Qwen2.5 1.5B Instruct — Hardware Requirements & GPU Compatibility
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Qwen2.5 1.5B Instruct is a 1.5-billion parameter instruction-tuned model from Alibaba Cloud's Qwen 2.5 series. It is a lightweight model suitable for deployment on minimal hardware, including low-VRAM GPUs and even CPU-only setups with acceptable latency. It supports a 128K token context window. The model handles basic conversational tasks, simple question answering, and text generation. While limited in reasoning depth compared to larger variants, it is useful for applications where fast response times and minimal resource consumption are priorities. Released under the Apache 2.0 license.
10.7M downloads
737 likesSpecifications
- Publisher
- Alibaba
- Family
- Qwen 2.5
- Parameters
- 1.5B
- Architecture
- Qwen2ForCausalLM
- Context Length
- 32,768 tokens
- Vocabulary Size
- 151,936
- Release Date
- 2024-09-17
- License
- Apache 2.0
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HuggingFace
How Much VRAM Does Qwen2.5 1.5B Instruct Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q2_K | 3.40 | 1.0 GB | 1.9 GB | 0.66 GB | 2-bit quantization with K-quant improvements |
| Q3_K_S | 3.50 | 1.0 GB | 1.9 GB | 0.68 GB | 3-bit small quantization |
| Q3_K_M | 3.90 | 1.1 GB | 2.0 GB | 0.75 GB | 3-bit medium quantization |
| Q4_0 | 4.00 | 1.1 GB | 2.0 GB | 0.77 GB | 4-bit legacy quantization |
| Q4_K_M | 4.80 | 1.3 GB | 2.2 GB | 0.93 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_M | 5.70 | 1.5 GB | 2.3 GB | 1.10 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_K | 6.60 | 1.6 GB | 2.5 GB | 1.27 GB | 6-bit quantization, very good quality |
| Q8_0 | 8.00 | 1.9 GB | 2.8 GB | 1.54 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 Qwen2.5 1.5B Instruct?
Q4_K_M · 1.3 GBQwen2.5 1.5B Instruct (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 33K context window can add up to 0.9 GB, bringing total usage to 2.2 GB. 50 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti.
Runs great
— Plenty of headroomWhich Devices Can Run Qwen2.5 1.5B Instruct?
Q4_K_M · 1.3 GB59 devices with unified memory can run Qwen2.5 1.5B Instruct, including NVIDIA DGX H100, NVIDIA DGX A100 640GB.
Runs great
— Plenty of headroomWhere to Download Qwen2.5 1.5B Instruct
Community quantizations of this model — GGUF for llama.cpp, Ollama, and LM Studio, plus AWQ/MLX variants where available.
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Frequently Asked Questions
- How much VRAM does Qwen2.5 1.5B Instruct need?
Qwen2.5 1.5B Instruct requires 1.3 GB of VRAM at Q4_K_M, or 3.5 GB at BF16. Full 33K context adds up to 0.9 GB (2.2 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 ≈ 1.3 GB (at full 33K context)
VRAM usage by quantization
Q4_K_M1.3 GBQ4_K_M + full context2.2 GB- What's the best quantization for Qwen2.5 1.5B Instruct?
For Qwen2.5 1.5B Instruct, Q4_K_M (1.3 GB) offers the best balance of quality and VRAM usage. Q4_K_L (1.3 GB) provides better quality if you have the VRAM. The smallest option is IQ2_XS at 0.8 GB.
VRAM requirement by quantization
IQ2_XS0.8 GBIQ3_M1.1 GBQ4_K_S1.2 GBQ4_K_M ★1.3 GBQ5_K_S1.4 GBBF163.5 GB★ Recommended — best balance of quality and VRAM usage.
- Can I run Qwen2.5 1.5B Instruct on a Mac?
Qwen2.5 1.5B Instruct requires at least 0.8 GB at IQ2_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 1.5B Instruct locally?
Yes — Qwen2.5 1.5B Instruct 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 Qwen2.5 1.5B Instruct?
At Q4_K_M, Qwen2.5 1.5B Instruct 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 Qwen2.5 1.5B Instruct?
At Q4_K_M, the download is about 0.93 GB. The full-precision BF16 version is 3.09 GB. The smallest option (IQ2_XS) is 0.46 GB.
- Which GPUs can run Qwen2.5 1.5B Instruct?
50 consumer GPUs can run Qwen2.5 1.5B Instruct 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 Qwen2.5 1.5B Instruct?
59 devices with unified memory can run Qwen2.5 1.5B Instruct 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.