Can T4 16GB run Llama-3.2-3B-Instruct?

NVIDIA · 16 GB GDDR6 · 320 GB/s bandwidth · Meta · 3.2B · model ctx up to 128K

Yes — the T4 16GB (16 GB GDDR6) can run Llama-3.2-3B-Instruct. At Q4 with the default 8K context it needs ≈ 3.4 GB VRAM (weights 1.76 GB + KV cache 0.64 GB + 1 GB runtime overhead), which fits within 16 GB. Decode at Q4/8K: ≈ 63.6 tok/s (estimated, batch 1). FP16 (≈ 8.04 GB) also fits, at ≈ 17.5 tok/s (estimated). 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	3.08 GB	Yes	63.6 est.
Q4	8K default	3.4 GB	Yes	63.6 est.
Q4	32K	5.32 GB	Yes	63.6 est.
FP16	4K	7.72 GB	Yes	17.5 est.
FP16	8K default	8.04 GB	Yes	17.5 est.
FP16	32K	9.96 GB	Yes	17.5 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	1.76 GB	0.64 GB	1 GB	3.4 GB	16 GB
FP16	6.4 GB	0.64 GB	1 GB	8.04 GB	16 GB

Other GPUs that run Llama-3.2-3B-Instruct

• B200 192GB SXM — ≈1590.9 tok/s Q4 est.
• MI300X 192GB — ≈1054.0 tok/s Q4 est.
• H200 141GB SXM5 — ≈954.5 tok/s Q4 est.
• H100 80GB SXM5 — ≈666.2 tok/s Q4 est.
• Gaudi 3 128GB — ≈651.5 tok/s Q4 est.
• A100 80GB SXM4 — ≈405.5 tok/s Q4 est.
• RTX 5090 32GB — ≈356.4 tok/s Q4 est.
• RTX 4090 24GB — ≈200.5 tok/s Q4 est.

All GPUs for Llama-3.2-3B-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.
• 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.
• gemma-3-4b-it — ≈52.2 tok/s Q4 est.

All models on the T4 16GB →