Can RTX 4060 8GB run Qwen3-Coder-30B-A3B-Instruct?

NVIDIA · 8 GB GDDR6 · 272 GB/s bandwidth · Alibaba · 30.5B (active 3B) · MoE · model ctx up to 128K

No — Qwen3-Coder-30B-A3B-Instruct at Q4 needs ≈ 18.08 GB even at 4K context, beyond the RTX 4060 8GB's 8 GB GDDR6. It would only run with heavy CPU/disk offload. Decode at Q4/8K: ≈ 31.2 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	32.3 est.
Q4	8K default	18.38 GB	No	31.2 est.
Q4	32K	20.18 GB	No	25.9 est.
FP16	4K	62.3 GB	No	—
FP16	8K default	62.6 GB	No	—
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	RTX 4060 8GB VRAM
Q4	16.78 GB	0.6 GB	1 GB	18.38 GB	8 GB
FP16	61.0 GB	0.6 GB	1 GB	62.6 GB	8 GB

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

• 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-Coder-30B-A3B-Instruct →

Other models for the RTX 4060 8GB

• Qwen3-0.6B — ≈216.4 tok/s Q4 est.
• gemma-3-1b-it — ≈173.1 tok/s Q4 est.
• Llama-3.2-1B-Instruct — ≈144.2 tok/s Q4 est.
• Qwen3-1.7B — ≈86.5 tok/s Q4 est.
• Llama-3.2-3B-Instruct — ≈54.1 tok/s Q4 est.
• Phi-4-mini-instruct — ≈45.6 tok/s Q4 est.
• Phi-4-mini-reasoning — ≈45.6 tok/s Q4 est.
• gemma-3-4b-it — ≈44.4 tok/s Q4 est.

All models on the RTX 4060 8GB →