Can the RTX 4060 Ti 16GB run Qwen3-4B-Instruct-2507?

Yes. Qwen3-4B-Instruct-2507 fits the RTX 4060 Ti 16GB's 16 GB at Q4_K_M (about 3.8 GB needed), running at an estimated 83 tok/s. There's room for up to FP16 if you want higher quality.

VRAM
16 GB
Needs at Q4
3.8 GB
Headroom
12.2 GB
Speed (Q4_K_M)
~83 tok/s

Every quantization

Qwen3-4B-Instruct-2507 (4B) on 16 GB

QuantWeightsNeedsFitEst. speed
Q4_K_M2 GB3.8 GBFits easily~83 tok/s
Q5_K_M3 GB4.8 GBFits easily~55 tok/s
Q8_04 GB5.8 GBFits easily~41 tok/s
FP168 GB9.8 GBFits~21 tok/s

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

Copy, paste, go

llama-server -hf unsloth/Qwen3-4B-Instruct-2507-GGUF -ngl 99

Ollama pulls a Q4 build by default. Pick a larger quant only if the table above says it fits. Full Qwen3-4B-Instruct-2507 details →

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Same model, other hardware - and other models on this card

Qwen3-4B-Instruct-2507 on other hardware

Also runs on the RTX 4060 Ti 16GB

Frequently asked

Quick answers

Can the NVIDIA RTX 4060 Ti 16GB run Qwen3-4B-Instruct-2507?

Yes. Qwen3-4B-Instruct-2507 fits the RTX 4060 Ti 16GB's 16 GB at Q4_K_M (about 3.8 GB needed), running at an estimated 83 tok/s. There's room for up to FP16 if you want higher quality.

How much VRAM does Qwen3-4B-Instruct-2507 need?

About 2 GB of weights at Q4_K_M, 4 GB at Q8_0 and 8 GB at FP16. Add roughly 1.8 GB for the KV cache and runtime at modest context lengths.

How fast is Qwen3-4B-Instruct-2507 on the RTX 4060 Ti 16GB?

Roughly 83 tokens per second at Q4_K_M (single user, llama.cpp-style decode). This is an estimate from the RTX 4060 Ti 16GB's 288 GB/s memory bandwidth.