Can RTX 4070 Ti Super 16GB run Qwen3-Coder-Next?

NVIDIA · 16 GB GDDR6X · 672 GB/s bandwidth · Alibaba · 79.7B (active 3B) · MoE · model ctx up to 256K

No — Qwen3-Coder-Next at Q4 needs ≈ 45.14 GB even at 4K context, beyond the RTX 4070 Ti Super 16GB's 16 GB GDDR6X. It would only run with heavy CPU/disk offload. Decode at Q4/8K: ≈ 50.5 tok/s (estimated, batch 1). FP16 needs ≈ 161.0 GB — does not fit on this card. Estimates come from memory-bandwidth math; rows tagged measured override estimates. Relative ranking is more reliable than absolute tok/s. Methodology.

Fit & speed by quant and context

Quant	Context	VRAM needed	Fits?	tok/s (decode)
Q4	4K	45.14 GB	No	51.2 est.
Q4	8K default	45.44 GB	No	50.5 est.
Q4	32K	47.24 GB	No	46.7 est.
FP16	4K	160.7 GB	No	—
FP16	8K default	161.0 GB	No	—
FP16	32K	162.8 GB	No	—

Fits = weights + KV(ctx) + 1 GB overhead ≤ 95% of VRAM. Measured anchors are context-agnostic; the fit verdict is recomputed per context. A missing tok/s means the model is far beyond this card (offload-only territory).

VRAM breakdown at 8K context

Quant	Weights	KV cache	Overhead	Total needed	RTX 4070 Ti Super 16GB VRAM
Q4	43.84 GB	0.6 GB	1 GB	45.44 GB	16 GB
FP16	159.4 GB	0.6 GB	1 GB	161.0 GB	16 GB

Other GPUs that run Qwen3-Coder-Next

• B200 192GB SXM — ≈1697.0 tok/s Q4 est.
• MI300X 192GB — ≈1124.2 tok/s Q4 est.
• H200 141GB SXM5 — ≈1018.2 tok/s Q4 est.
• H100 80GB SXM5 — ≈710.6 tok/s Q4 est.
• Gaudi 3 128GB — ≈694.9 tok/s Q4 est.
• A100 80GB SXM4 — ≈432.5 tok/s Q4 est.
• RTX 6000 Ada 48GB — ≈203.6 tok/s Q4 est.
• L40S 48GB — ≈183.3 tok/s Q4 est.

All GPUs for Qwen3-Coder-Next →

Other models for the RTX 4070 Ti Super 16GB

• Qwen3-0.6B — ≈534.5 tok/s Q4 est.
• gemma-3-1b-it — ≈427.6 tok/s Q4 est.
• Llama-3.2-1B-Instruct — ≈356.4 tok/s Q4 est.
• Qwen3-1.7B — ≈213.8 tok/s Q4 est.
• Llama-3.2-3B-Instruct — ≈199.0 tok/s Q4 measured
• gpt-oss-20b — ≈118.8 tok/s Q4 est.
• Phi-4-mini-instruct — ≈112.5 tok/s Q4 est.
• Phi-4-mini-reasoning — ≈112.5 tok/s Q4 est.

All models on the RTX 4070 Ti Super 16GB →