Qwen 14B — Hardware Requirements & GPU Compatibility
Chat
Qwen-14B is Alibaba's first-generation 14-billion-parameter base language model, pretrained from scratch on over 3 trillion tokens of Chinese, English, multilingual, code, and math data, with a roughly 150,000-token vocabulary for broader multilingual coverage. It is a raw pretrained Transformer, not tuned for conversation; Alibaba's aligned Qwen-14B-Chat is the assistant built on top of it. The card reports it beating other open models of similar size, and in some benchmarks larger models too, on Chinese and English evaluation suites. At 14B parameters it needs a capable consumer GPU at full precision, considerably less once quantized. Context length is 8,192 tokens, extendable further with the NTK-aware interpolation and window-attention techniques described in the card. It is released under a custom Tongyi Qianwen License Agreement that is free for research, with commercial use requiring a separate application to Alibaba. It was published in September 2023.
5.1K downloads
214 likesSpecifications
- Publisher
- Alibaba
- Family
- Qwen
- Parameters
- 14.2B
- Architecture
- QWenLMHeadModel
- Context Length
- 8,192 tokens
- Vocabulary Size
- 152,064
- Release Date
- 2023-09-24
Get Started
HuggingFace
How Much VRAM Does Qwen 14B Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q2_K | 3.40 | 6.6 GB | — | 6.02 GB | 2-bit quantization with K-quant improvements |
| Q3_K_S | 3.50 | 6.8 GB | — | 6.20 GB | 3-bit small quantization |
| Q3_K_M | 3.90 | 7.6 GB | — | 6.91 GB | 3-bit medium quantization |
| Q4_K_M | 4.80 | 9.3 GB | — | 8.50 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_M | 5.70 | 11.1 GB | — | 10.09 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_K | 6.60 | 12.9 GB | — | 11.69 GB | 6-bit quantization, very good quality |
| Q8_0 | 8.00 | 15.6 GB | — | 14.17 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 Qwen 14B?
Q4_K_M · 9.3 GBQwen 14B (Q4_K_M) requires 9.3 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 13+ GB is recommended. 40 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 Qwen 14B?
Q4_K_M · 9.3 GB49 devices with unified memory can run Qwen 14B, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, iPad Pro M5 13" (16 GB).
Runs great
— Plenty of headroomDecent
— Enough memory, may be tightWhere to Download Qwen 14B
Community quantizations of this model — GGUF for llama.cpp, Ollama, and LM Studio, plus AWQ/MLX variants where available.
Benchmarks
Benchmark details →Related Models
Frequently Asked Questions
- How much VRAM does Qwen 14B need?
Qwen 14B requires 9.3 GB of VRAM at Q4_K_M, or 31.2 GB at BF16.
VRAM = Weights + KV Cache + Overhead
Weights = 14.2B × 4.8 bits ÷ 8 = 8.5 GB
KV Cache + Overhead ≈ 0.8 GB (at 2K context + ~0.3 GB framework)
VRAM usage by quantization
Q4_K_M9.3 GB- Can NVIDIA GeForce RTX 4090 run Qwen 14B?
Yes, at Q8_0 (15.6 GB) or lower. Higher quantizations like BF16 (31.2 GB) exceed the NVIDIA GeForce RTX 4090's 24 GB.
- What's the best quantization for Qwen 14B?
For Qwen 14B, Q4_K_M (9.3 GB) offers the best balance of quality and VRAM usage. Q5_K_S (10.7 GB) provides better quality if you have the VRAM. The smallest option is Q2_K at 6.6 GB.
VRAM requirement by quantization
Q2_K6.6 GBQ3_K_L8.0 GBQ4_K_M ★9.3 GBQ5_K_S10.7 GBQ5_K_M11.1 GBBF1631.2 GB★ Recommended — best balance of quality and VRAM usage.
- Can I run Qwen 14B on a Mac?
Qwen 14B requires at least 6.6 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 Qwen 14B locally?
Yes — Qwen 14B can run locally on consumer hardware. At Q4_K_M quantization it needs 9.3 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Qwen 14B?
At Q4_K_M, Qwen 14B can reach ~513 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~70 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 ÷ 9.3 × 0.65 = ~556 tok/s
Estimated speed at Q4_K_M (9.3 GB)
~556 tok/s~70 tok/s~556 tok/s~513 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of Qwen 14B?
At Q4_K_M, the download is about 8.50 GB. The full-precision BF16 version is 28.33 GB. The smallest option (Q2_K) is 6.02 GB.
- Which GPUs can run Qwen 14B?
40 consumer GPUs can run Qwen 14B at Q4_K_M (9.3 GB). Top options include AMD Radeon RX 6800, AMD Radeon RX 6800 XT, AMD Radeon RX 6900 XT, AMD Radeon RX 6700 XT. 26 GPUs have plenty of headroom for comfortable inference.
- Which devices can run Qwen 14B?
52 devices with unified memory can run Qwen 14B at Q4_K_M (9.3 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.