Qwen2.5 14B Instruct — Hardware Requirements & GPU Compatibility
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Qwen2.5 14B Instruct is a 14-billion parameter instruction-tuned model from Alibaba Cloud's Qwen 2.5 series. It supports a 128K token context window and provides a balanced tradeoff between quality and hardware requirements, running well on GPUs with 16GB of VRAM in quantized formats. The model is fine-tuned for chat, instruction following, and general-purpose assistant tasks. It performs well across reasoning, coding, and multilingual benchmarks for its size class, making it a practical option for local deployment when larger models are not feasible. Released under the Apache 2.0 license.
1.9M downloads
347 likesSpecifications
- Publisher
- Alibaba
- Family
- Qwen 2.5
- Parameters
- 14.8B
- Architecture
- Qwen2ForCausalLM
- Context Length
- 32,768 tokens
- Vocabulary Size
- 152,064
- Release Date
- 2024-09-16
- License
- Apache 2.0
Get Started
HuggingFace
How Much VRAM Does Qwen2.5 14B Instruct Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q2_K | 3.40 | 7.0 GB | 13.0 GB | 6.28 GB | 2-bit quantization with K-quant improvements |
| Q3_K_S | 3.50 | 7.2 GB | 13.2 GB | 6.46 GB | 3-bit small quantization |
| Q3_K_M | 3.90 | 7.9 GB | 13.9 GB | 7.20 GB | 3-bit medium quantization |
| Q4_0 | 4.00 | 8.1 GB | 14.1 GB | 7.39 GB | 4-bit legacy quantization |
| Q4_K_M | 4.80 | 9.6 GB | 15.6 GB | 8.86 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_M | 5.70 | 11.2 GB | 17.3 GB | 10.52 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_K | 6.60 | 12.9 GB | 18.9 GB | 12.19 GB | 6-bit quantization, very good quality |
| Q8_0 | 8.00 | 15.5 GB | 21.5 GB | 14.77 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 Qwen2.5 14B Instruct?
Q4_K_M · 9.6 GBQwen2.5 14B Instruct (Q4_K_M) requires 9.6 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 33K context window can add up to 6.0 GB, bringing total usage to 15.6 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 Qwen2.5 14B Instruct?
Q4_K_M · 9.6 GB49 devices with unified memory can run Qwen2.5 14B Instruct, 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 Qwen2.5 14B Instruct
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 Qwen2.5 14B Instruct need?
Qwen2.5 14B Instruct requires 9.6 GB of VRAM at Q4_K_M, or 30.2 GB at BF16. Full 33K context adds up to 6.0 GB (15.6 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 14.8B × 4.8 bits ÷ 8 = 8.9 GB
KV Cache + Overhead ≈ 0.7 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 6.7 GB (at full 33K context)
VRAM usage by quantization
Q4_K_M9.6 GBQ4_K_M + full context15.6 GB- Can NVIDIA GeForce RTX 4090 run Qwen2.5 14B Instruct?
Yes, at Q8_0 (15.5 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 Qwen2.5 14B Instruct?
For Qwen2.5 14B Instruct, Q4_K_M (9.6 GB) offers the best balance of quality and VRAM usage. Q4_K_L (9.8 GB) provides better quality if you have the VRAM. The smallest option is IQ2_XS at 5.1 GB.
VRAM requirement by quantization
IQ2_XS5.1 GBIQ3_M7.3 GBIQ4_NL9.0 GBQ4_K_M ★9.6 GBQ5_K_S10.9 GBBF1630.2 GB★ Recommended — best balance of quality and VRAM usage.
- Can I run Qwen2.5 14B Instruct on a Mac?
Qwen2.5 14B Instruct requires at least 5.1 GB at IQ2_XS, 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 Qwen2.5 14B Instruct locally?
Yes — Qwen2.5 14B Instruct can run locally on consumer hardware. At Q4_K_M quantization it needs 9.6 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Qwen2.5 14B Instruct?
At Q4_K_M, Qwen2.5 14B Instruct can reach ~460 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 ÷ 9.6 × 0.65 = ~544 tok/s
Estimated speed at Q4_K_M (9.6 GB)
~544 tok/s~69 tok/s~544 tok/s~460 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of Qwen2.5 14B Instruct?
At Q4_K_M, the download is about 8.86 GB. The full-precision BF16 version is 29.54 GB. The smallest option (IQ2_XS) is 4.43 GB.
- Which GPUs can run Qwen2.5 14B Instruct?
39 consumer GPUs can run Qwen2.5 14B Instruct at Q4_K_M (9.6 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 Qwen2.5 14B Instruct?
52 devices with unified memory can run Qwen2.5 14B Instruct at Q4_K_M (9.6 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.