Kimi K2 0905 RAM Calculator
Kimi K2 0905 is the September update of [Kimi K2 0711](moonshotai/kimi-k2). It is a large-scale Mixture-of-Experts (MoE) language model developed by Moonshot AI, featuring 1 trillion total parameters with 32...
768GB 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.
768GB System RAM
Stably runs model with Q4/Q8/FP16 quantization & target context windows.
Memory Allocation Sizer
Dynamic Dual-Channel Picks (768GB)
Micron Original 768GB (24x32GB) Server Memory Upgrade for Oracle Server X5-2 DDR4 2400MHZ PC4-19200 ECC Registered Chip 2Rx4 CL17 1.2V RAM
768GB (24x32GB) Server Memory Upgrade for Oracle Server X5-2 DDR4 2133 PC4-17000 ECC Registered 2Rx4 CL15 1.2v RAM
Micron Original 768GB (24x32GB) Server Memory Upgrade for Lenovo System x3850 X6 DDR4 2400MHZ PC4-19200 ECC Registered Chip 2Rx4 CL17 1.2V RAM
Micron Original 768GB (24x32GB) Server Memory Upgrade for Dell PowerEdge FC830 DDR4 2400MHZ PC4-19200 ECC Registered Chip 2Rx4 CL17 1.2v RAM
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.
Kimi K2 0905 Memory FAQs
How much RAM does Kimi K2 0905 require?
To run Kimi K2 0905 locally, memory size depends on your selected quantization. At 4-bit compression (Q4_K_M), the weights take up ~562.5GB of RAM. When combined with context cache and OS overhead, a standard **768GB system memory kit** is recommended. Unquantized FP16 execution requires a **1024GB memory setup**.
What are the hardware requirements to run Kimi K2 0905 at FP16 precision?
Running Kimi K2 0905 at unquantized 16-bit precision requires loading ~2000GB 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 Kimi K2 0905 dense or Mixture-of-Experts (MoE)?
Kimi K2 0905 is built on a **MoE** architecture. It has a total of 1000 Billion parameters, but only activates 100 Billion parameters per token. While active parameter sparse execution makes token computing very fast, the entire 1000B parameters must reside in VRAM/RAM for fast expert switching during execution.
How does context window size affect RAM usage for Kimi K2 0905?
Context size directly scales the size of the Key-Value (KV) cache. At a standard 8,192 token context, the KV cache for Kimi K2 0905 uses ~0.25GB. If you scale this to 262.144K tokens, the KV cache can scale past hundreds of GBs, requiring multi-GPU or workstation-class RAM configurations.