Can the RTX 4070 Ti run Qwen3-4B-Instruct-2507?

Yes. Qwen3-4B-Instruct-2507 fits the RTX 4070 Ti's 12 GB at Q4_K_M (about 3.8 GB needed), running at an estimated 201 tok/s. There's room for up to FP16 if you want higher quality.

VRAM
12 GB
Needs at Q4
3.8 GB
Headroom
8.2 GB
Speed (Q4_K_M)
~201 tok/s

Every quantization

Qwen3-4B-Instruct-2507 (4B) on 12 GB

QuantWeightsNeedsFitEst. speed
Q4_K_M2 GB3.8 GBFits easily~201 tok/s
Q5_K_M3 GB4.8 GBFits easily~134 tok/s
Q8_04 GB5.8 GBFits easily~101 tok/s
FP168 GB9.8 GBFits~50 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 4070 Ti's 504.2 GB/s bandwidth (XiongjieDai/GPU-Benchmarks-on-LLM-Inference). 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 4070 Ti

Frequently asked

Quick answers

Can the GeForce RTX 4070 Ti run Qwen3-4B-Instruct-2507?

Yes. Qwen3-4B-Instruct-2507 fits the RTX 4070 Ti's 12 GB at Q4_K_M (about 3.8 GB needed), running at an estimated 201 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 4070 Ti?

Roughly 201 tokens per second at Q4_K_M (single user, llama.cpp-style decode). This is an estimate from the RTX 4070 Ti's 504.2 GB/s memory bandwidth.