Qwen2.5 14B Instruct — Hardware Requirements & GPU Compatibility
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Qwen2.5 14B Instruct is a 14-billion parameter instruction-tuned model from Alibaba Cloud's Qwen 2.5 series. It supports a 128K token context window and provides a balanced tradeoff between quality and hardware requirements, running well on GPUs with 16GB of VRAM in quantized formats. The model is fine-tuned for chat, instruction following, and general-purpose assistant tasks. It performs well across reasoning, coding, and multilingual benchmarks for its size class, making it a practical option for local deployment when larger models are not feasible. Released under the Apache 2.0 license.
2.0M downloads
322 likesSpecifications
- Publisher
- Alibaba
- Family
- Qwen 2.5
- Parameters
- 14B
- Architecture
- Qwen2ForCausalLM
- Context Length
- 32,768 tokens
- Vocabulary Size
- 152,064
- Release Date
- 2024-09-25
- License
- Apache 2.0
Get Started
HuggingFace
How Much VRAM Does Qwen2.5 14B Instruct Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| IQ2_XS | 2.40 | 4.9 GB | 10.9 GB | 4.20 GB | Importance-weighted 2-bit, extra small |
| IQ2_M | 2.70 | 5.4 GB | 11.5 GB | 4.72 GB | Importance-weighted 2-bit, medium |
| IQ3_XS | 3.30 | 6.5 GB | 12.5 GB | 5.78 GB | Importance-weighted 3-bit, extra small |
| Q2_K | 3.40 | 6.7 GB | 12.7 GB | 5.95 GB | 2-bit quantization with K-quant improvements |
| IQ3_S | 3.40 | 6.7 GB | 12.7 GB | 5.95 GB | Importance-weighted 3-bit, small |
| Q3_K_S | 3.50 | 6.8 GB | 12.9 GB | 6.13 GB | 3-bit small quantization |
| IQ3_M | 3.60 | 7 GB | 13.0 GB | 6.30 GB | Importance-weighted 3-bit, medium |
| Q3_K_M | 3.90 | 7.5 GB | 13.6 GB | 6.83 GB | 3-bit medium quantization |
| Q4_0 | 4.00 | 7.7 GB | 13.7 GB | 7.00 GB | 4-bit legacy quantization |
| Q3_K_L | 4.10 | 7.9 GB | 13.9 GB | 7.17 GB | 3-bit large quantization |
| IQ4_XS | 4.30 | 8.2 GB | 14.3 GB | 7.53 GB | Importance-weighted 4-bit, compact |
| Q4_K_S | 4.50 | 8.6 GB | 14.6 GB | 7.88 GB | 4-bit small quantization |
| IQ4_NL | 4.50 | 8.6 GB | 14.6 GB | 7.88 GB | Importance-weighted 4-bit, non-linear |
| Q4_1 | 4.50 | 8.6 GB | 14.6 GB | 7.88 GB | 4-bit legacy quantization with offset |
| Q4_K_M | 4.80 | 9.1 GB | 15.1 GB | 8.40 GB | 4-bit medium quantization — most popular sweet spot |
| Q4_K_L | 4.90 | 9.3 GB | 15.3 GB | 8.58 GB | 4-bit large quantization |
| Q5_0 | 5.00 | 9.4 GB | 15.5 GB | 8.75 GB | 5-bit legacy quantization |
| Q5_1 | 5.50 | 10.3 GB | 16.4 GB | 9.63 GB | 5-bit legacy quantization with offset |
| Q5_K_S | 5.50 | 10.3 GB | 16.4 GB | 9.63 GB | 5-bit small quantization |
| Q5_K_M | 5.70 | 10.7 GB | 16.7 GB | 9.97 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q5_K_L | 5.80 | 10.8 GB | 16.9 GB | 10.15 GB | 5-bit large quantization |
| Q6_K | 6.60 | 12.3 GB | 18.3 GB | 11.55 GB | 6-bit quantization, very good quality |
| Q8_0 | 8.00 | 14.7 GB | 20.7 GB | 14.00 GB | 8-bit quantization, near-lossless |
Which GPUs Can Run Qwen2.5 14B Instruct?
Q4_K_M · 9.1 GBQwen2.5 14B Instruct (Q4_K_M) requires 9.1 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 12+ GB is recommended. Using the full 33K context window can add up to 6.0 GB, bringing total usage to 15.1 GB. 28 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti, NVIDIA GeForce RTX 3080 Ti.
Runs great
— Plenty of headroom
Decent
— Enough VRAM, may be tightWhich Devices Can Run Qwen2.5 14B Instruct?
Q4_K_M · 9.1 GB27 devices with unified memory can run Qwen2.5 14B Instruct, including NVIDIA DGX H100, NVIDIA DGX A100 640GB.
Runs great
— Plenty of headroomRelated Models
Derivatives (5)
Frequently Asked Questions
- How much VRAM does Qwen2.5 14B Instruct need?
Qwen2.5 14B Instruct requires 9.1 GB of VRAM at Q4_K_M, or 14.7 GB at Q8_0. Full 33K context adds up to 6.0 GB (15.1 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 14B × 4.8 bits ÷ 8 = 8.4 GB
KV Cache + Overhead ≈ 0.7 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 6.7 GB (at full 33K context)
VRAM usage by quantization
Q4_K_M9.1 GBQ4_K_M + full context15.1 GB- What's the best quantization for Qwen2.5 14B Instruct?
For Qwen2.5 14B Instruct, Q4_K_M (9.1 GB) offers the best balance of quality and VRAM usage. Q4_K_L (9.3 GB) provides better quality if you have the VRAM. The smallest option is IQ2_XS at 4.9 GB.
VRAM requirement by quantization
IQ2_XS4.9 GB~57%IQ3_M7.0 GB~78%Q4_K_S8.6 GB~88%Q4_K_M ★9.1 GB~89%Q5_110.3 GB~92%Q8_014.7 GB~99%★ Recommended — best balance of quality and VRAM usage.
- Can I run Qwen2.5 14B Instruct on a Mac?
Qwen2.5 14B Instruct requires at least 4.9 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 14B Instruct locally?
Yes — Qwen2.5 14B Instruct can run locally on consumer hardware. At Q4_K_M quantization it needs 9.1 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Qwen2.5 14B Instruct?
At Q4_K_M, Qwen2.5 14B Instruct can reach ~320 tok/s on AMD Instinct MI300X. On NVIDIA GeForce RTX 4090: ~72 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 ÷ 9.1 × 0.55 = ~320 tok/s
Estimated speed at Q4_K_M (9.1 GB)
AMD Instinct MI300X~320 tok/sNVIDIA GeForce RTX 4090~72 tok/sNVIDIA H100 SXM~239 tok/sAMD Instinct MI250X~198 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 14B Instruct?
At Q4_K_M, the download is about 8.40 GB. The full-precision Q8_0 version is 14.00 GB. The smallest option (IQ2_XS) is 4.20 GB.