Qwen3 14B — Hardware Requirements & GPU Compatibility
Chat
Qwen3 14B is a 14-billion parameter instruction-tuned model from Alibaba Cloud's Qwen 3 series. It occupies a practical middle ground in the Qwen 3 lineup, offering stronger reasoning and generation quality than the 8B variant while remaining manageable on GPUs with 16GB or more of VRAM in quantized formats. The model supports hybrid thinking mode for flexible reasoning depth. Qwen3 14B is well suited for chat, instruction following, coding assistance, and multilingual tasks. It benefits from the generational improvements of Qwen 3 in pretraining data and alignment techniques, delivering performance that competes with larger models from previous generations. Released under the Apache 2.0 license.
1.5M downloads
407 likesSpecifications
- Publisher
- Alibaba
- Family
- Qwen 3
- Parameters
- 14.8B
- Architecture
- Qwen3ForCausalLM
- Context Length
- 40,960 tokens
- Vocabulary Size
- 151,936
- Release Date
- 2025-04-27
- License
- Apache 2.0
Get Started
HuggingFace
How Much VRAM Does Qwen3 14B Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q2_K | 3.40 | 6.9 GB | 13.3 GB | 6.28 GB | 2-bit quantization with K-quant improvements |
| Q3_K_S | 3.50 | 7.1 GB | 13.5 GB | 6.46 GB | 3-bit small quantization |
| Q3_K_M | 3.90 | 7.8 GB | 14.2 GB | 7.20 GB | 3-bit medium quantization |
| Q4_0 | 4.00 | 8.0 GB | 14.4 GB | 7.38 GB | 4-bit legacy quantization |
| Q4_K_M | 4.80 | 9.5 GB | 15.9 GB | 8.86 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_M | 5.70 | 11.2 GB | 17.5 GB | 10.52 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_K | 6.60 | 12.8 GB | 19.2 GB | 12.18 GB | 6-bit quantization, very good quality |
| Q8_0 | 8.00 | 15.4 GB | 21.8 GB | 14.77 GB | 8-bit quantization, near-lossless |
Which GPUs Can Run Qwen3 14B?
Q4_K_M · 9.5 GBQwen3 14B (Q4_K_M) requires 9.5 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 41K context window can add up to 6.4 GB, bringing total usage to 15.9 GB. 39 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 Qwen3 14B?
Q4_K_M · 9.5 GB49 devices with unified memory can run Qwen3 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 Qwen3 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 Qwen3 14B need?
Qwen3 14B requires 9.5 GB of VRAM at Q4_K_M, or 30.2 GB at BF16. Full 41K context adds up to 6.4 GB (15.9 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 14.8B × 4.8 bits ÷ 8 = 8.9 GB
KV Cache + Overhead ≈ 0.6 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 7 GB (at full 41K context)
VRAM usage by quantization
Q4_K_M9.5 GBQ4_K_M + full context15.9 GB- Can NVIDIA GeForce RTX 4090 run Qwen3 14B?
Yes, at Q8_0 (15.4 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 Qwen3 14B?
For Qwen3 14B, Q4_K_M (9.5 GB) offers the best balance of quality and VRAM usage. Q4_K_L (9.7 GB) provides better quality if you have the VRAM. The smallest option is IQ2_XXS at 4.7 GB.
VRAM requirement by quantization
IQ2_XXS4.7 GBQ2_K6.9 GBIQ4_XS8.6 GBQ4_K_M ★9.5 GBQ5_09.9 GBBF1630.2 GB★ Recommended — best balance of quality and VRAM usage.
- Can I run Qwen3 14B on a Mac?
Qwen3 14B requires at least 4.7 GB at IQ2_XXS, 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 Qwen3 14B locally?
Yes — Qwen3 14B can run locally on consumer hardware. At Q4_K_M quantization it needs 9.5 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Qwen3 14B?
At Q4_K_M, Qwen3 14B can reach ~463 tok/s on AMD Instinct MI350X. 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: NVIDIA B200 → 8000 ÷ 9.5 × 0.65 = ~547 tok/s
Estimated speed at Q4_K_M (9.5 GB)
~547 tok/s~69 tok/s~547 tok/s~463 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of Qwen3 14B?
At Q4_K_M, the download is about 8.86 GB. The full-precision BF16 version is 29.54 GB. The smallest option (IQ2_XXS) is 4.06 GB.
- Which GPUs can run Qwen3 14B?
39 consumer GPUs can run Qwen3 14B at Q4_K_M (9.5 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 Qwen3 14B?
52 devices with unified memory can run Qwen3 14B at Q4_K_M (9.5 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.