Can T4 16GB run Qwen3-30B-A3B-Instruct-2507?

NVIDIA · 16 GB GDDR6 · 320 GB/s bandwidth · Alibaba · 30.5B (active 3B) · MoE · model ctx up to 256K

No — Qwen3-30B-A3B-Instruct-2507 at Q4 needs ≈ 18.08 GB even at 4K context, beyond the T4 16GB's 16 GB GDDR6. It would only run with heavy CPU/disk offload. Decode at Q4/8K: ≈ 147.0 tok/s (estimated, batch 1). FP16 needs ≈ 62.6 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	18.08 GB	No	151.9 est.
Q4	8K default	18.38 GB	No	147.0 est.
Q4	32K	20.18 GB	No	121.9 est.
FP16	4K	62.3 GB	No	3.5 est.
FP16	8K default	62.6 GB	No	3.5 est.
FP16	32K	64.4 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	T4 16GB VRAM
Q4	16.78 GB	0.6 GB	1 GB	18.38 GB	16 GB
FP16	61.0 GB	0.6 GB	1 GB	62.6 GB	16 GB

Other GPUs that run Qwen3-30B-A3B-Instruct-2507

• 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 5090 32GB — ≈380.1 tok/s Q4 est.
• RTX 4090 24GB — ≈213.8 tok/s Q4 est.

All GPUs for Qwen3-30B-A3B-Instruct-2507 →

Other models for the T4 16GB

• Qwen3-0.6B — ≈254.5 tok/s Q4 est.
• gemma-3-1b-it — ≈203.6 tok/s Q4 est.
• Llama-3.2-1B-Instruct — ≈169.7 tok/s Q4 est.
• Qwen3-1.7B — ≈101.8 tok/s Q4 est.
• Llama-3.2-3B-Instruct — ≈63.6 tok/s Q4 est.
• gpt-oss-20b — ≈56.6 tok/s Q4 est.
• Phi-4-mini-instruct — ≈53.6 tok/s Q4 est.
• Phi-4-mini-reasoning — ≈53.6 tok/s Q4 est.

All models on the T4 16GB →