How much VRAM does Qwen3-Coder-Next need?

Qwen3-Coder-Next (80B parameters) requires approximately 52.4 GB of memory with Q4_K_M quantization.

What is the best quantization for Qwen3-Coder-Next?

The recommended quantization for Qwen3-Coder-Next is Q4_K_M, which balances quality and memory efficiency.

Can it run?

No, Qwen3-Coder-Next requires more memory than RTX 4090 Laptop 16GB provides.

FWon't run

Too heavy

Using Q4_K_M in Ollama

Capabilities:

Fit status

Too heavy

Decode

25.8 tok/s

TTFT

7518 ms

Safe context

Memory

52.4 GB / 16.0 GB

Weights48.8 GB

KV Cache0.8 GB

Runtime1.2 GB

Headroom1.6 GB

Workload	Grade	Fit	Decode	TTFT	Context
Agentic Coding	F	Too heavy	28.6 tok/s	9841 ms	10K
Chat	F	Too heavy	28.6 tok/s	3690 ms	4K
Coding	F	Too heavy	25.8 tok/s	7518 ms	5K
RAG	F	Too heavy	28.6 tok/s	12302 ms	10K
Reasoning	F	Too heavy	28.6 tok/s	7996 ms	5K

How Qwen3-Coder-Next (80B params) fits at each quantization level on RTX 4090 Laptop 16GB (16.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	31.2 GB	Low	F0
Q3_K_S	3	39.2 GB	Low	F0
NVFP4	4

Upgrade options

MacBook Pro M3 Max 128GBBudget pick

C14.9 tok/s decode

~$2,499 MSRP

MacBook Pro M4 Max 96GBBest value

C21.4 tok/s decode

~$2,499 MSRP

NVIDIA A100 80GBBiggest leap