Qwen2 1.5B — Hardware Requirements & GPU Compatibility
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Qwen2 1.5B is a 1.5-billion parameter base (pretrained) model from Alibaba Cloud's older Qwen 2 generation. It was trained on a multilingual corpus and supports a context window of up to 32K tokens. As a base model, it is designed for fine-tuning and research rather than direct conversational use. While superseded by the Qwen 2.5 series in terms of training data quality and benchmark performance, Qwen2 1.5B remains a lightweight option for experimentation and as a baseline for comparison. Released under the Apache 2.0 license.
108.4K downloads
100 likesSpecifications
- Publisher
- Alibaba
- Family
- Qwen 2
- Parameters
- 1.5B
- Architecture
- Qwen2ForCausalLM
- Context Length
- 131,072 tokens
- Vocabulary Size
- 151,936
- Release Date
- 2024-06-06
- License
- Apache 2.0
Get Started
HuggingFace
How Much VRAM Does Qwen2 1.5B Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| BF16 | 16.00 | 3.5 GB | 7.2 GB | 3.09 GB | Brain floating point 16 — preferred for training |
Which GPUs Can Run Qwen2 1.5B?
BF16 · 3.5 GBQwen2 1.5B (BF16) requires 3.5 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 5+ GB is recommended. Using the full 131K context window can add up to 3.7 GB, bringing total usage to 7.2 GB. 35 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti.
Runs great
— Plenty of headroomWhich Devices Can Run Qwen2 1.5B?
BF16 · 3.5 GB33 devices with unified memory can run Qwen2 1.5B, including NVIDIA DGX H100, NVIDIA DGX A100 640GB.
Runs great
— Plenty of headroomRelated Models
Frequently Asked Questions
- How much VRAM does Qwen2 1.5B need?
Qwen2 1.5B requires 3.5 GB of VRAM at BF16. Full 131K context adds up to 3.7 GB (7.2 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 1.5B × 16 bits ÷ 8 = 3.1 GB
KV Cache + Overhead ≈ 0.4 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 4.1 GB (at full 131K context)
VRAM usage by quantization
BF163.5 GBBF16 + full context7.2 GB- Can I run Qwen2 1.5B on a Mac?
Qwen2 1.5B requires at least 3.5 GB at BF16, 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 1.5B locally?
Yes — Qwen2 1.5B can run locally on consumer hardware. At BF16 quantization it needs 3.5 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Qwen2 1.5B?
At BF16, Qwen2 1.5B can reach ~845 tok/s on AMD Instinct MI300X. On NVIDIA GeForce RTX 4090: ~190 tok/s. 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 ÷ 3.5 × 0.55 = ~845 tok/s
Estimated speed at BF16 (3.5 GB)
AMD Instinct MI300X~845 tok/sNVIDIA GeForce RTX 4090~190 tok/sNVIDIA H100 SXM~632 tok/sAMD Instinct MI250X~522 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of Qwen2 1.5B?
At BF16, the download is about 3.09 GB.