Can I run it?

Can I run Qwen3 Coder Next on GeForce RTX 4060 Ti 16GB?

Expert offload

Expert offload on your GeForce RTX 4060 Ti 16GB: UD-IQ4_XS keeps shared weights on the GPU and about 23.7 GB of experts in system RAM. About 22–30 tok/s. Active-parameter speed is approximate.

Qwen3 Coder Next details · GeForce RTX 4060 Ti 16GB page · How this estimate is made

Run commands

# Expert offload is approximate. llama.cpp can pin experts to CPU:
#   -ot "\\.ffn_.*_exps\\.=CPU"
llama-server -hf unsloth/Qwen3-Coder-Next-GGUF:UD-IQ4_XS -c 8192 -ngl 99

ollama run qwen3-coder-next

lms get unsloth/Qwen3-Coder-Next-GGUF
# Then load quant UD-IQ4_XS

Assumptions

• Estimate, not a benchmark. Decode speed uses a memory-bandwidth model at 55–75% of peak.
• KV cache is FP16. Engine overhead assumes llama.cpp (0.3 GB + 1% of the weights). Ollama is closer to 0.9 GB + 3%, LM Studio closer to 1 GB + 3%.
• UD-IQ4_XS size is the real GGUF file size from Hugging Face.
• 0.5 GB is reserved because this GPU also drives a display, then 5% of the rest is left free.
• Speed is quoted at 8K context, not at the maximum context that still fits.
• Mixture-of-experts split is approximate: shared layers on the GPU, expert tensors in system RAM. Speed uses active parameters.

Models that fit this machine more comfortably

• Can I run Kwaipilot KAT Coder V2.5 Dev on GeForce RTX 4060 Ti 16GB? — Tight fit, IQ3_XXS, 57–78 tok/s
• Can I run Qwen2.5 Coder 32B Instruct on GeForce RTX 4060 Ti 16GB? — Tight fit, IQ3_XXS, 11–14 tok/s
• Can I run Qwen3 Coder 30B A3B Instruct on GeForce RTX 4060 Ti 16GB? — Tight fit, UD-Q3_K_XL, 73–100 tok/s

Updated 2026-10-17. Profile on this page is the device default plus 32 GB of DDR5-5600 system RAM.