Qwen 14B — Hardware Requirements & GPU Compatibility
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Qwen 14B is a 14.2B-parameter open language model from Alibaba in the Qwen family. It supports a context window of up to 8,192 tokens. At Q4_K_M it needs about 9.35 GB of VRAM — see which GPUs and Macs can run it below.
2.2K downloads
214 likesSpecifications
- Publisher
- Alibaba
- Family
- Qwen
- Parameters
- 14.2B
- Architecture
- QWenLMHeadModel
- Context Length
- 8,192 tokens
- Vocabulary Size
- 152,064
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 |
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. 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 Qwen 14B?
Q4_K_M · 9.3 GB27 devices with unified memory can run Qwen 14B, including NVIDIA DGX H100, NVIDIA DGX A100 640GB.
Runs great
— Plenty of headroomBenchmarks
View all 3 →Related Models
Derivatives (1)
Frequently Asked Questions
- How much VRAM does Qwen 14B need?
Qwen 14B requires 9.3 GB of VRAM at Q4_K_M, or 15.6 GB at Q8_0.
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- 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_S8.8 GBQ4_K_M ★9.3 GBQ5_K_M11.1 GBQ8_015.6 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 ~312 tok/s on AMD Instinct MI300X. 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: AMD Instinct MI300X → 5300 ÷ 9.3 × 0.55 = ~312 tok/s
Estimated speed at Q4_K_M (9.3 GB)
~312 tok/s~70 tok/s~233 tok/s~193 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 Q8_0 version is 14.17 GB. The smallest option (Q2_K) is 6.02 GB.
- Which GPUs can run Qwen 14B?
28 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. 17 GPUs have plenty of headroom for comfortable inference.
- Which devices can run Qwen 14B?
27 devices with unified memory can run Qwen 14B at Q4_K_M (9.3 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.