Can I run it?

Can I run Qwen3.8 35B A3B Distill on GeForce RTX 4060 Ti 8GB?

Expert offload

Expert offload on your GeForce RTX 4060 Ti 8GB: Q4_K_M keeps shared weights on the GPU and about 16.8 GB of experts in system RAM. About 78–106 tok/s. Active-parameter speed is approximate.

Qwen3.8 35B A3B Distill details · GeForce RTX 4060 Ti 8GB 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 empero-ai/Qwen3.8-35B-A3B-Distill-GGUF:Q4_K_M -c 8192 -ngl 99

ollama run qwen3.8

lms get empero-ai/Qwen3.8-35B-A3B-Distill-GGUF
# Then load quant Q4_K_M

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%.
• Q4_K_M 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 Qwen3.8 27B on GeForce RTX 4060 Ti 8GB? — Tight fit, UD-IQ1_M, 23–31 tok/s
• Can I run gemma 3 27b it on GeForce RTX 4060 Ti 8GB? — Tight fit, UD-IQ1_S, 23–31 tok/s
• Can I run medgemma 27b text it on GeForce RTX 4060 Ti 8GB? — Tight fit, UD-IQ1_S, 21–29 tok/s

Updated 2026-10-24. Profile on this page is the device default plus 32 GB of DDR5-5600 system RAM.