Qwen 14B Chat — Hardware Requirements & GPU Compatibility
Chat
Qwen-14B-Chat is Alibaba Cloud's 14-billion-parameter bilingual Chinese/English chat model, aligned from the pretrained Qwen-14B base using supervised alignment techniques on top of large-scale web text, books, and code pretraining data. It supports tool and API use through ReAct-style prompting, the same pattern used by LangChain agents, and can extend beyond its native context via optional NTK-aware interpolation and LogN attention scaling for long-document summarization. At 14 billion parameters it fits a single consumer GPU once quantized. Context length is 8,192 tokens natively. It is released under the Tongyi Qianwen License Agreement, Alibaba's custom license that allows research and commercial use, with a separate application required for larger-scale commercial deployment. It was published in September 2023, part of the original Qwen generation that Alibaba has since superseded with Qwen1.5, Qwen2, and Qwen3.
Runs great on a 24 GB GPU
Runs from 8 GB (IQ3_S · 8K)Best on 24 GB (Q4_K_M · ~70 tok/s · 8K)Macs from 16 GB (Q4_K_M · ~15 tok/s)
Reference GPU · RTX 4090 24 GB
Estimates, ±20% · how we estimate
Run Qwen 14B Chat
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/Qwen-14B-Chat-GGUF:Q4_K_MQ4_K_M fits fully on the RTX 4090 · 13.4 GB free · 8K context
Download: Qwen-14B-Chat.Q4_K_M.gguf9.45 GBmradermacher
Ways to runOther ways
each opens step-by-stepRun in cloud
Your own private copy · Q4_K_M · 24 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 3090 (24 GB) (13 GB headroom) · Q4_K_M
- Generation speed
- ~65 tok/s
- generation speed
- Cost per 1M output tokens
- $0.72
- per 1M output tokens
How Much VRAM Does Qwen 14B Chat Need?
Select a quantization level; the listed hardware adheres to it.
| Quantization | VRAM | +32K | On RTX 4090 (reference) | |
|---|---|---|---|---|
| 6.6 GB | — | 2-bit · K-quant — 2-bit quantization with K-quant improvements | Fits: 8K context · ~99 tok/s | |
| 6.8 GB | — | 3-bit small — 3-bit small quantization | Fits: 8K context · ~96 tok/s | |
| 7.6 GB | — | 3-bit medium — 3-bit medium quantization | Fits: 8K context · ~86 tok/s | |
| 7.8 GB | — | 4-bit · legacy — 4-bit legacy quantization | Fits: 8K context · ~84 tok/s | |
| 9.3 GB | — | 4-bit medium — 4-bit medium quantization — most popular sweet spot | Fits: 8K context · ~70 tok/s | |
| 11.1 GB | — | 5-bit medium — 5-bit medium quantization — good quality/size tradeoff | Fits: 8K context · ~59 tok/s | |
| 12.9 GB | — | 6-bit, very good — 6-bit quantization, very good quality | Fits: 8K context · ~51 tok/s | |
| 15.6 GB | — | 8-bit, near-lossless — 8-bit quantization, near-lossless | Fits: 8K context · ~42 tok/s |
Selected Q4_K_M
4-bit medium quantization10.6GBat 8K context
- Weights
- 8.6 GB
- KV cache
- 1.7 GB
- Overhead
- 0.3 GB
Fits RTX 4090 (reference) · 13.4 GB free · ~70 tok/s
≥50 tok/s needs ≥719 GB/s · RTX 5090 ~125, RTX 3090 Ti ~70
Which Hardware Can Run Qwen 14B Chat?
Q4_K_M · 9.3 GBQwen 14B Chat (Q4_K_M) requires 9.3 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 13+ GB is recommended. 40 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 Qwen 14B Chat, including Mac Studio (M3 Ultra, 256GB), Mac Studio (M3 Ultra, 512GB), Mac Mini M2 Pro (16 GB).
12 GB
12 GB tier →fits · 1.4 GB free · up to 8K ctx
10 GPUs
RTX 3080 Ti~63Simulate output on RTX 3080 TiRTX 5070~47Simulate output on RTX 5070RTX 4070~35Simulate output on RTX 407020 GB
fits · 9.4 GB free · up to 8K ctx
1 GPU
RX 7900 XT~51Simulate output on RX 7900 XT32–40 GB
fits · 21.4 GB free · up to 8K ctx
1 GPU · 3 Mac classes · 4 device families
RTX 5090~125Simulate output on RTX 5090M5 Max36 GB~34Simulate output on Mac Studio M5 Max (36 GB)M532 GB~12Simulate output on MacBook Air 13" M5 (32 GB)Orin~14Simulate output on NVIDIA Jetson AGX Orin 32GBSnapdragon X Elite (X1E-80-100)~9Simulate output on Snapdragon X Elite Copilot+ PC
By VRAM tier · typical GPUs and Macs at Q4_K_M
How good is Qwen 14B Chat?
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
Here are all 4 boards that have results for this model, sorted by their weight in the llmrun Score; badges show who ran the test.
- LMArena Text
LMArena · Oct 2026Arena
Arena1139.4
#173/188 open
- MMLU
Epoch AI · Sep 2023Lab-reported
Lab-reported65.0%
#39/85 open
- BIG-Bench Hard
Epoch AI · Sep 2023Lab-reported
Lab-reported55.0%
#16/38 open
- GSM8K
Epoch AI · Sep 2023Lab-reported
Lab-reported61.2%
#25/62 open
Alternatives to Qwen 14B Chat
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
- QWenLMHeadModel
- Context Length
- 8,192 tokens
- Vocabulary Size
- 152,064
- Release Date
- 2023-09-24
Frequently Asked Questions
How much VRAM does Qwen 14B Chat need?
Qwen 14B Chat requires 9.3 GB of VRAM at Q4_K_M, or 31.2 GB at BF16.
VRAM = Weights + KV Cache + Overhead
Weights = 14.2B × 4.8 bits ÷ 8 = 8.5 GB
KV Cache + Overhead ≈ 0.8 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.
VRAM usage by quantization
Can NVIDIA GeForce RTX 4090 run Qwen 14B Chat?
Yes, at Q8_0 (15.6 GB) or lower. Higher quantizations like BF16 (31.2 GB) exceed the NVIDIA GeForce RTX 4090's 24 GB.
What's the best quantization for Qwen 14B Chat?
For Qwen 14B Chat, Q4_K_M (9.3 GB) offers the best balance of quality and VRAM usage. Q5_K_S (10.7 GB) provides better quality if you have the VRAM. The smallest option is IQ2_XXS at 4.3 GB.
VRAM requirement by quantization
★ Recommended — best balance of quality and VRAM usage.
Can I run Qwen 14B Chat on a Mac?
Yes — Mac Mini M2 Pro (16 GB) and 133 other Macs can run Qwen 14B Chat. Apple Silicon uses unified memory, so the model shares RAM with the system. At Q4_K_M you need at least 9.3 GB of usable unified memory (RAM minus macOS overhead).
Can I run Qwen 14B Chat locally?
Yes — Qwen 14B Chat can run locally on consumer hardware. Popular tools include Ollama, LM Studio, and llama.cpp.
How fast is Qwen 14B Chat?
At Q4_K_M, Qwen 14B Chat can reach ~513 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~70 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 ÷ 9.35 × 0.65 = ~556 tok/s
Estimated speed at Q4_K_M (9.3 GB)
Real-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
What's the download size of Qwen 14B Chat?
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 Qwen 14B Chat?
40 consumer GPUs can run Qwen 14B Chat 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 Qwen 14B Chat?
150 devices with unified memory can run Qwen 14B Chat at Q4_K_M (9.3 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.