AllocateQwen3 Next 80B A3b Thinking GPU requirements
What it takes to run Qwen3 Next 80B A3b Thinking locally: memory by quantization, the smallest GPU that fits, and the managed alternative.
Qwen3 Next 80B A3b Thinking serves up to 262,144 tokens of context; the KV cache grows linearly toward that ceiling, so the slider below shows exactly what longer context costs in memory.
Smallest single device that fits: A100 (80 GB)
How it works
Common questions
How much VRAM does Qwen3 Next 80B A3b Thinking need?
At 8K context: roughly 179 GB at FP16, 89 GB at 8-bit, and 49 GB at 4-bit, including KV cache and runtime overhead. Longer context adds memory linearly.
Can a single GPU run Qwen3 Next 80B A3b Thinking?
At 4-bit, yes: a A100 (80 GB) or larger handles it at 8K context. At FP16 you need a B200 (192 GB) or larger.
Why does Qwen3 Next 80B A3b Thinking need so much memory as a MoE model?
Only some experts activate per token, which sets speed, but all 80 billion parameters must sit in memory. MoE trades memory for throughput.
What license is Qwen3 Next 80B A3b Thinking under?
Apache 2.0. Permissive: run it, fine-tune it, and own the result.
Is there an alternative to buying GPUs for Qwen3 Next 80B A3b Thinking?
Yes: managed inference. Allocate serves Qwen3 Next 80B A3b Thinking token-metered from $0.15 per million input tokens at provider list price, plus the 7% transaction fee. Idle time costs nothing.
More free tools
Allocate serves open-weight models like Qwen3 Next 80B A3b Thinking token-metered inside your own boundary. No hardware to buy, and if you fine-tune on your data, the weights are yours.