Qwen1.5 72B Chat — Hardware Requirements & GPU Compatibility
Chat
Qwen1.5-72B-Chat is Alibaba's instruction-tuned, 72.3-billion-parameter flagship chat model from the Qwen1.5 series, a beta release of the Qwen2 architecture. Like the rest of the family, it offers stable 32K context length, broader multilingual support versus the original Qwen, and no need for custom trust_remote_code, and it was aligned on top of the pretrained base with supervised fine-tuning and direct preference optimization. As the largest dense model in the Qwen1.5 lineup, it was the series' primary competitor to other 70B-class open chat models at release. At 72.3 billion parameters, it needs a multi-GPU workstation or server to run, even once quantized. Context length is 32,768 tokens. It is released under Alibaba's Tongyi Qianwen license, a custom license that is free for most commercial and research use but requires a separate license from Alibaba once a deployment exceeds 100 million monthly active users. It was published in January 2024 and was later superseded by Qwen2-72B-Instruct.
10.7K downloads
218 likesSpecifications
- Publisher
- Alibaba
- Family
- Qwen
- Parameters
- 72.3B
- Architecture
- Qwen2ForCausalLM
- Context Length
- 32,768 tokens
- Vocabulary Size
- 152,064
- Release Date
- 2024-01-30
- License
- Other
Get Started
HuggingFace
How Much VRAM Does Qwen1.5 72B Chat Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q2_K | 3.40 | 36.4 GB | 116.9 GB | 30.72 GB | 2-bit quantization with K-quant improvements |
| Q3_K_S | 3.50 | 37.3 GB | 117.8 GB | 31.63 GB | 3-bit small quantization |
| Q3_K_M | 3.90 | 40.9 GB | 121.4 GB | 35.24 GB | 3-bit medium quantization |
| Q4_K_M | 4.80 | 49.0 GB | 129.6 GB | 43.37 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_Mest. | 5.70 | 57.2 GB | 137.7 GB | 51.51 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_Kest. | 6.60 | 65.3 GB | 145.8 GB | 59.64 GB | 6-bit quantization, very good quality |
| Q8_0est. | 8.00 | 78.0 GB | 158.5 GB | 72.29 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 Qwen1.5 72B Chat?
Q4_K_M · 49.0 GBQwen1.5 72B Chat (Q4_K_M) requires 49.0 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 64+ GB is recommended. Using the full 33K context window can add up to 80.5 GB, bringing total usage to 129.6 GB. No single GPU has enough memory — multi-GPU or cluster setups are needed.
Which Devices Can Run Qwen1.5 72B Chat?
Q4_K_M · 49.0 GB22 devices with unified memory can run Qwen1.5 72B Chat, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, Mac Studio (M3 Ultra, 96GB).
Runs great
— Plenty of headroom
Where to Download Qwen1.5 72B Chat
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 Qwen1.5 72B Chat need?
Qwen1.5 72B Chat requires 49.0 GB of VRAM at Q4_K_M, or 150.2 GB at BF16. Full 33K context adds up to 80.5 GB (129.6 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 72.3B × 4.8 bits ÷ 8 = 43.4 GB
KV Cache + Overhead ≈ 5.6 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 86.2 GB (at full 33K context)
VRAM usage by quantization
Q4_K_M49.0 GBQ4_K_M + full context129.6 GB- Can NVIDIA GeForce RTX 5090 run Qwen1.5 72B Chat?
No — Qwen1.5 72B Chat requires at least 35.5 GB at IQ3_XS, which exceeds the NVIDIA GeForce RTX 5090's 32 GB of VRAM.
- What's the best quantization for Qwen1.5 72B Chat?
For Qwen1.5 72B Chat, Q4_K_M (49.0 GB) offers the best balance of quality and VRAM usage. Q5_K_S (55.4 GB) provides better quality if you have the VRAM. The smallest option is IQ3_XS at 35.5 GB.
VRAM requirement by quantization
IQ3_XS35.5 GBIQ3_M38.2 GBIQ4_XS44.5 GBQ4_K_M ★49.0 GBQ5_K_M57.2 GBBF16150.2 GB★ Recommended — best balance of quality and VRAM usage.
- Can I run Qwen1.5 72B Chat on a Mac?
Qwen1.5 72B Chat requires at least 35.5 GB at IQ3_XS, 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 Qwen1.5 72B Chat locally?
Yes — Qwen1.5 72B Chat can run locally on consumer hardware. At Q4_K_M quantization it needs 49.0 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Qwen1.5 72B Chat?
At Q4_K_M, Qwen1.5 72B Chat can reach ~98 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 ÷ 49.0 × 0.65 = ~106 tok/s
Estimated speed at Q4_K_M (49.0 GB)
~106 tok/s~106 tok/s~98 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of Qwen1.5 72B Chat?
At Q4_K_M, the download is about 43.37 GB. The full-precision BF16 version is 144.58 GB. The smallest option (IQ3_XS) is 29.82 GB.
- Which GPUs can run Qwen1.5 72B Chat?
No single consumer GPU has enough VRAM to run Qwen1.5 72B Chat at Q4_K_M (49.0 GB). Multi-GPU or professional hardware is required.
- Which devices can run Qwen1.5 72B Chat?
23 devices with unified memory can run Qwen1.5 72B Chat at Q4_K_M (49.0 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.