Qwen2.5 1.5B Instruct GGUF — Hardware Requirements & GPU Compatibility
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Qwen2.5 1.5B Instruct is a compact general-purpose language model from Alibaba's Qwen team, offered here in official GGUF format for easy local deployment. With 1.5 billion parameters, it strikes a practical balance between capability and resource efficiency, handling everyday tasks like summarization, Q&A, and light creative writing without demanding a powerful GPU. This model is an excellent entry point for users who want a responsive local assistant on modest hardware. It runs comfortably on most modern laptops and even some higher-end single-board computers, making it one of the most accessible instruction-tuned models in the Qwen2.5 lineup.
340.6K downloads
86 likesSpecifications
- Publisher
- Alibaba
- Family
- Qwen 2.5
- Parameters
- 1.5B
- Release Date
- 2024-09-20
- License
- Apache 2.0
Get Started
HuggingFace
How Much VRAM Does Qwen2.5 1.5B Instruct GGUF Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q2_K | 3.40 | 0.7 GB | — | 0.64 GB | 2-bit quantization with K-quant improvements |
| Q3_K_M | 3.90 | 0.8 GB | — | 0.73 GB | 3-bit medium quantization |
| Q4_0 | 4.00 | 0.8 GB | — | 0.75 GB | 4-bit legacy quantization |
| Q4_K_M | 4.80 | 1.0 GB | — | 0.90 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_0 | 5.00 | 1.0 GB | — | 0.94 GB | 5-bit legacy quantization |
| Q5_K_M | 5.70 | 1.2 GB | — | 1.07 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_K | 6.60 | 1.4 GB | — | 1.24 GB | 6-bit quantization, very good quality |
| Q8_0 | 8.00 | 1.6 GB | — | 1.50 GB | 8-bit quantization, near-lossless |
Which GPUs Can Run Qwen2.5 1.5B Instruct GGUF?
Q4_K_M · 1.0 GBQwen2.5 1.5B Instruct GGUF (Q4_K_M) requires 1.0 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 2+ GB is recommended. 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 1.5B Instruct GGUF?
Q4_K_M · 1.0 GB33 devices with unified memory can run Qwen2.5 1.5B Instruct GGUF, including NVIDIA DGX H100, NVIDIA DGX A100 640GB.
Runs great
— Plenty of headroomRelated Models
Frequently Asked Questions
- How much VRAM does Qwen2.5 1.5B Instruct GGUF need?
Qwen2.5 1.5B Instruct GGUF requires 1.0 GB of VRAM at Q4_K_M, or 1.6 GB at Q8_0.
VRAM = Weights + KV Cache + Overhead
Weights = 1.5B × 4.8 bits ÷ 8 = 0.9 GB
KV Cache + Overhead ≈ 0.1 GB (at 2K context + ~0.3 GB framework)
VRAM usage by quantization
Q4_K_M1.0 GB- What's the best quantization for Qwen2.5 1.5B Instruct GGUF?
For Qwen2.5 1.5B Instruct GGUF, Q4_K_M (1.0 GB) offers the best balance of quality and VRAM usage. Q5_0 (1.0 GB) provides better quality if you have the VRAM. The smallest option is Q2_K at 0.7 GB.
VRAM requirement by quantization
Q2_K0.7 GB~75%Q4_00.8 GB~85%Q4_K_M ★1.0 GB~89%Q5_01.0 GB~90%Q5_K_M1.2 GB~92%Q8_01.6 GB~99%★ Recommended — best balance of quality and VRAM usage.
- Can I run Qwen2.5 1.5B Instruct GGUF on a Mac?
Qwen2.5 1.5B Instruct GGUF requires at least 0.7 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 Qwen2.5 1.5B Instruct GGUF locally?
Yes — Qwen2.5 1.5B Instruct GGUF can run locally on consumer hardware. At Q4_K_M quantization it needs 1.0 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Qwen2.5 1.5B Instruct GGUF?
At Q4_K_M, Qwen2.5 1.5B Instruct GGUF can reach ~2944 tok/s on AMD Instinct MI300X. On NVIDIA GeForce RTX 4090: ~662 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 ÷ 1.0 × 0.55 = ~2944 tok/s
Estimated speed at Q4_K_M (1.0 GB)
AMD Instinct MI300X~2944 tok/sNVIDIA GeForce RTX 4090~662 tok/sNVIDIA H100 SXM~2201 tok/sAMD Instinct MI250X~1820 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 1.5B Instruct GGUF?
At Q4_K_M, the download is about 0.90 GB. The full-precision Q8_0 version is 1.50 GB. The smallest option (Q2_K) is 0.64 GB.