Qwen2.5 3B Instruct — Hardware Requirements & GPU Compatibility
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Qwen2.5 3B Instruct is a 3.1-billion parameter instruction-tuned model from Alibaba Cloud's Qwen 2.5 family. It is designed for efficient local inference on consumer hardware, supporting a 128K token context window despite its compact footprint. The model can run on GPUs with as little as 4GB of VRAM when quantized. Despite its small size, Qwen2.5 3B Instruct delivers competitive performance for basic conversational tasks, summarization, and simple instruction following. It is a good option for edge deployment and resource-constrained environments. Released under the Apache 2.0 license.
7.0M downloads
415 likesSpecifications
- Publisher
- Alibaba
- Family
- Qwen 2.5
- Parameters
- 3.1B
- Architecture
- Qwen2ForCausalLM
- Context Length
- 32,768 tokens
- Vocabulary Size
- 151,936
- Release Date
- 2024-09-25
- License
- Other
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HuggingFace
How Much VRAM Does Qwen2.5 3B Instruct Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q2_K | 3.40 | 1.7 GB | 2.8 GB | 1.31 GB | 2-bit quantization with K-quant improvements |
| Q3_K_M | 3.90 | 1.9 GB | 3.0 GB | 1.50 GB | 3-bit medium quantization |
| Q4_0 | 4.00 | 1.9 GB | 3.0 GB | 1.54 GB | 4-bit legacy quantization |
| Q4_K_M | 4.80 | 2.2 GB | 3.4 GB | 1.85 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_0 | 5.00 | 2.3 GB | 3.4 GB | 1.93 GB | 5-bit legacy quantization |
| Q5_K_M | 5.70 | 2.6 GB | 3.7 GB | 2.20 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_K | 6.60 | 2.9 GB | 4.0 GB | 2.55 GB | 6-bit quantization, very good quality |
| Q8_0 | 8.00 | 3.5 GB | 4.6 GB | 3.09 GB | 8-bit quantization, near-lossless |
Which GPUs Can Run Qwen2.5 3B Instruct?
Q4_K_M · 2.2 GBQwen2.5 3B Instruct (Q4_K_M) requires 2.2 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 3+ GB is recommended. Using the full 33K context window can add up to 1.1 GB, bringing total usage to 3.4 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 3B Instruct?
Q4_K_M · 2.2 GB33 devices with unified memory can run Qwen2.5 3B Instruct, 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 3B Instruct need?
Qwen2.5 3B Instruct requires 2.2 GB of VRAM at Q4_K_M, or 3.5 GB at Q8_0. Full 33K context adds up to 1.1 GB (3.4 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 3.1B × 4.8 bits ÷ 8 = 1.9 GB
KV Cache + Overhead ≈ 0.3 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 1.5 GB (at full 33K context)
VRAM usage by quantization
Q4_K_M2.2 GBQ4_K_M + full context3.4 GB- What's the best quantization for Qwen2.5 3B Instruct?
For Qwen2.5 3B Instruct, Q4_K_M (2.2 GB) offers the best balance of quality and VRAM usage. Q5_0 (2.3 GB) provides better quality if you have the VRAM. The smallest option is Q2_K at 1.7 GB.
VRAM requirement by quantization
Q2_K1.7 GB~75%Q4_01.9 GB~85%Q4_K_M ★2.2 GB~89%Q5_02.3 GB~90%Q5_K_M2.6 GB~92%Q8_03.5 GB~99%★ Recommended — best balance of quality and VRAM usage.
- Can I run Qwen2.5 3B Instruct on a Mac?
Qwen2.5 3B Instruct requires at least 1.7 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 3B Instruct locally?
Yes — Qwen2.5 3B Instruct can run locally on consumer hardware. At Q4_K_M quantization it needs 2.2 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Qwen2.5 3B Instruct?
At Q4_K_M, Qwen2.5 3B Instruct can reach ~1307 tok/s on AMD Instinct MI300X. On NVIDIA GeForce RTX 4090: ~294 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 ÷ 2.2 × 0.55 = ~1307 tok/s
Estimated speed at Q4_K_M (2.2 GB)
AMD Instinct MI300X~1307 tok/sNVIDIA GeForce RTX 4090~294 tok/sNVIDIA H100 SXM~977 tok/sAMD Instinct MI250X~808 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 3B Instruct?
At Q4_K_M, the download is about 1.85 GB. The full-precision Q8_0 version is 3.09 GB. The smallest option (Q2_K) is 1.31 GB.