Can you run Qwen3 14B on RTX 5090?
Qwen3 14B Q4_K_M on the NVIDIA GeForce RTX 5090 — verdict, VRAM math and estimated speed.
Yes, the NVIDIA GeForce RTX 5090 runs Qwen3 14B. 11 GB weights at Q4_K_M vs 28.8 GB usable VRAM; ~90 tok/s est..
VRAM math and speed are ModelFit engine estimates, not measurements. Commands are registry-verified Ollama tags.
Cite this page: ModelFit, Qwen3 14B on RTX 5090, https://modelfit.io/can-i-run/qwen3-14b-q4-on-rtx-5090/, updated August 2026, CC BY 4.0.
Last updated: August 16, 2026 · Editor: ModelFit Team
Memory math: weights + context vs budget
Weights take 11 GB. Context costs extra KV-cache on top — this is where long-context sessions break on cards that technically fit the weights.
KV-cache figures assume an fp16 cache, the llama.cpp/Ollama default. Standard GQA models use a size-class estimate (8 KV heads x 128 head dim class); hybrid linear-attention models (Qwen3.5/3.6, Qwen3-Next) use the exact per-token cost from their published config, since only their sparse full-attention layers cache KV. A q8_0 KV cache roughly halves either figure. Estimates, not measurements.
Qwen3 14B on RTX 5090: FAQ
Can the NVIDIA GeForce RTX 5090 run Qwen3 14B?
Yes. Qwen3 14B (Q4_K_M) loads in about 11 GB and the RTX 5090 offers 28.8 GB of usable VRAM, leaving headroom for context. Expect at roughly 90 tokens/sec (est.).
How much VRAM does Qwen3 14B need?
About 11 GB for the weights at Q4_K_M, plus KV-cache for context: roughly 3.0 GB extra at 16k tokens. The RTX 5090 budget is 28.8 GB (32 GB x 90%).
What is the best quantization of Qwen3 14B for the RTX 5090?
The Q8_0 build is the highest quality that fits (15.9 GB vs 28.8 GB usable). The Q4_K_M build at 11 GB leaves more room for long context.
What GPU do I need to run Qwen3 14B comfortably?
The RTX 5090 already runs Qwen3 14B comfortably. Larger cards only buy you longer context or a heavier quant.