Qwen1.5 14B — Hardware Requirements & GPU Compatibility
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Qwen1.5-14B is Alibaba's base pretrained language model, not instruction-tuned, from the Qwen1.5 series that served as a beta preview of Qwen2. It is a dense Transformer with SwiGLU activations, QKV bias, and an improved multilingual tokenizer, released alongside a matching aligned chat model. The Qwen1.5 line spans eight sizes from 0.5B to 72B dense models plus a 14B-class Mixture-of-Experts model, all with stable 32K context support. At 14 billion parameters it fits comfortably on a single consumer GPU once quantized. Context length is 32,768 tokens. It is released under Alibaba's custom Tongyi Qianwen license, which permits commercial use below 100 million monthly active users. It was published in January 2024; Qwen1.5 has since been superseded by the Qwen2, Qwen2.5, and Qwen3 series.
Decent on a 24 GB GPU
Runs from 12 GB (Q4_K_M · 2K)Best on 24 GB (Q4_K_M · ~63 tok/s · 16K)Macs from 16 GB (Q4_K_M · ~13 tok/s)
Other
Reference GPU · RTX 4090 24 GB
Estimates, ±20% · how we estimate
Run Qwen1.5 14B
One recommended way. Every other method, OS and quant is in the step-by-step guide.Each way opens a step-by-step guide for your OS and quant.
Recommended
ollama run hf.co/mradermacher/Qwen1.5-14B-i1-GGUF:Q4_K_MQ4_K_M fits fully on the RTX 4090 · 1.8 GB free · 16K context
Download: Qwen1.5-14B.i1-Q4_K_M.gguf9.19 GBmradermacher
Ways to runOther ways
each opens step-by-stepRun in cloud
Your own private copy · Q4_K_M · 32 GB GPU
- The easiest way to try this model
- Running in a few minutes, easy setup
- Chat right here or connect any app
$5+ bonus for new users
Runs on RTX 5090 (32 GB) (9 GB headroom) · Q4_K_M
- Generation speed
- ~111 tok/s
- generation speed
- Cost per 1M output tokens
- $1.24
- per 1M output tokens
How Much VRAM Does Qwen1.5 14B Need?
Select a quantization level; the listed hardware adheres to it.
| Quantization | VRAM | +32K | On RTX 4090 (reference) | |
|---|---|---|---|---|
| 8 GB | 33.2 GB | 2-bit · K-quant — 2-bit quantization with K-quant improvements | Fits: 16K context · ~82 tok/s | |
| 8.2 GB | 33.4 GB | 3-bit small — 3-bit small quantization | Fits: 16K context · ~80 tok/s | |
| 8.9 GB | 34.1 GB | 3-bit medium — 3-bit medium quantization | Fits: 16K context · ~74 tok/s | |
| 9.1 GB | 34.2 GB | 4-bit · legacy — 4-bit legacy quantization | Fits: 16K context · ~72 tok/s | |
| 10.5 GB | 35.7 GB | 4-bit medium — 4-bit medium quantization — most popular sweet spot | Fits: 16K context · ~63 tok/s | |
| 12.1 GB | 37.2 GB | 5-bit medium — 5-bit medium quantization — good quality/size tradeoff | Fits: 16K context · ~54 tok/s | |
| 13.7 GB | 38.8 GB | 6-bit, very good — 6-bit quantization, very good quality | Fits: 8K context · ~48 tok/s | |
| 16.1 GB | 41.3 GB | 8-bit, near-lossless — 8-bit quantization, near-lossless | Fits: 8K context · ~41 tok/s |
Selected Q4_K_M
4-bit medium quantization22.2GBat 16K context
- Weights
- 8.5 GB
- KV cache
- 13.4 GB
- Overhead
- 0.3 GB
Fits RTX 4090 (reference) · 1.8 GB free · ~63 tok/s
≥50 tok/s needs ≥805 GB/s · RTX 5090 ~111, RTX 3090 Ti ~63
Which Hardware 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. 38 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti, NVIDIA GeForce RTX 3080 Ti. 150 devices with unified memory can run Qwen1.5 14B, including Mac Studio (M3 Ultra, 256GB), Mac Studio (M3 Ultra, 512GB), Mac Mini M2 Pro (16 GB).
20 GB
tight · up to 8K ctx
1 GPU
RX 7900 XT~46Simulate output on RX 7900 XT32–40 GB
fits · 9.8 GB free · up to 16K ctx
1 GPU · 3 Mac classes · 4 device families
RTX 5090~111Simulate output on RTX 5090M5 Max36 GB~31Simulate output on Mac Studio M5 Max (36 GB)M532 GB~10Simulate output on MacBook Air 13" M5 (32 GB)Orin~13Simulate output on NVIDIA Jetson AGX Orin 32GBSnapdragon X Elite (X1E-80-100)~8Simulate output on Snapdragon X Elite Copilot+ PC
By VRAM tier · typical GPUs and Macs at Q4_K_M
How good is Qwen1.5 14B?
llmrun Score blends public benchmark boards into one number; 100 = the average open model.
Not enough data yet for an llmrun Score.
A Score needs results on enough independent boards. How the Composite Score is built
Benchmarks
This is the single board showing results for this model; badges show who ran the test.
- MMLU
Epoch AI · Feb 2024Lab-reported
Lab-reported68.6%
#32/85 open
Alternatives to Qwen1.5 14B
Qwen family · 28 models →Similar models
6| Model | Score | VRAM | tok/s | Why it is similar |
|---|---|---|---|---|
| Apriel 1.6 15B ThinkerServiceNow-AI | — | 13.2 GB | ~67 | same size |
| Phi 4 ReasoningMicrosoft | — | 12.5 GB | ~69 | same size |
| Phi 3 Medium 128k InstructMicrosoft | — | 12 GB | ~72 | same size |
| JiRackUltra 14BCMSManhattan | — | 12.4 GB | ~69 | same size |
| Nemotron Terminal 14BNVIDIA | — | 11.9 GB | ~69 | same size |
| Vllm Translategemma 12B ITchbae624 | — | 9.8 GB | ~73 | same size |
Specifications
- 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
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)
Fit ratings and hardware model lists check this model with room for a 16K-token context, which needs a little more memory.
KV Cache + Overhead ≈ 27.1 GB (at full 33K context)
VRAM usage by quantization
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 IQ2_XXS at 5.9 GB.
VRAM requirement by quantization
★ Recommended — best balance of quality and VRAM usage.
Can I run Qwen1.5 14B on a Mac?
Yes — Mac Mini M2 Pro (16 GB) and 133 other Macs can run Qwen1.5 14B. Apple Silicon uses unified memory, so the model shares RAM with the system. At Q4_K_M you need at least 10.5 GB of usable unified memory (RAM minus macOS overhead).
Can I run Qwen1.5 14B locally?
Yes — Qwen1.5 14B can run locally on consumer hardware. Popular tools include Ollama, LM Studio, and llama.cpp.
How fast is Qwen1.5 14B?
At Q4_K_M, Qwen1.5 14B can reach ~458 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 ÷ 10.48 × 0.65 = ~496 tok/s
Estimated speed at Q4_K_M (10.5 GB)
Real-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 (IQ2_XXS) is 3.90 GB.
Which GPUs can run Qwen1.5 14B?
38 consumer GPUs can run Qwen1.5 14B at Q4_K_M. 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?
150 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, Beelink GTR9 Pro (Ryzen AI Max+ 395, 128 GB), Framework Desktop (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.