How much VRAM does Qwen3-Coder-Next need?

Qwen3-Coder-Next (80B parameters) requires approximately 55.6 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, RTX 6000 Ada 48GB can run Qwen3-Coder-Next with a C grade (Very compromised). Expected decode speed: 37.9 tok/s.

CUsable

Very compromised

Using Q4_K_M in Ollama

Capabilities:

Fit status

Very compromised

Decode

37.9 tok/s

TTFT

5104 ms

Safe context

14K

Memory

55.6 GB / 48.0 GB

Offload

10%

Weights48.8 GB

KV Cache0.8 GB

Runtime1.2 GB

Headroom4.8 GB

Workload	Grade	Fit	Decode	TTFT	Context
Agentic Coding	C	Very compromised (needs ~6.8 GB host RAM)	43.1 tok/s	6531 ms	28K
Chat	C	Very compromised (needs ~6.7 GB host RAM)	43.2 tok/s	2444 ms	7K
Coding	C	Very compromised	37.9 tok/s	5104 ms	14K
RAG	C	Very compromised (needs ~6.8 GB host RAM)	43.1 tok/s	8164 ms	28K
Reasoning	C	Very compromised (needs ~6.7 GB host RAM)	43.2 tok/s

How Qwen3-Coder-Next (80B params) fits at each quantization level on RTX 6000 Ada 48GB (48.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_KBest for your GPU	2	31.2 GB	Low	C43
Q3_K_S	3	39.2 GB	Low	C45

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

MacBook Pro M4 Max 96GBBest value

C21.4 tok/s decode

~$2,499 MSRP

NVIDIA A100 80GBBiggest leap