Can the RTX 4060 Ti 16GB run Qwen3-30B-A3B-Instruct-2507?
Not fully, but it's usable. Qwen3-30B-A3B-Instruct-2507 needs about 19.8 GB at Q4_K_M and the RTX 4060 Ti 16GB has 16 GB - yet as a mixture-of-experts model it reads only ~3B parameters per token, so you can keep attention on the GPU and the experts in system RAM (llama.cpp --n-cpu-moe) and still get interactive speeds with 32 GB+ of RAM.
Every quantization
Qwen3-30B-A3B-Instruct-2507 (30.5B, 3B active) on 16 GB
| Quant | Weights | Needs | Fit | Est. speed |
|---|---|---|---|---|
| Q4_K_M | 18 GB | 19.8 GB | Too big | - |
| Q5_K_M | 22 GB | 23.8 GB | Too big | - |
| Q8_0 | 33 GB | 34.8 GB | Too big | - |
| FP16 | 61 GB | 62.8 GB | Too big | - |
Estimates. Needs = weights + 1.8 GB for KV cache and runtime at modest context. Speed is single-stream decode scaled from the RTX 4060 Ti 16GB's 288 GB/s bandwidth (Estimated from memory bandwidth (llama.cpp decode)). Long contexts need more memory and run slower.
Run it with expert offload
Attention on the GPU, experts in system RAM
llama-server -hf unsloth/Qwen3-30B-A3B-Instruct-2507-GGUF -ngl 99 --n-cpu-moe 99Lower --n-cpu-moe step by step until the RTX 4060 Ti 16GB's 16 GB is nearly full - each expert layer moved back to the GPU speeds things up. Needs about 8 GB+ of free system RAM.
What to do instead
Two ways forward
Cheapest cards that run it
- NVIDIA Tesla P40 - 24 GB, ~$159
- AMD Radeon RX 7900 XT - 20 GB, ~$699
- NVIDIA RTX 3090 - 24 GB, ~$750
- AMD Radeon RX 7900 XTX - 24 GB, ~$849
Models that fit the RTX 4060 Ti 16GB
- Qwen2.5-Coder-14B-Instruct - 14.8B, 9 GB at Q4
- Qwen3 14B - 14B, 8 GB at Q4
- Phi-4 - 14B, 8.5 GB at Q4
- Gemma 4 12B - 12B, 7 GB at Q4
Compare
Same model, other hardware - and other models on this card
Qwen3-30B-A3B-Instruct-2507 on other hardware
- RTX 5090~387 tok/s
- RTX 5090 D~387 tok/s
- RTX 6000D~339 tok/s
- RTX 4090~236 tok/s
- Radeon RX 7900 XTX~207 tok/s
- RTX 3090~206 tok/s
- MacBook Pro 16" M4 Max~118 tok/s
- Arc Pro B60~99 tok/s
Also runs on the RTX 4060 Ti 16GB
- Qwen2.5-Coder-14B-Instruct~18 tok/s
- Qwen3 14B~21 tok/s
- Phi-4~19 tok/s
- Gemma 4 12B~24 tok/s
- Qwen3.5 9B~28 tok/s
- Llama 3.1 8B~30 tok/s
- Mistral-7B-Instruct-v0.2~41 tok/s
- Qwen3.5 4B~55 tok/s
Frequently asked
Quick answers
Can the NVIDIA RTX 4060 Ti 16GB run Qwen3-30B-A3B-Instruct-2507?
Not fully, but it's usable. Qwen3-30B-A3B-Instruct-2507 needs about 19.8 GB at Q4_K_M and the RTX 4060 Ti 16GB has 16 GB - yet as a mixture-of-experts model it reads only ~3B parameters per token, so you can keep attention on the GPU and the experts in system RAM (llama.cpp --n-cpu-moe) and still get interactive speeds with 32 GB+ of RAM.
How much VRAM does Qwen3-30B-A3B-Instruct-2507 need?
About 18 GB of weights at Q4_K_M, 33 GB at Q8_0 and 61 GB at FP16. Add roughly 1.8 GB for the KV cache and runtime at modest context lengths.
How fast is Qwen3-30B-A3B-Instruct-2507 on the RTX 4060 Ti 16GB?
It doesn't fit in the RTX 4060 Ti 16GB's memory, so speed depends on how many layers you offload to system RAM - typically single-digit tokens per second or lower.