed at Q4_K_M (44.6 GB)
~117 tok/s~117 tok/s~108 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of Qwen2 72B Instruct?
At Q4_K_M, the download is about 43.62 GB. The full-precision BF16 version is 145.41 GB. The smallest option (IQ2_XXS) is 19.99 GB.
- Which GPUs can run Qwen2 72B Instruct?
No single consumer GPU has enough VRAM to run Qwen2 72B Instruct at Q4_K_M (44.6 GB). Multi-GPU or professional hardware is required.
- Which devices can run Qwen2 72B Instruct?
27 devices with unified memory can run Qwen2 72B Instruct at Q4_K_M (44.6 GB), including ASUS Ascent GX10, Asus ROG Flow Z13 (2025, Ryzen AI Max+ 395, 128 GB), 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.