Qwen3 14B VRAM requirements: can you run it locally?

Qwen3 14B is Alibaba Qwen's dense 14-billion-parameter model from the Qwen3 family. Its Hugging Face repo was created on April 27, 2025, it is Apache 2.0 licensed, and Hugging Face lists 14,768,307,200 parameters.^[1] The model card gives a native context of 32,768 tokens, and 131,072 tokens with YaRN rope scaling.^[2] It remains one of the most-downloaded open models, with about 9.4 million Hugging Face downloads in the last 30 days (checked October 23, 2026).^[1]

Memory needed at each quantization

Quantization stores each weight in fewer bits. Smaller files fit on smaller GPUs, at some cost in quality, and the cost grows below about 4 bits. Below is every GGUF build in the bartowski repo, from 2-bit to the original BF16, using the exact file sizes on Hugging Face (1 GB = 1,000,000,000 bytes), largest first.^[4] Qwen's own official GGUF repo has the same sizes for the quants it publishes (Q4_K_M, Q5_0, Q5_K_M, Q6_K, Q8_0), to within 1 KB.^[5]

Ollama's default qwen3:14b download is 9.3 GB (its Q4_K_M build). Its q8_0 tag is 16 GB and fp16 is 30 GB.^[6]

Which quant should you pick? Q4_K_M is the usual balance of size and quality. Use Q5_K_M or Q6_K if you have the memory, and use Q8_0 if you want results close to the original. Treat 2-bit and 3-bit builds as a last resort for small cards.

Context length adds memory (the KV cache)

On top of the weights, the engine keeps a key/value cache for every token in the conversation. From config.json (40 layers, 8 KV heads, head size 128),^[3] an FP16 cache takes 2 × 40 × 8 × 128 × 2 bytes = 163,840 bytes per token:

The 131,072-token row needs YaRN enabled.^[2] At the native 32K limit, the cache (5.4 GB) is more than half the size of the Q4_K_M weights. Some engines can store the cache in 8-bit to roughly halve it.

Which GPUs and Macs it fits on

Each cell compares the weights plus an 8K-token KV cache with the device's memory.^{[7][8][9][10][11][12]}

Column headers show weights + 8,192-token KV cache.

How to read it: "Fits" means weights + 8K cache use under 90% of memory. "Tight" means 90–100%: it may load, but the engine's own working memory or a longer context can push it over. "No" means it won't fit on the GPU alone. The 90% line is our own rule of thumb, not a vendor figure. On a Mac, macOS also needs some of the unified memory, so treat Mac rows as optimistic.

Our recommendations

These follow from the tables above.

How it compares with similar models

Q4_K_M file sizes and KV cache per token come from each model's Hugging Face GGUF repo and config.json, the same data as our comparison table.^[14]

*Qwen3.8 27B counts only its 16 full-attention layers. For context, Qwen3 14B supports 32K natively (131K with YaRN),^[2] Llama 3.1 8B supports 128K,^[15] and Qwen3.8 27B supports 262,144 tokens.^[16] We only list context lengths we have checked against the model's own card or config.

Qwen3 14B's KV cache grows faster per token than most models here, so long contexts cost it more memory than gpt-oss-20b or Qwen3.8 27B.

How to run it

The easiest way is Ollama. ollama run qwen3:14b pulls the 9.3 GB Q4_K_M build. Use qwen3:14b-q8_0 (16 GB) or qwen3:14b-fp16 (30 GB) for higher precision.^[6] With llama.cpp or LM Studio, download a GGUF file such as Qwen_Qwen3-14B-Q4_K_M.gguf from the quant list above.^[4] Qwen3 has a thinking mode and a non-thinking mode. Thinking produces long reasoning output, so leave enough context for it.^[2]

If the model is bigger than your VRAM, these tools can keep some layers in system RAM, but every token then has to wait on the slower memory, so expect much lower speed.

Rent a GPU for this model

Any 24 GB GPU runs Q8_0 with a 32K context, so renting is cheap. On-demand prices listed on Runpod's pricing page (checked October 23, 2026; Community Cloud is cheaper but runs on shared hosts):^[13]

Prices checked on 2026-10-23. They change often, so confirm on the provider's page. A 48 GB card runs Q4_K_M tightly; 80 GB gives room for longer context or Q5/Q6.

See all GPU rental prices for more providers and multi-GPU setups.

Where to rent or buy

Frequently asked questions

Can I run Qwen3 14B on a 12 GB GPU like the RTX 3060?

Yes, at Q4_K_M. The 9.0 GB file plus an 8K cache is about 10.3 GB, or 86% of 12 GB.^[4][8] Q5_K_M (10.5 GB file, 11.9 GB with the cache) is tight. For longer contexts, drop to IQ4_XS (8.1 GB) or Q3_K_M (7.3 GB).

Can I run Qwen3 14B on an 8 GB GPU?

Only at 2 bits on the GPU alone. Q2_K (5.8 GB) plus an 8K cache is about 7.1 GB, and Q3_K_M (7.3 GB, 8.7 GB with the cache) does not fit.^[4] Otherwise, offload some layers to system RAM and accept slower output.

How much VRAM does Qwen3 14B need for a 32K context?

At FP16 the KV cache for 32,768 tokens is about 5.4 GB.^[3] With Q4_K_M weights that's about 14.4 GB, and with Q8_0 about 21.1 GB, before engine overhead.

Is the Ollama download the same as Q4_K_M?

Yes. Ollama's default qwen3:14b tag is 9.3 GB, the same as its Q4_K_M tag.^[6] The GGUF Q4_K_M file on Hugging Face is 9.0 GB.^[4]

Can a Mac run Qwen3 14B?

Yes. A 16 GB Mac can hold Q4_K_M with a moderate context, though macOS takes some of the memory. A 24 GB or 32 GB Mac can run Q8_0 (15.7 GB).^[4][12]

Qwen3 14B or Qwen3.8 27B?

If you have 24 GB, Qwen3.8 27B at Q4_K_M (17.4 GB) also fits and is the newer, larger model.^[16] On 12–16 GB, Qwen3 14B is the one that fits at 4 bits or better.

How we calculated

• Weights are the exact GGUF file sizes on Hugging Face, checked October 23, 2026.^[4][5] BF16 is the BF16 GGUF file.
• KV cache is calculated from config.json. We assume an FP16 cache.^[3]
• Device memory is from NVIDIA and Apple spec pages. We compare in decimal GB, so the results lean conservative.
• Rental prices are the listed on-demand Runpod rates from our GPU price data, checked October 23, 2026.^[13]
• Not included: engine working buffers, the OS and display. These vary by engine and settings, so leave headroom.

Sources

All sources checked on 2026-10-23.

1. Hugging Face, Qwen/Qwen3-14B (parameter count, license, repo date, 30-day downloads). https://huggingface.co/Qwen/Qwen3-14B
2. Qwen3-14B model card (context length, YaRN, thinking mode). https://huggingface.co/Qwen/Qwen3-14B/blob/main/README.md
3. Hugging Face, config.json (layers, KV heads, head size). https://huggingface.co/Qwen/Qwen3-14B/blob/main/config.json
4. Hugging Face, bartowski/Qwen_Qwen3-14B-GGUF file list. https://huggingface.co/bartowski/Qwen_Qwen3-14B-GGUF/tree/main
5. Hugging Face, Qwen/Qwen3-14B-GGUF (official GGUF files). https://huggingface.co/Qwen/Qwen3-14B-GGUF/tree/main
6. Ollama, qwen3 tags. https://ollama.com/library/qwen3/tags
7. NVIDIA, GeForce RTX 4060 family (8 GB / 16 GB). https://www.nvidia.com/en-us/geforce/graphics-cards/40-series/rtx-4060-4060ti/
8. NVIDIA, GeForce RTX 3060 family (12 GB). https://www.nvidia.com/en-us/geforce/graphics-cards/30-series/rtx-3060-3060ti/
9. NVIDIA, GeForce RTX 3090 specs (24 GB). https://www.nvidia.com/en-us/geforce/graphics-cards/30-series/rtx-3090-3090ti/
10. NVIDIA, GeForce RTX 4090 specs (24 GB). https://www.nvidia.com/en-us/geforce/graphics-cards/40-series/rtx-4090/
11. NVIDIA, GeForce RTX 5090 specs (32 GB). https://www.nvidia.com/en-us/geforce/graphics-cards/50-series/rtx-5090/
12. Apple, MacBook Pro / MacBook Air tech specs (unified memory options). https://www.apple.com/macbook-pro/specs/
13. Runpod, GPU pricing. https://www.runpod.io/pricing
14. VRAMNeeded comparison table (sources listed per model). https://vramneeded.com/compare/
15. Meta, Llama 3.1 model card (128K context). https://github.com/meta-llama/llama-models/blob/main/models/llama3_1/MODEL_CARD.md
16. VRAMNeeded, Qwen3.8 27B page (from its config.json). https://vramneeded.com/models/qwen3-8-27b/