Qwen3-14B-f16-GGUF

This is a GGUF-quantized version of the Qwen/Qwen3-14B language model — a 14-billion-parameter LLM with deep reasoning, research-grade accuracy, and autonomous workflows. Converted for use with \llama.cpp, LM Studio, OpenWebUI, GPT4All, and more.

NEW: I have a custom model called Q3_HIFI, which is better than the standard Q3_K_M model. It is higher quality, smaller in size, and nearly the same speed as Q3_K_M.

It is listed under the 'f16' options because it's not an officially recognised type (at the moment).

Q3_HIFI

Pros:

• 🏆 Best quality with lowest perplexity of 9.38 (1.6% better than Q3_K_M, 3.4% better than Q3_K_S)
• 📦 Smaller than Q3_K_M (6.59 vs 6.81 GiB) while being significantly better quality
• 🎯 Uses intelligent layer-sensitive quantization (Q3_HIFI on sensitive layers, mixed q3_K/q4_K elsewhere)
• ⚡ Slightly faster than Q3_K_M (85.58 vs 85.40 TPS)

Cons:

• 🐢 Slower than Q3_K_S at 85.58 TPS (6.5% slower than Q3_K_S)
• 🔧 Custom quantization may have less community support

Best for: Production deployments where output quality matters, tasks requiring accuracy (reasoning, coding, complex instructions), or when you want the best quality-to-size ratio.

You can read more about how it compares to Q3_K_M and Q3_K_S here: Q3_Quantization_Comparison.md

You can also view a cross-model comparison of the Q3_HIFI type here.

Available Quantizations (from f16)

Level	Speed	Size	Recommendation
Q2_K	⚡ Fastest	5.75 GB	An excellent option but it failed the 'hello' test. Use with caution.
🥇 Q3_K_S	⚡ Fast	6.66 GB	🥇 Best overall model. Two first places and two 3rd places. Excellent results across the full temperature range.
🥉 Q3_K_M	⚡ Fast	7.32 GB	🥉 A good option - it came 1st and 3rd, covering both ends of the temperature range.
Q4_K_S	🚀 Fast	8.57 GB	Not recommended, two 2nd places in low temperature questions with no other appearances.
Q4_K_M	🚀 Fast	9.00 GB	Not recommended. A single 3rd place with no other appearances.
🥈 Q5_K_S	🐢 Medium	10.3 GB	🥈 A very good second place option. A top 3 finisher across the full temperature range.
Q5_K_M	🐢 Medium	10.5 GB	Not recommended. A single 3rd place with no other appearances.
Q6_K	🐌 Slow	12.1 GB	Not recommended. No top 3 finishes at all.
Q8_0	🐌 Slow	15.7 GB	Not recommended. A single 2nd place with no other appearances.

Certainly! Here's a polished and purpose-driven description for the Qwen3-14B model:

---

Why Use a 14B Model?

The Qwen3-14B model delivers serious intelligence in a locally runnable package, offering near-flagship performance while remaining feasible to run on a single high-end consumer GPU or a well-equipped CPU setup. It’s the optimal choice when you need strong reasoning, robust code generation, and deep language understanding—without relying on the cloud or massive infrastructure.

Highlights:

• State-of-the-art performance among open 14B-class models, excelling in reasoning, math, coding, and multilingual tasks
• Efficient inference with quantization: runs on a 24 GB GPU (e.g., RTX 4090) or even CPU with quantized GGUF/AWQ variants (~12–14 GB RAM usage)
• Strong contextual handling: supports long inputs and complex multi-step workflows, ideal for agentic or RAG-based systems
• Fully open and commercially usable, giving you full control over deployment and customization

It’s ideal for:

• Self-hosted AI assistants that understand nuance, remember context, and generate high-quality responses
• On-prem development environments needing local code completion, documentation, or debugging
• Private RAG or enterprise applications requiring accuracy, reliability, and data sovereignty
• Researchers and developers seeking a powerful, open-weight alternative to closed 10B–20B models

Choose Qwen3-14B when you’ve outgrown 7B–8B models but still want to run efficiently offline—balancing capability, control, and cost without sacrificing quality.

Build notes

All of these models (including Q3_HIFI) where built using these commands:

mkdir build
cmake -B build -DCMAKE_BUILD_TYPE=Release -DGGML_NATIVE=ON -DGGML_AVX=ON -DGGML_AVX2=ON -DGGML_CUDA=ON -DGGML_VULKAN=OFF -DLLAMA_CURL=OFF
cmake --build build --config Release -j 

NOTE: Vulkan support is specifically turned off here. Vulkan performance was much worse, so if you want Vulkan support you can rebuild these models yourself.

The quantisation for Q3_HIFI also used a 5000 chunk imatrix file for extra precision. You can re-use it here: Qwen3-14B-f16-imatrix-5000.gguf

You can use the Q3_HIFI GitHub repository to build it from source if you're interested (use the Q3_HIFI branch)https://github.com/geoffmunn/llama.cpp.

Model anaysis and rankings

There are two good candidates: Qwen3-14B-f16:Q3_K_S and Qwen3-14B-f16:Q5_K_M. These cover the full range of temperatures and are good at all question types.

Another good option would be Qwen3-14B-f16:Q3_K_M, with good finishes across the temperature range.

Qwen3-14B-f16:Q2_K got very good results and would have been a 1st or 2nd place candidate but was the only model to fail the 'hello' question which it should have passed.

You can read the results here: Qwen3-14b-analysis.md

If you find this useful, please give the project a ❤️ like.

Usage

Load this model using:

• OpenWebUI – self-hosted AI interface with RAG & tools
• LM Studio – desktop app with GPU support and chat templates
• GPT4All – private, local AI chatbot (offline-first)
• Or directly via llama.cpp

Each quantized model includes its own README.md and shares a common MODELFILE for optimal configuration.

Importing directly into Ollama should work, but you might encounter this error: Error: invalid character '<' looking for beginning of value. In this case try these steps:

1. wget https://huggingface.co/geoffmunn/Qwen3-14B/resolve/main/Qwen3-14B-f16%3AQ3_K_S.gguf (replace the quantised version with the one you want)
2. nano Modelfile and enter these details (again, replacing Q3_K_S with the version you want):

FROM ./Qwen3-14B-f16:Q3_K_S.gguf

# Chat template using ChatML (used by Qwen)
SYSTEM You are a helpful assistant

TEMPLATE "{{ if .System }}<|im_start|>system
{{ .System }}<|im_end|>{{ end }}<|im_start|>user
{{ .Prompt }}<|im_end|>
<|im_start|>assistant
"
PARAMETER stop <|im_start|>
PARAMETER stop <|im_end|>

# Default sampling
PARAMETER temperature 0.6
PARAMETER top_p 0.95
PARAMETER top_k 20
PARAMETER min_p 0.0
PARAMETER repeat_penalty 1.1
PARAMETER num_ctx 4096

The num_ctx value has been dropped to increase speed significantly.

3. Then run this command: ollama create Qwen3-14B-f16:Q3_K_S -f Modelfile

You will now see "Qwen3-14B-f16:Q3_K_S" in your Ollama model list.

These import steps are also useful if you want to customise the default parameters or system prompt.

Author

👤 Geoff Munn (@geoffmunn)
🔗 Hugging Face Profile

Disclaimer

This is a community conversion for local inference. Not affiliated with Alibaba Cloud or the Qwen team.