How much VRAM does Qwen3-Coder-Next need?

Qwen3-Coder-Next (80B parameters) requires approximately 57.2 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?

Yes, NVIDIA A16 64GB can run Qwen3-Coder-Next with a C grade (Tight fit). Expected decode speed: 26.8 tok/s.

CUsable

Tight fit

Using Q4_K_M in Ollama

Capabilities:

Fit status

Tight fit

Decode

26.8 tok/s

TTFT

7217 ms

Safe context

18K

Memory

57.2 GB / 64.0 GB

Weights48.8 GB

KV Cache0.8 GB

Runtime1.2 GB

Headroom6.4 GB

Workload	Grade	Fit	Decode	TTFT	Context
Agentic Coding	C	Tight fit	29.1 tok/s	9690 ms	36K
Chat	C	Tight fit	29.1 tok/s	3634 ms	9K
Coding	C	Tight fit	26.8 tok/s	7217 ms	18K
RAG	C	Tight fit	29.1 tok/s	12112 ms	36K
Reasoning	C	Tight fit	29.1 tok/s	7873 ms	18K

How Qwen3-Coder-Next (80B params) fits at each quantization level on NVIDIA A16 64GB (64.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	31.2 GB	Low	D40
Q3_K_S	3	39.2 GB	Low	C43
NVFP4	4

Ollama

ollama run qwen-3-coder-next

HuggingFace

huggingface-cli download qwen-3-coder-next

Upgrade options

MacBook Pro M3 Max 128GBBudget pick

C14.9 tok/s decode

~$2,499 MSRP

Mac Studio M2 Ultra 128GBBest value

C28.8 tok/s decode

~$3,999 MSRP

NVIDIA A100 80GBBiggest leap