Can I Run Qwen3-Coder on RTX 4090 Laptop (16 GB VRAM, 32 GB RAM)?

Written by Jakub Rusinowski · Last updated July 8, 2026

RTX 4090 Laptop (16 GB VRAM, 32 GB RAM) — what it gives a model

Usable memory for models	16 GB
Memory bandwidth	717 GB/s
Form factor	Laptop
Operating system	Windows or Linux
Memory upgradeable	Yes

Qwen3-Coder on RTX 4090 Laptop (16 GB VRAM, 32 GB RAM): memory by quantization

Quant	Memory needed	Fits 16 GB?	Max context	Est. speed	Download
F16	17.9 GB	✗ No	—	—	16 GB
Q8_0	10.4 GB	✓ Yes	32K	~52.3 tok/s	8.5 GB
Q6_K	8.5 GB	✓ Yes	32K	~64.5 tok/s	6.6 GB
Q5_K_M	7.6 GB	✓ Yes	64K	~72.2 tok/s	5.7 GB
Q4_K_M	6.8 GB	✓ Yes	64K	~81.4 tok/s	4.8 GB
Q3_K_M	5.4 GB	✓ Yes	64K	~103.6 tok/s	3.4 GB
Q2_K	4.6 GB	✓ Yes	64K	~122 tok/s	2.6 GB

Which Qwen3-Coder sizes fit

Variant	Needs at Q4_K_M	Fits?	Est. speed
Qwen3-Coder 480B-A35B (MoE)	291.6 GB	✗ Too large	—
Qwen3-Coder 80B-A3B (MoE)	51.6 GB	✗ Too large	—
Qwen3-Coder 8B	6.8 GB	✓ Fits	~81.4 tok/s

What to watch out for

• 2 larger variants of Qwen3-Coder do not fit and would need CPU offload or different hardware.
• This model does not publish its full attention configuration, so the KV-cache share of these figures is inferred from its parameter count rather than computed exactly.

RTX 4090 laptop limitations

• A mobile RTX 4090 carries 16 GB, not the desktop card's 24 GB, and is closer to a desktop 4080 in throughput — which is why it is modelled against that chip here.
• Sustained throughput depends on the chassis power limit; thin laptops throttle well below the quoted figures.

Recommended setup

Ollama or llama.cpp (CUDA/ROCm) — vLLM if you need concurrent serving

How these numbers are calculated

• Memory need = quantized weights + KV cache at 8,192 tokens (f16) + 0.8 GB runtime overhead.
• 16 GB of VRAM on the NVIDIA GeForce RTX 4080 at 717 GB/s.
• 32 GB of system RAM available for CPU offload when a model exceeds VRAM.
• Throughput is a memory-bandwidth roofline estimate, not a measurement. It is labelled "estimated" everywhere it appears.
• KV cache is inferred from the parameter count because this model does not publish its full attention configuration.

FAQ

Can I run Qwen3-Coder on RTX 4090 Laptop (16 GB VRAM, 32 GB RAM)?

Yes, comfortably — Qwen3-Coder 8B at Q8_0 needs about 10.4 GB of the 16 GB usable on RTX 4090 Laptop (16 GB VRAM, 32 GB RAM), leaving ~5.6 GB spare and running at ~52.3 tok/s (estimated), with room for about 32,768 tokens of context.

Which quantization of Qwen3-Coder should I use on RTX 4090 Laptop (16 GB VRAM, 32 GB RAM)?

Q8_0 — it needs about 10.4 GB of the 16 GB available, downloads as roughly 8.5 GB, and runs at an estimated 52.3 tokens/sec with up to 32K of context.

What limits Qwen3-Coder on RTX 4090 Laptop (16 GB VRAM, 32 GB RAM)?

Nothing binding — the model fits with headroom and generates at a usable speed on this hardware.

Which runtime should I use?

Ollama or llama.cpp (CUDA/ROCm) — vLLM if you need concurrent serving

Other Computers

• Qwen3-Coder on MacBook Pro M4 Max 128 GB
• Qwen3-Coder on MacBook Pro M4 Max 48 GB
• Qwen3-Coder on MacBook Pro M4 Pro 24 GB
• Qwen3-Coder on MacBook Air M4 16 GB

Other Models on RTX 4090 Laptop (16 GB VRAM, 32 GB RAM)

• SmolLM2 on RTX 4090 Laptop (16 GB VRAM, 32 GB RAM)
• StarCoder 2 on RTX 4090 Laptop (16 GB VRAM, 32 GB RAM)
• Aya 3B (Tiny Aya) on RTX 4090 Laptop (16 GB VRAM, 32 GB RAM)
• Yi 1.5 Family on RTX 4090 Laptop (16 GB VRAM, 32 GB RAM)
• Aya Expanse on RTX 4090 Laptop (16 GB VRAM, 32 GB RAM)

Qwen3-Coder on GPUs

• Qwen3-Coder on NVIDIA GeForce RTX 5070
• Qwen3-Coder on NVIDIA GeForce RTX 5060 Ti 8GB
• Qwen3-Coder on NVIDIA GeForce RTX 5060
• Qwen3-Coder on NVIDIA GeForce RTX 4070 Ti

What This Model Is Good At

• Best local LLMs for coding
• Best local LLMs for cybersecurity

Model & Tools

• Qwen3-Coder model page
• VRAM calculator
• Check your own hardware

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