DeepSeek MoE & MLA Inference Efficiency & Cost Simulator

1. Architectural & Workload Parameters REALTIME SIMULATOR

Total Parameters 236 B

Active Params / Token 21 B

Total Experts 64

Active Experts / Token 6

Context Length 8,192 tokens

Batch Size 32

Attention KV Cache Type

Target GPU Accelerator

Sparse MoE Router & Active Gate Allocation Sparsity: 91.1%

KV Cache Projection: Standard MHA vs MLA Compression Compress: 16.0x

2. Real-Time Telemetry & Hardware Requirements FP8 / BF16 Execution

KV Cache Footprint
0.048 GB/seq
Total: 1.54 GB (Batch 32)

Active FLOPs / Token

42.0 GFLOPs

88.9% compute reduction

GPUs Required

4 x H100

Model VRAM: 236 GB

API Hosting Cost
$0.140 / 1M tokens
~2,850 tok/s cluster

Architectural Efficiency Matrix

Metric Dimension	Dense Baseline	Current Config	MoE/MLA Advantage
Total Parameters (Weight Memory)	236.0 B	236.0 B	1.0x Memory
Active Params per Forward Pass	236.0 B	21.0 B	11.2x Savings
KV Cache VRAM (Per 128k Batch 32)	512.0 GB	32.0 GB	16.0x Compressed
Theoretical Decoding Bandwidth Bound	High VRAM Pressure	Optimal MLA Dec	Memory-Bound Resolved
Total Minimal Cluster VRAM Required	984 GB	268 GB	3.7x Lower Node Size

Why DeepSeek API Pricing is ~10x Lower

1. Sparse MoE Active Computation: Although DeepSeek-V3 hosts 671 Billion total parameters for world-knowledge capacity, each token routes through only 37 Billion active parameters (~5.5% active), reducing matrix multiplication FLOPs per token by 18x.

2. MLA Latent KV Compression: Multi-Head Latent Attention projects high-dimensional Key-Value states into a low-rank latent vector (d_c = 512 vs 16,384 in standard MHA), compressing context VRAM by up to 16x and preventing memory-bandwidth bottleneck during generation.

3. Hardware Sizing Impact: Reduced VRAM footprint permits hosting 671B model instances on 8x H100 nodes rather than requiring massive 32x-64x H100 tensor parallel clusters, dramatically lowering per-second server amortized cost.