Can I run it?
Can I run Swift Qwen3.8 27B on GeForce RTX 5060 Ti 16GB?
Tight fit
Tight fit fully on your GeForce RTX 5060 Ti 16GB at Q3_K_L, 8K context, about 17–23 tok/s. Weights 13.4 GB plus 0.57 GB of KV cache — 14.4 GB of 14.7 GB usable.
Best quantQ3_K_L
Working context8K
Max that fits8K
Speed17–23 tok/sEstimate from memory bandwidth, not a benchmark. Method.
Weights13.4 GB
KV cache0.57 GB
Total14.4 GB
Usable14.7 GB
Swift Qwen3.8 27B details · GeForce RTX 5060 Ti 16GB page · How this estimate is made
Run commands
llama.cpp
llama-server -hf ukisai/Swift-Qwen3.8-27B-GGUF:Q3_K_L -c 8192 -ngl 64
No matching name in the Ollama library snapshot.
LM Studio
lms get ukisai/Swift-Qwen3.8-27B-GGUF # Then load quant Q3_K_L
Assumptions
- Estimate from memory bandwidth, not a benchmark. Decode speed uses 55–75% of peak bandwidth.
- 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%.
- Q3_K_L 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.
Larger models that also fit
- Can I run Qwen3.8 27B on GeForce RTX 5060 Ti 16GB? — Tight fit, UD-IQ4_XS, 17–23 tok/s (estimate)
- Can I run Qwen3.6 35B A3B on GeForce RTX 5060 Ti 16GB? — Tight fit, UD-IQ3_S, 43–59 tok/s (estimate)
- Can I run Qwen3.8 35B A3B Distill on GeForce RTX 5060 Ti 16GB? — Tight fit, Q2_K, 42–58 tok/s (estimate)
Updated 2026-10-24. Profile on this page is the device default plus 32 GB of DDR5-5600 system RAM.