Qwen3 14B PARO — Hardware Requirements & GPU Compatibility
Chat
345 downloads
2 likesSpecifications
- Publisher
- z-lab
- Family
- Qwen
- Parameters
- 1.6B
- Architecture
- Qwen3ForCausalLM
- Context Length
- 40,960 tokens
- Vocabulary Size
- 151,936
- Release Date
- 2026-03-15
- License
- Apache 2.0
Get Started
HuggingFace
How Much VRAM Does Qwen3 14B PARO Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q4_0 | 4.00 | 1.4 GB | 7.8 GB | 0.78 GB | 4-bit legacy quantization |
| Q4_1 | 4.50 | 1.5 GB | 7.9 GB | 0.88 GB | 4-bit legacy quantization with offset |
| Q5_0 | 5.00 | 1.6 GB | 8.0 GB | 0.97 GB | 5-bit legacy quantization |
| Q5_1 | 5.50 | 1.7 GB | 8.1 GB | 1.07 GB | 5-bit legacy quantization with offset |
| Q8_0 | 8.00 | 2.2 GB | 8.6 GB | 1.56 GB | 8-bit quantization, near-lossless |
Which GPUs Can Run Qwen3 14B PARO?
Q4_0 · 1.4 GBQwen3 14B PARO (Q4_0) requires 1.4 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 2+ GB is recommended. Using the full 41K context window can add up to 6.4 GB, bringing total usage to 7.8 GB. 35 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti.
Runs great
— Plenty of headroomWhich Devices Can Run Qwen3 14B PARO?
Q4_0 · 1.4 GB33 devices with unified memory can run Qwen3 14B PARO, including NVIDIA DGX H100, NVIDIA DGX A100 640GB.
Runs great
— Plenty of headroomRelated Models
Frequently Asked Questions
- How much VRAM does Qwen3 14B PARO need?
Qwen3 14B PARO requires 1.4 GB of VRAM at Q4_0, or 2.2 GB at Q8_0. Full 41K context adds up to 6.4 GB (7.8 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 1.6B × 4 bits ÷ 8 = 0.8 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_01.4 GBQ4_0 + full context7.8 GB- What's the best quantization for Qwen3 14B PARO?
For Qwen3 14B PARO, Q5_0 (1.6 GB) offers the best balance of quality and VRAM usage. Q5_1 (1.7 GB) provides better quality if you have the VRAM. The smallest option is Q4_0 at 1.4 GB.
VRAM requirement by quantization
Q4_01.4 GB~85%Q4_11.5 GB~88%Q5_0 ★1.6 GB~90%Q5_11.7 GB~92%Q8_02.2 GB~99%★ Recommended — best balance of quality and VRAM usage.
- Can I run Qwen3 14B PARO on a Mac?
Qwen3 14B PARO requires at least 1.4 GB at Q4_0, 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 PARO locally?
Yes — Qwen3 14B PARO can run locally on consumer hardware. At Q4_0 quantization it needs 1.4 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Qwen3 14B PARO?
At Q4_0, Qwen3 14B PARO can reach ~2067 tok/s on AMD Instinct MI300X. On NVIDIA GeForce RTX 4090: ~465 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 ÷ 1.4 × 0.55 = ~2067 tok/s
Estimated speed at Q4_0 (1.4 GB)
AMD Instinct MI300X~2067 tok/sNVIDIA GeForce RTX 4090~465 tok/sNVIDIA H100 SXM~1545 tok/sAMD Instinct MI250X~1278 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 PARO?
At Q4_0, the download is about 0.78 GB. The full-precision Q8_0 version is 1.56 GB.
- Which GPUs can run Qwen3 14B PARO?
35 consumer GPUs can run Qwen3 14B PARO at Q4_0 (1.4 GB). Top options include AMD Radeon RX 6700 XT, AMD Radeon RX 6800, AMD Radeon RX 6800 XT. 35 GPUs have plenty of headroom for comfortable inference.
- Which devices can run Qwen3 14B PARO?
33 devices with unified memory can run Qwen3 14B PARO at Q4_0 (1.4 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.