Can RX 7900 GRE 16GB run Qwen2.5-Coder-32B-Instruct?

AMD · 16 GB GDDR6 · 576 GB/s bandwidth · Alibaba · 32.8B · model ctx up to 32K

No — Qwen2.5-Coder-32B-Instruct at Q4 needs ≈ 22.32 GB even at 4K context, beyond the RX 7900 GRE 16GB's 16 GB GDDR6. It would only run with heavy CPU/disk offload. Decode at Q4/8K: ≈ 12.5 tok/s (estimated, batch 1). FP16 needs ≈ 73.16 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	22.32 GB	No	16.4 est.
Q4	8K default	25.6 GB	No	12.5 est.
Q4	32K	45.28 GB	No	4.0 est.
FP16	4K	69.88 GB	No	—
FP16	8K default	73.16 GB	No	—
FP16	32K	92.84 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	RX 7900 GRE 16GB VRAM
Q4	18.04 GB	6.56 GB	1 GB	25.6 GB	16 GB
FP16	65.6 GB	6.56 GB	1 GB	73.16 GB	16 GB

Other GPUs that run Qwen2.5-Coder-32B-Instruct

• B200 192GB SXM — ≈155.2 tok/s Q4 est.
• MI300X 192GB — ≈102.8 tok/s Q4 est.
• H200 141GB SXM5 — ≈93.1 tok/s Q4 est.
• H100 80GB SXM5 — ≈65.0 tok/s Q4 est.
• Gaudi 3 128GB — ≈63.6 tok/s Q4 est.
• A100 80GB SXM4 — ≈39.6 tok/s Q4 est.
• RTX 5090 32GB — ≈34.8 tok/s Q4 est.
• RTX 6000 Ada 48GB — ≈18.6 tok/s Q4 est.

All GPUs for Qwen2.5-Coder-32B-Instruct →

Other models for the RX 7900 GRE 16GB

• Qwen3-0.6B — ≈458.2 tok/s Q4 est.
• gemma-3-1b-it — ≈366.5 tok/s Q4 est.
• Llama-3.2-1B-Instruct — ≈305.5 tok/s Q4 est.
• Qwen3-1.7B — ≈183.3 tok/s Q4 est.
• Llama-3.2-3B-Instruct — ≈114.5 tok/s Q4 est.
• gpt-oss-20b — ≈101.8 tok/s Q4 est.
• Phi-4-mini-instruct — ≈96.5 tok/s Q4 est.
• Phi-4-mini-reasoning — ≈96.5 tok/s Q4 est.

All models on the RX 7900 GRE 16GB →