AllocateDeepseek Coder 33B Instruct GPU requirements
What it takes to run Deepseek Coder 33B Instruct locally: memory by quantization, the smallest GPU that fits, and the managed alternative.
Deepseek Coder 33B Instruct serves up to 16,384 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: RTX 4090 (24 GB)
How it works
Common questions
How much VRAM does Deepseek Coder 33B Instruct need?
At 8K context: roughly 75 GB at FP16, 37 GB at 8-bit, and 21 GB at 4-bit, including KV cache and runtime overhead. Longer context adds memory linearly.
Can a single GPU run Deepseek Coder 33B Instruct?
At 4-bit, yes: a RTX 4090 (24 GB) or larger handles it at 8K context. At FP16 you need a A100 (80 GB) or larger.
Does quantization hurt Deepseek Coder 33B Instruct's quality?
Modern 4-bit quantization costs a small amount of quality for a 4x memory saving; 8-bit is near-lossless. Validate on your own tasks before production.
What license is Deepseek Coder 33B Instruct under?
Custom license. Check the license terms for your use case before deployment.
Is there an alternative to buying GPUs for Deepseek Coder 33B Instruct?
Yes: managed inference. Allocate serves Deepseek Coder 33B Instruct token-metered from $0.8 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 Deepseek Coder 33B Instruct token-metered inside your own boundary. No hardware to buy, and if you fine-tune on your data, the weights are yours.