DeepSeek-V4-Pro (1.6T MoE) RAM Calculator
Flagship open reasoning model featuring a 1.6 Trillion parameter Mixture-of-Experts (MoE) architecture with 49 Billion active parameters per token. Utilizes Compressed Sparse Attention (CSA) for extreme long-context memory efficiency.
1024GB RAM
Calculated for 4-bit (Q4_K_M) @ 8K Context1. Select Quantization Level
Quantization compresses model weights to reduce RAM usage, with minor impacts on output quality.
2. Set Target Context Length
Longer contexts require more active memory for the Key-Value (KV) cache.
Inference Bandwidth & Speed Matrix
Estimates generation speeds (tokens per second) based on physical memory channel bandwidth constraints.
DDR4 CPU Mode
45 GB/s
0.5 t/s
DDR5 CPU Mode
96 GB/s
1.0 t/s
Mac Unified Memory
300 GB/s
3.0 t/s
GPU VRAM (RTX 4090)
1008 GB/s
5.0 t/s
*Token throughput calculated strictly from weight volume transfers over memory channels. Actual generation speeds can be further throttled by processing threads or VRAM offloading parameters.
1024GB System RAM
Stably runs model with Q4/Q8/FP16 quantization & target context windows.
Memory Allocation Sizer
Dynamic Dual-Channel Picks (1024GB)
1TB 16X64GB DDR4 3200MHZ PC4-25600 2Rx4 1.2V CL22 288-PIN ECC Registered RDIMM NEMIX RAM Server Memory KIT
NEMIX RAM 1TB (8X128GB) DDR5 6400MHZ PC5-51200 CL52 2Rx4 1.1V 288-PIN ECC RDIMM Registered Server Memory KIT
NEMIX RAM 1TB (16X64GB) DDR5 4800MHZ PC5-38400 2Rx4 1.1V CL40 288-PIN ECC RDIMM Registered Server Memory KIT Compatible with ASUS 1U Dual-Socket Server Model RS700-E11-RS12U
NEMIX RAM 1TB (8X128GB) DDR5 6400MHZ PC5-51200 CL52 4Rx4 1.1V 288-PIN ECC RDIMM Registered Server Memory KIT Compatible with ASRock Rack TURIND8HM3 Motherboard
Technical Specifications
GPU & VRAM Sizing Profile
Enterprise GPU Node / Mac Studio 192GBHardware Profile: Server-scale deployment. Running this model locally requires extreme unified memory Apple systems or professional multi-GPU servers.
DeepSeek-V4-Pro (1.6T MoE) Memory FAQs
How much RAM does DeepSeek-V4-Pro (1.6T MoE) require?
To run DeepSeek-V4-Pro (1.6T MoE) locally, memory size depends on your selected quantization. At 4-bit compression (Q4_K_M), the weights take up ~900GB of RAM. When combined with context cache and OS overhead, a standard **1024GB system memory kit** is recommended. Unquantized FP16 execution requires a **1024GB memory setup**.
What are the hardware requirements to run DeepSeek-V4-Pro (1.6T MoE) at FP16 precision?
Running DeepSeek-V4-Pro (1.6T MoE) at unquantized 16-bit precision requires loading ~3200GB of model weights directly into VRAM or system memory. A minimum system memory target of **1024GB RAM** is required to run the weights stably and avoid out-of-memory crashes.
Is DeepSeek-V4-Pro (1.6T MoE) dense or Mixture-of-Experts (MoE)?
DeepSeek-V4-Pro (1.6T MoE) is built on a **MoE** architecture. It has a total of 1600 Billion parameters, but only activates 49 Billion parameters per token. While active parameter sparse execution makes token computing very fast, the entire 1600B parameters must reside in VRAM/RAM for fast expert switching during execution.
How does context window size affect RAM usage for DeepSeek-V4-Pro (1.6T MoE)?
Context size directly scales the size of the Key-Value (KV) cache. At a standard 8,192 token context, the KV cache for DeepSeek-V4-Pro (1.6T MoE) uses ~0.12GB. If you scale this to 1 Million tokens, the KV cache can scale past hundreds of GBs, requiring multi-GPU or workstation-class RAM configurations.