R1 0528 RAM Calculator
May 28th update to the [original DeepSeek R1](/deepseek/deepseek-r1) Performance on par with [OpenAI o1](/openai/o1), but open-sourced and with fully open reasoning tokens. It's 671B parameters in size, with 37B active...
512GB 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.
512GB System RAM
Stably runs model with Q4/Q8/FP16 quantization & target context windows.
Memory Allocation Sizer
Dynamic Dual-Channel Picks (512GB)
A-Tech 512GB Kit (8x64GB) DDR4 2666MHz PC4-21300 ECC LRDIMM 4Rx4 (4DRx4) Quad Rank 1.2V Load Reduced DIMM 288-Pin Server RAM Memory Upgrade Modules (A-Tech Enterprise Series)
OWC 512GB (4x128GB) DDR4 3200MHz PC4-25600 CL22 4RX4 ECC Registered RDIMM 1.2V 288-pin Memory RAM Upgrade for Server
NEMIX RAM 512GB (8X64GB) DDR5 5600MHZ PC5-44800 2Rx4 1.1V CL46 288-PIN ECC RDIMM Registered Server Memory KIT Compatible with ASUS Pro WS WRX90E SAGE SE Workstation Motherboard
A-Tech 512GB Kit (8x64GB) DDR4 2666MHz PC4-21300 ECC RDIMM 4Rx4 (3DS 2S2Rx4) Quad Rank 1.2V ECC Registered DIMM 288-Pin Server & Workstation RAM Memory Upgrade Modules (A-Tech Enterprise Series)
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.
R1 0528 Memory FAQs
How much RAM does R1 0528 require?
To run R1 0528 locally, memory size depends on your selected quantization. At 4-bit compression (Q4_K_M), the weights take up ~377.4GB of RAM. When combined with context cache and OS overhead, a standard **512GB system memory kit** is recommended. Unquantized FP16 execution requires a **1024GB memory setup**.
What are the hardware requirements to run R1 0528 at FP16 precision?
Running R1 0528 at unquantized 16-bit precision requires loading ~1342GB 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 R1 0528 dense or Mixture-of-Experts (MoE)?
R1 0528 is built on a **Dense** architecture. It features a dense parameter layout containing 671 Billion parameters. All weights are active and computed on every single pass during token generation.
How does context window size affect RAM usage for R1 0528?
Context size directly scales the size of the Key-Value (KV) cache. At a standard 8,192 token context, the KV cache for R1 0528 uses ~1.65GB. If you scale this to 163.84K tokens, the KV cache can scale past hundreds of GBs, requiring multi-GPU or workstation-class RAM configurations.