AllocateQwen3 Coder 480B A35B Instruct Fp8 GPU requirements
What it takes to run Qwen3 Coder 480B A35B Instruct Fp8 locally: memory by quantization, the smallest GPU that fits, and the managed alternative.
Qwen3 Coder 480B A35B Instruct Fp8 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: Mac M3 Ultra (512 GB unified)
Or skip the hardware: run Qwen3 Coder 480B A35B Instruct Fp8 on Allocate, token-metered.
How it works
Common questions
How much VRAM does Qwen3 Coder 480B A35B Instruct Fp8 need?
At 8K context: roughly 1060 GB at FP16, 530 GB at 8-bit, and 291 GB at 4-bit, including KV cache and runtime overhead. Longer context adds memory linearly.
Can a single GPU run Qwen3 Coder 480B A35B Instruct Fp8?
At 4-bit, yes: a Mac M3 Ultra (512 GB unified) or larger handles it at 8K context. At FP16 you need multiple devices.
Why does Qwen3 Coder 480B A35B Instruct Fp8 need so much memory as a MoE model?
Only some experts activate per token, which sets speed, but all 480 billion parameters must sit in memory. MoE trades memory for throughput.
What license is Qwen3 Coder 480B A35B Instruct Fp8 under?
Apache 2.0. Permissive: run it, fine-tune it, and own the result.
Is there an alternative to buying GPUs for Qwen3 Coder 480B A35B Instruct Fp8?
Yes: managed inference. Allocate serves Qwen3 Coder 480B A35B Instruct Fp8 token-metered from $2 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 Coder 480B A35B Instruct Fp8 token-metered inside your own boundary. No hardware to buy, and if you fine-tune on your data, the weights are yours.