Qwen2.5 0.5B — Hardware Requirements & GPU Compatibility
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Qwen2.5 0.5B is the smallest base (pretrained) model in Alibaba Cloud's Qwen 2.5 family, with 494 million parameters. As a base model, it is not instruction-tuned and is intended for fine-tuning, research, and as a foundation for custom applications. It supports a 128K token context window. Its minimal size makes it suitable for experimentation, rapid prototyping, and resource-constrained fine-tuning tasks. The model can run on virtually any hardware. Released under the Apache 2.0 license.
1.5M downloads
381 likesSpecifications
- Publisher
- Alibaba
- Family
- Qwen 2.5
- Parameters
- 494M
- Architecture
- Qwen2ForCausalLM
- Context Length
- 32,768 tokens
- Vocabulary Size
- 151,936
- Release Date
- 2024-09-25
- License
- Apache 2.0
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HuggingFace
How Much VRAM Does Qwen2.5 0.5B Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q3_K_S | 3.50 | 0.5 GB | 0.9 GB | 0.22 GB | 3-bit small quantization |
| Q2_K | 3.40 | 0.5 GB | 0.9 GB | 0.21 GB | 2-bit quantization with K-quant improvements |
| Q3_K_M | 3.90 | 0.6 GB | 0.9 GB | 0.24 GB | 3-bit medium quantization |
| Q4_0 | 4.00 | 0.6 GB | 0.9 GB | 0.25 GB | 4-bit legacy quantization |
| Q4_K_M | 4.80 | 0.6 GB | 1 GB | 0.30 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_M | 5.70 | 0.7 GB | 1.1 GB | 0.35 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_K | 6.60 | 0.7 GB | 1.1 GB | 0.41 GB | 6-bit quantization, very good quality |
| Q8_0 | 8.00 | 0.8 GB | 1.2 GB | 0.49 GB | 8-bit quantization, near-lossless |
Which GPUs Can Run Qwen2.5 0.5B?
Q4_K_M · 0.6 GBQwen2.5 0.5B (Q4_K_M) requires 0.6 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 1+ GB is recommended. Using the full 33K context window can add up to 0.4 GB, bringing total usage to 1 GB. 35 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti.
Runs great
— Plenty of headroomWhich Devices Can Run Qwen2.5 0.5B?
Q4_K_M · 0.6 GB33 devices with unified memory can run Qwen2.5 0.5B, including NVIDIA DGX H100, NVIDIA DGX A100 640GB.
Runs great
— Plenty of headroomRelated Models
Derivatives (3)
Frequently Asked Questions
- How much VRAM does Qwen2.5 0.5B need?
Qwen2.5 0.5B requires 0.6 GB of VRAM at Q4_K_M, or 0.8 GB at Q8_0.
VRAM = Weights + KV Cache + Overhead
Weights = 494M × 4.8 bits ÷ 8 = 0.3 GB
KV Cache + Overhead ≈ 0.3 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 0.7 GB (at full 33K context)
VRAM usage by quantization
Q4_K_M0.6 GBQ4_K_M + full context1.0 GB- What's the best quantization for Qwen2.5 0.5B?
For Qwen2.5 0.5B, Q4_K_M (0.6 GB) offers the best balance of quality and VRAM usage. Q5_0 (0.6 GB) provides better quality if you have the VRAM. The smallest option is IQ3_XS at 0.5 GB.
VRAM requirement by quantization
IQ3_XS0.5 GBQ3_K_M0.6 GBQ4_K_S0.6 GBQ4_K_M ★0.6 GBQ5_10.7 GBQ8_00.8 GB★ Recommended — best balance of quality and VRAM usage.
- Can I run Qwen2.5 0.5B on a Mac?
Qwen2.5 0.5B requires at least 0.5 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 Qwen2.5 0.5B locally?
Yes — Qwen2.5 0.5B can run locally on consumer hardware. At Q4_K_M quantization it needs 0.6 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Qwen2.5 0.5B?
At Q4_K_M, Qwen2.5 0.5B can reach ~4702 tok/s on AMD Instinct MI300X. On NVIDIA GeForce RTX 4090: ~1057 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 ÷ 0.6 × 0.55 = ~4702 tok/s
Estimated speed at Q4_K_M (0.6 GB)
~4702 tok/s~1057 tok/s~3514 tok/s~2907 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 0.5B?
At Q4_K_M, the download is about 0.30 GB. The full-precision Q8_0 version is 0.49 GB. The smallest option (IQ3_XS) is 0.20 GB.
- Which GPUs can run Qwen2.5 0.5B?
35 consumer GPUs can run Qwen2.5 0.5B at Q4_K_M (0.6 GB). Top options include AMD Radeon RX 6700 XT, AMD Radeon RX 6800, AMD Radeon RX 6800 XT. 35 GPUs have plenty of headroom for comfortable inference.
- Which devices can run Qwen2.5 0.5B?
33 devices with unified memory can run Qwen2.5 0.5B at Q4_K_M (0.6 GB), including Mac Mini M4 (16 GB), Mac Mini M4 (32 GB), Mac Mini M4 Pro (24 GB), Mac Mini M4 Pro (48 GB). Apple Silicon Macs use unified memory shared between CPU and GPU, making them well-suited for local LLM inference.