Qwen2.5 14B — Hardware Requirements & GPU Compatibility
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Qwen2.5 14B is a 14.8B-parameter open language model from Alibaba in the Qwen 2.5 family. It supports a context window of up to 131,072 tokens. At Q4_K_M it needs about 9.56 GB of VRAM — see which GPUs and Macs can run it below.
60.9K downloads
154 likesSpecifications
- Publisher
- Alibaba
- Family
- Qwen 2.5
- Parameters
- 14.8B
- Architecture
- Qwen2ForCausalLM
- Context Length
- 131,072 tokens
- Vocabulary Size
- 152,064
- Release Date
- 2024-09-15
- License
- Apache 2.0
Get Started
HuggingFace
How Much VRAM Does Qwen2.5 14B Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q2_K | 3.40 | 7.0 GB | 32.4 GB | 6.28 GB | 2-bit quantization with K-quant improvements |
| Q3_K_S | 3.50 | 7.2 GB | 32.5 GB | 6.46 GB | 3-bit small quantization |
| Q3_K_M | 3.90 | 7.9 GB | 33.3 GB | 7.20 GB | 3-bit medium quantization |
| Q4_0 | 4.00 | 8.1 GB | 33.5 GB | 7.39 GB | 4-bit legacy quantization |
| Q4_K_M | 4.80 | 9.6 GB | 34.9 GB | 8.86 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_M | 5.70 | 11.2 GB | 36.6 GB | 10.52 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_K | 6.60 | 12.9 GB | 38.3 GB | 12.19 GB | 6-bit quantization, very good quality |
| Q8_0 | 8.00 | 15.5 GB | 40.8 GB | 14.77 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 14B?
Q4_K_M · 9.6 GBQwen2.5 14B (Q4_K_M) requires 9.6 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 13+ GB is recommended. Using the full 131K context window can add up to 25.4 GB, bringing total usage to 34.9 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?
Q4_K_M · 9.6 GB27 devices with unified memory can run Qwen2.5 14B, including NVIDIA DGX H100, NVIDIA DGX A100 640GB.
Runs great
— Plenty of headroomWhere to Download Qwen2.5 14B
Community quantizations of this model — GGUF for llama.cpp, Ollama, and LM Studio, plus AWQ/MLX variants where available.
Related Models
Frequently Asked Questions
- How much VRAM does Qwen2.5 14B need?
Qwen2.5 14B requires 9.6 GB of VRAM at Q4_K_M, or 30.2 GB at BF16. Full 131K context adds up to 25.4 GB (34.9 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 14.8B × 4.8 bits ÷ 8 = 8.9 GB
KV Cache + Overhead ≈ 0.7 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 26 GB (at full 131K context)
VRAM usage by quantization
Q4_K_M9.6 GBQ4_K_M + full context34.9 GB- Can NVIDIA GeForce RTX 4090 run Qwen2.5 14B?
Yes, at Q8_0 (15.5 GB) or lower. Higher quantizations like BF16 (30.2 GB) exceed the NVIDIA GeForce RTX 4090's 24 GB.
- What's the best quantization for Qwen2.5 14B?
For Qwen2.5 14B, Q4_K_M (9.6 GB) offers the best balance of quality and VRAM usage. Q5_0 (9.9 GB) provides better quality if you have the VRAM. The smallest option is IQ3_XS at 6.8 GB.
VRAM requirement by quantization
IQ3_XS6.8 GBQ3_K_M7.9 GBQ4_19.0 GBQ4_K_M ★9.6 GBQ5_110.9 GBBF1630.2 GB★ Recommended — best balance of quality and VRAM usage.
- Can I run Qwen2.5 14B on a Mac?
Qwen2.5 14B requires at least 6.8 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 14B locally?
Yes — Qwen2.5 14B can run locally on consumer hardware. At Q4_K_M quantization it needs 9.6 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Qwen2.5 14B?
At Q4_K_M, Qwen2.5 14B can reach ~305 tok/s on AMD Instinct MI300X. On NVIDIA GeForce RTX 4090: ~69 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 ÷ 9.6 × 0.55 = ~305 tok/s
Estimated speed at Q4_K_M (9.6 GB)
~305 tok/s~69 tok/s~228 tok/s~189 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?
At Q4_K_M, the download is about 8.86 GB. The full-precision BF16 version is 29.54 GB. The smallest option (IQ3_XS) is 6.09 GB.
- Which GPUs can run Qwen2.5 14B?
28 consumer GPUs can run Qwen2.5 14B at Q4_K_M (9.6 GB). Top options include AMD Radeon RX 6800, AMD Radeon RX 6800 XT, AMD Radeon RX 6900 XT, AMD Radeon RX 6700 XT. 17 GPUs have plenty of headroom for comfortable inference.
- Which devices can run Qwen2.5 14B?
27 devices with unified memory can run Qwen2.5 14B at Q4_K_M (9.6 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.