AllocateDeepSeek V4 1.6T GPU requirements
What it takes to run DeepSeek V4 1.6T (MoE) locally: memory by quantization, the smallest GPU that fits, and the managed alternative.
DeepSeek V4 1.6T serves up to 512,000 tokens of context; the KV cache grows linearly toward that ceiling, so the slider below shows exactly what longer context costs in memory.
No single GPU fits this configuration. Shard across devices, quantize harder, or run it managed.
Or skip the hardware: run DeepSeek V4 1.6T on Allocate, token-metered.
How it works
Common questions
How much VRAM does DeepSeek V4 1.6T need?
At 8K context: roughly 3525 GB at FP16, 1763 GB at 8-bit, and 969 GB at 4-bit, including KV cache and runtime overhead. Longer context adds memory linearly.
Can a single GPU run DeepSeek V4 1.6T?
Not at practical quantizations: even 4-bit needs about 969 GB, beyond any single device here. Sharding across a fleet or managed serving are the options.
Why does DeepSeek V4 1.6T need so much memory as a MoE model?
Only some experts activate per token, which sets speed, but all 1.6 trillion parameters must sit in memory. MoE trades memory for throughput.
What license is DeepSeek V4 1.6T under?
Proprietary API. Check the license terms for your use case before deployment.
Is there an alternative to buying GPUs for DeepSeek V4 1.6T?
Yes: managed inference. Allocate serves comparable models token-metered inside your own boundary, and if you fine-tune an open base on your data, the weights belong to you.
More free tools
Allocate serves open-weight models like DeepSeek V4 1.6T token-metered inside your own boundary. No hardware to buy, and if you fine-tune on your data, the weights are yours.