Qwen3 14B quants compared

26 GGUF builds by real file size, probed from bartowski/Qwen_Qwen3-14B-GGUF on Hugging Face (2026-09-02). 14B params.

Download Qwen3 14B Q4_K_M (8.38 GB) — it fits 16 GB of memory with 16k context. With Ollama: ollama run qwen3:14b-q4_K_M

Every Qwen3 14B quant by real file size

QuantWeights+ KV (16k)TotalFits comfortably inQuality
BF1627.51 GB3.0 GB30.5 GB48 GBFull precision (lossless)
Q8_014.62 GB3.0 GB17.6 GB24 GBNear-lossless
Q6_K_L11.64 GB3.0 GB14.6 GB24 GBExcellent
Q6_K11.29 GB3.0 GB14.3 GB16 GBExcellent
Q5_K_L10.24 GB3.0 GB13.2 GB16 GBVery high
Q5_K_M9.79 GB3.0 GB12.8 GB16 GBVery high
Q5_K_S9.56 GB3.0 GB12.6 GB16 GBVery high
Q4_K_M *8.38 GB3.0 GB11.4 GB16 GBHigh — the default pick
Q4_K_L8.92 GB3.0 GB11.9 GB16 GBHigh
Q4_18.74 GB3.0 GB11.7 GB16 GBHigh
Q4_K_S7.98 GB3.0 GB11.0 GB16 GBHigh
Q4_07.96 GB3.0 GB11.0 GB16 GBHigh
IQ4_NL7.95 GB3.0 GB10.9 GB16 GBHigh
IQ4_XS7.55 GB3.0 GB10.6 GB12 GBHigh
Q3_K_XL7.99 GB3.0 GB11.0 GB16 GBAcceptable — visible loss
Q3_K_L7.36 GB3.0 GB10.4 GB12 GBAcceptable — visible loss
Q3_K_M6.82 GB3.0 GB9.8 GB12 GBAcceptable — visible loss
IQ3_M6.41 GB3.0 GB9.4 GB12 GBAcceptable — visible loss
Q3_K_S6.2 GB3.0 GB9.2 GB12 GBAcceptable — visible loss
IQ3_XS5.94 GB3.0 GB8.9 GB12 GBAcceptable — visible loss
IQ3_XXS5.53 GB3.0 GB8.5 GB12 GBAcceptable — visible loss
Q2_K_L6.07 GB3.0 GB9.1 GB12 GBExperimental — not ranked — never recommended
Q2_K5.36 GB3.0 GB8.4 GB12 GBExperimental — not ranked — never recommended
IQ2_M4.96 GB3.0 GB8.0 GB12 GBExperimental — not ranked — never recommended
IQ2_S4.62 GB3.0 GB7.6 GB12 GBExperimental — not ranked — never recommended
IQ2_XS4.37 GB3.0 GB7.4 GB12 GBExperimental — not ranked — never recommended

* default pick. Weights = real GGUF file sizes from bartowski/Qwen_Qwen3-14B-GGUF (probed 2026-09-02). KV = fp16 estimate; a q8_0 cache roughly halves it. "Comfortable" = weights + KV within 90% of memory.

Best Qwen3 14B quant by memory

MemoryRecommended quantTotal (16k ctx)
12 GBIQ4_XS10.6 GB
16 GBQ6_K14.3 GB
24 GBQ8_017.6 GB
48 GBBF1630.5 GB

Why we don't rank Qwen3 14B's 2-bit quants

Quants at 2 bits per weight or below (Q2_K, IQ2, IQ1, TQ1) cut file size by roughly half versus Q4, but the quality collapse is steep and non-linear: perplexity spikes, instruction-following degrades, and hallucinations rise. A model that answers faster but wrong is not a smaller model — it is a worse one. ModelFit lists these builds for completeness but never ranks or recommends them.

Run Qwen3 14B on your GPU

RTX 3060
12 GB — verdict
RTX 4060
8 GB — verdict
RTX 4060 Ti
16 GB — verdict
RTX 4070
12 GB — verdict
RTX 4070 Ti SUPER
16 GB — verdict
RTX 3090
24 GB — verdict
RTX 4090
24 GB — verdict
RTX 5070 Ti
16 GB — verdict
RTX 5080
16 GB — verdict
RTX 5090
32 GB — verdict

Frequently asked questions

What is the best quantization of Qwen3 14B?

Q4_K_M is the default pick: 8.38 GB of weights, high — the default pick quality, fitting comfortably in 16 GB of memory (weights + 16k context KV-cache). Go Q6_K or Q8_0 if you have headroom.

How much memory does Qwen3 14B need?

At Q4_K_M, Qwen3 14B needs 8.38 GB for the weights plus ~3.0 GB of KV-cache at 16k context — about 11.4 GB total, so a 16 GB card or Mac (90% usable budget) runs it comfortably.

Should I use a Q2_K or IQ2 quant of Qwen3 14B?

No. Qwen3 14B at 2 bits per weight is a visibly worse model — quality collapse at that bitrate is steep, not gradual. If only a 2-bit build fits your memory, run a smaller model at Q4_K_M instead. ModelFit lists these builds but never recommends them.

Cite this page

ModelFit: Qwen3 14B quantization comparison (real GGUF file sizes).
https://modelfit.io/quant-compare/qwen3-14b/ (data probed 2026-09-02, CC BY 4.0).