Qwen3 14B Instruct
14BAlibaba Qwen3
Qwen3 14B — best balance of reasoning and VRAM in the 2026 Qwen lineup.
41K
Max Context
4
Quant Variants
GGUF Q5_K_M
Best Quality
98.8%
Accuracy Retained
Quantization Variants
Per-quant VRAM, quality loss, and inference speed on RTX 4090
Measured = run on this site · Estimated = not run here: calculated, or a figure this site has not verified · Community = public reports
Similar models
Compare with Qwen3 8BQwen3 8B Instruct
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Qwen3 32B Instruct
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Running Qwen3 14B Instruct locally
At Q4_K_M and 4K of context, Qwen3 14B Instruct needs about 10.0 GB — 8.5 GB of weights, 0.63 GB of KV cache and a 0.9 GB activation buffer. The smallest cards in this index that clear that comfortably are 12 GB ones — 10 of them, from the RTX 3060 12G up — and 65 of the 78 cards here do. These are calculated figures, not measurements: the estimate stops counting a card as comfortable at 88% of its VRAM, which is roughly the room a desktop session needs.
What longer context costs
Going from 4K to 32K adds about 4.37 GB, taking the total to 14.9 GB. Weights do not move with context — only the KV cache does, and it grows linearly, so this is the number to watch when planning for long documents. The model's native window is 40K; holding all of it at Q4_K_M would need about 16 GB.
Which build to download
This index tracks 3 formats for it — GGUF, AWQ, EXL2 — across 4 levels. Q5_K_M carries the lowest published perplexity loss at 1.2%. The fastest level measured here is EXL2 4.65bpw at 125 tok/s on an RTX 4090, batch 1. GGUF runs on llama.cpp and Ollama across NVIDIA, AMD and Apple silicon; AWQ and GPTQ target vLLM on CUDA and ROCm; EXL2 is ExLlamaV2 — CUDA only, and now archived, so local use rather than serving.
Common questions
- How much VRAM does Qwen3 14B Instruct need?
- About 10.0 GB at Q4_K_M with 4K of context and batch 1, rising to roughly 14.9 GB at 32K. That figure is weights plus KV cache plus a 10% activation buffer, calculated from the model's architecture rather than measured on a card.
- Will Qwen3 14B Instruct run on a 12GB GPU?
- Yes — at Q4_K_M and 4K context it needs about 10.0 GB, which leaves 2.0 GB spare on a RTX 3060 12G, or on any of the other 12 GB cards here (10 in all). That is the smallest size in this index that clears it comfortably; 65 of 78 do.
- Which quantization of Qwen3 14B Instruct should I use?
- Q5_K_M has the lowest published quality loss (1.2%), and Q4_K_M is the level most people run. All 4 levels in the index are Q4_K_M, Q5_K_M, AWQ INT4, EXL2 4.65bpw.
Where this model fits
Sized at Q4_K_M with a 4K context window, smallest card first. Comfortable means the estimate uses at most 88% of the memory.
- Runs comfortably on
- RTX 3060 12G12GB · +2.0Radeon RX 6700 XT12GB · +2.0Radeon RX 7700 XT12GB · +2.0Arc B580 12G12GB · +2.0RTX 4070 Ti12GB · +2.0
- Tight but possible
- Arc B570 10G10GBRTX 3080 10G10GB
- Just misses
- RTX 4060+2.0GB overRTX 4060 Ti 8G+2.0GB over
- Compared here
- GGUF vs AWQGGUF vs EXL2AWQ vs EXL2
- Try it yourself
- Size it in the calculatorGenerate the run command
How to actually run this
Deployment guides for this model and this class of hardware.
- What Can You Run on RTX 4060 Ti 16G? 3 min read·Beginner·covers this model
- Mac M3 Pro: Realistic Model Limits 4 min read·Beginner·covers this model
- Intel Arc: llama.cpp (SYCL) or Ollama (Vulkan) 5 min read·Intermediate·covers this model
- What to Run on a 12GB GPU (RTX 3060 12G, 4070, 5070) 5 min read·Beginner·covers this model
This page's figures change when the model or the runtime does.Last updated 2026-10-20 RSS → /feed.xml