Can you run Qwen3 8B on RTX 4070 Ti SUPER?
Qwen3 8B Q4_K_M on the NVIDIA GeForce RTX 4070 Ti SUPER — verdict, VRAM math and estimated speed.
Yes, the NVIDIA GeForce RTX 4070 Ti SUPER runs Qwen3 8B. 6.5 GB weights at Q4_K_M vs 14.4 GB usable VRAM; ~72 tok/s est..
VRAM math and speed are ModelFit engine estimates, not measurements. Commands are registry-verified Ollama tags.
Cite this page: ModelFit, Qwen3 8B on RTX 4070 Ti SUPER, https://modelfit.io/can-i-run/qwen3-8b-q4-on-rtx-4070-ti-super/, updated August 2026, CC BY 4.0.
Last updated: August 16, 2026 · Editor: ModelFit Team
Memory math: weights + context vs budget
Weights take 6.5 GB. Context costs extra KV-cache on top — this is where long-context sessions break on cards that technically fit the weights.
KV-cache figures assume an fp16 cache, the llama.cpp/Ollama default. Standard GQA models use a size-class estimate (8 KV heads x 128 head dim class); hybrid linear-attention models (Qwen3.5/3.6, Qwen3-Next) use the exact per-token cost from their published config, since only their sparse full-attention layers cache KV. A q8_0 KV cache roughly halves either figure. Estimates, not measurements.
Qwen3 8B on RTX 4070 Ti SUPER: FAQ
Can the NVIDIA GeForce RTX 4070 Ti SUPER run Qwen3 8B?
Yes. Qwen3 8B (Q4_K_M) loads in about 6.5 GB and the RTX 4070 Ti SUPER offers 14.4 GB of usable VRAM, leaving headroom for context. Expect at roughly 72 tokens/sec (est.).
How much VRAM does Qwen3 8B need?
About 6.5 GB for the weights at Q4_K_M, plus KV-cache for context: roughly 2.0 GB extra at 16k tokens. The RTX 4070 Ti SUPER budget is 14.4 GB (16 GB x 90%).
What is the best quantization of Qwen3 8B for the RTX 4070 Ti SUPER?
Stick with the Q4_K_M build at 6.5 GB — every heavier quant exceeds the 14.4 GB usable VRAM.
What GPU do I need to run Qwen3 8B comfortably?
The RTX 4070 Ti SUPER already runs Qwen3 8B comfortably. Larger cards only buy you longer context or a heavier quant.