Can T4 16GB run Qwen2.5-Coder-7B-Instruct?

NVIDIA · 16 GB GDDR6 · 320 GB/s bandwidth · Alibaba · 7.6B · model ctx up to 32K

Yes — the T4 16GB (16 GB GDDR6) can run Qwen2.5-Coder-7B-Instruct. At Q4 with the default 8K context it needs ≈ 6.7 GB VRAM (weights 4.18 GB + KV cache 1.52 GB + 1 GB runtime overhead), which fits within 16 GB. Decode at Q4/8K: ≈ 26.8 tok/s (estimated, batch 1). FP16 needs ≈ 17.72 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	5.94 GB	Yes	26.8 est.
Q4	8K default	6.7 GB	Yes	26.8 est.
Q4	32K	11.26 GB	Yes	26.8 est.
FP16	4K	16.96 GB	No	18.7 est.
FP16	8K default	17.72 GB	No	17.2 est.
FP16	32K	22.28 GB	No	10.9 est.

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	4.18 GB	1.52 GB	1 GB	6.7 GB	16 GB
FP16	15.2 GB	1.52 GB	1 GB	17.72 GB	16 GB

Other GPUs that run Qwen2.5-Coder-7B-Instruct

• B200 192GB SXM — ≈669.9 tok/s Q4 est.
• MI300X 192GB — ≈443.8 tok/s Q4 est.
• H200 141GB SXM5 — ≈401.9 tok/s Q4 est.
• H100 80GB SXM5 — ≈280.5 tok/s Q4 est.
• Gaudi 3 128GB — ≈274.3 tok/s Q4 est.
• A100 80GB SXM4 — ≈170.7 tok/s Q4 est.
• RTX 5090 32GB — ≈150.0 tok/s Q4 est.
• RTX 4090 24GB — ≈84.4 tok/s Q4 est.

All GPUs for Qwen2.5-Coder-7B-Instruct →

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 →