Qwen2 1.5B — Hardware Requirements & GPU Compatibility
Chat
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 While superseded by the Qwen 2.5 series in terms of training data quality and benchmark performance, Qwen2 1.5B remains a lightweight option for experimentation and as a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
- 2024-05-31
- 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 |
|---|---|---|---|---|---|
| Q2_Kest. | 3.40 | 1.0 GB | 4.7 GB | 0.66 GB | 2-bit quantization with K-quant improvements |
| Q3_K_Mest. | 3.90 | 1.1 GB | 4.8 GB | 0.75 GB | 3-bit medium quantization |
| Q4_K_Mest. | 4.80 | 1.3 GB | 5.0 GB | 0.93 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_Mest. | 5.70 | 1.5 GB | 5.2 GB | 1.10 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_Kest. | 6.60 | 1.6 GB | 5.3 GB | 1.27 GB | 6-bit quantization, very good quality |
| Q8_0est. | 8.00 | 1.9 GB | 5.6 GB | 1.54 GB | 8-bit quantization, near-lossless |
| BF16est. | 16.00 | 3.5 GB | 7.2 GB | 3.09 GB | Brain floating point 16 — preferred for training |
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 1.5B?
Q4_K_M · 1.3 GBQwen2 1.5B (Q4_K_M) requires 1.3 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 2+ GB is recommended. Using the full 131K context window can add up to 3.7 GB, bringing total usage to 5.0 GB. 50 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?
Q4_K_M · 1.3 GB59 devices with unified memory can run Qwen2 1.5B, including NVIDIA DGX H100, NVIDIA DGX A100 640GB.
Runs great
— Plenty of headroomBenchmarks
Benchmark details →Related Models
Frequently Asked Questions
- How much VRAM does Qwen2 1.5B need?
Qwen2 1.5B requires 1.3 GB of VRAM at Q4_K_M, or 3.5 GB at BF16. Full 131K context adds up to 3.7 GB (5.0 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 1.5B × 4.8 bits ÷ 8 = 0.9 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
Q4_K_M1.3 GBQ4_K_M + full context5.0 GB- What's the best quantization for Qwen2 1.5B?
For Qwen2 1.5B, Q4_K_M (1.3 GB) offers the best balance of quality and VRAM usage. Q5_K_M (1.5 GB) provides better quality if you have the VRAM. The smallest option is Q2_K at 1.0 GB.
VRAM requirement by quantization
Q2_K1.0 GBQ4_K_M ★1.3 GBQ5_K_M1.5 GBQ6_K1.6 GBQ8_01.9 GBBF163.5 GB★ Recommended — best balance of quality and VRAM usage.
- Can I run Qwen2 1.5B on a Mac?
Qwen2 1.5B requires at least 1.0 GB at Q2_K, 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 Q4_K_M quantization it needs 1.3 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Qwen2 1.5B?
At Q4_K_M, Qwen2 1.5B can reach ~3438 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~512 tok/s. 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 ÷ 1.3 × 0.65 = ~4063 tok/s
Estimated speed at Q4_K_M (1.3 GB)
~4063 tok/s~512 tok/s~4063 tok/s~3438 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 Q4_K_M, the download is about 0.93 GB. The full-precision BF16 version is 3.09 GB. The smallest option (Q2_K) is 0.66 GB.
- Which GPUs can run Qwen2 1.5B?
50 consumer GPUs can run Qwen2 1.5B at Q4_K_M (1.3 GB). Top options include AMD Radeon RX 6700 XT, AMD Radeon RX 6800, AMD Radeon RX 6800 XT. 50 GPUs have plenty of headroom for comfortable inference.
- Which devices can run Qwen2 1.5B?
59 devices with unified memory can run Qwen2 1.5B at Q4_K_M (1.3 GB), including AMD Ryzen AI 9 HX 370 (Strix Point) Laptop, ASUS Ascent GX10, Apple iPhone 17 Pro, Asus ROG Flow Z13 (2025, 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.