Qwen1.5 14B — Hardware Requirements & GPU Compatibility
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Qwen1.5 14B is a 14.2B-parameter open language model from Alibaba in the Qwen family. It supports a context window of up to 32,768 tokens. At Q4_K_M it needs about 10.48 GB of VRAM — see which GPUs and Macs can run it below.
19.2K downloads
41 likesSpecifications
- Publisher
- Alibaba
- Family
- Qwen
- Parameters
- 14.2B
- Architecture
- Qwen2ForCausalLM
- Context Length
- 32,768 tokens
- Vocabulary Size
- 152,064
- Release Date
- 2024-01-22
- License
- Other
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HuggingFace
How Much VRAM Does Qwen1.5 14B Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q2_K | 3.40 | 8 GB | 33.2 GB | 6.02 GB | 2-bit quantization with K-quant improvements |
| Q3_K_S | 3.50 | 8.2 GB | 33.3 GB | 6.20 GB | 3-bit small quantization |
| Q3_K_M | 3.90 | 8.9 GB | 34.0 GB | 6.91 GB | 3-bit medium quantization |
| Q4_0 | 4.00 | 9.1 GB | 34.2 GB | 7.08 GB | 4-bit legacy quantization |
| Q4_K_M | 4.80 | 10.5 GB | 35.6 GB | 8.50 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_M | 5.70 | 12.1 GB | 37.2 GB | 10.09 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_K | 6.60 | 13.7 GB | 38.8 GB | 11.69 GB | 6-bit quantization, very good quality |
| Q8_0 | 8.00 | 16.1 GB | 41.3 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 Qwen1.5 14B?
Q4_K_M · 10.5 GBQwen1.5 14B (Q4_K_M) requires 10.5 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 14+ GB is recommended. Using the full 33K context window can add up to 25.2 GB, bringing total usage to 35.6 GB. 37 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 Qwen1.5 14B?
Q4_K_M · 10.5 GB48 devices with unified memory can run Qwen1.5 14B, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, NVIDIA Jetson Orin NX 16GB.
Runs great
— Plenty of headroomDecent
— Enough memory, may be tightWhere to Download Qwen1.5 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 Qwen1.5 14B need?
Qwen1.5 14B requires 10.5 GB of VRAM at Q4_K_M, or 30.3 GB at BF16. Full 33K context adds up to 25.2 GB (35.6 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 14.2B × 4.8 bits ÷ 8 = 8.5 GB
KV Cache + Overhead ≈ 2 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 27.1 GB (at full 33K context)
VRAM usage by quantization
Q4_K_M10.5 GBQ4_K_M + full context35.6 GB- Can NVIDIA GeForce RTX 4090 run Qwen1.5 14B?
Yes, at Q8_0 (16.1 GB) or lower. Higher quantizations like BF16 (30.3 GB) exceed the NVIDIA GeForce RTX 4090's 24 GB.
- What's the best quantization for Qwen1.5 14B?
For Qwen1.5 14B, Q4_K_M (10.5 GB) offers the best balance of quality and VRAM usage. Q5_K_S (11.7 GB) provides better quality if you have the VRAM. The smallest option is Q2_K at 8 GB.
VRAM requirement by quantization
Q2_K8.0 GBQ4_09.1 GBQ4_K_S9.9 GBQ4_K_M ★10.5 GBQ5_K_M12.1 GBBF1630.3 GB★ Recommended — best balance of quality and VRAM usage.
- Can I run Qwen1.5 14B on a Mac?
Qwen1.5 14B requires at least 8 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 Qwen1.5 14B locally?
Yes — Qwen1.5 14B can run locally on consumer hardware. At Q4_K_M quantization it needs 10.5 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Qwen1.5 14B?
At Q4_K_M, Qwen1.5 14B can reach ~420 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~63 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 ÷ 10.5 × 0.65 = ~496 tok/s
Estimated speed at Q4_K_M (10.5 GB)
~496 tok/s~63 tok/s~496 tok/s~420 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of Qwen1.5 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 Qwen1.5 14B?
37 consumer GPUs can run Qwen1.5 14B at Q4_K_M (10.5 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 Qwen1.5 14B?
52 devices with unified memory can run Qwen1.5 14B at Q4_K_M (10.5 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.