# DeepSeek Coder V2 Lite (16B / 2.4B active) VRAM Requirements & Hardware Sizing Guide

> **Quick Summary (Answer Capsule)**: Running DeepSeek Coder V2 Lite (16B / 2.4B active) requires 12.86 GB at Q4_K_M (4-bit), 20.77 GB at Q8_0 (8-bit), and 35.62 GB at FP16. Recommended hardware sweet spot: NVIDIA GeForce RTX 5060 Ti 16GB. Scaling context past 32k tokens requires enabling FP8 KV-cache (-ctk q8_0) to prevent CUDA allocation failures.

## Memory Specifications by Precision
| Quantization Format | Precision Level | Required VRAM (8k context) | Quality Retention | Minimum Recommended GPU |
|---|---|---|---|---|
| 4-Bit (Q4_K_M) | Recommended Default | 12.86 GB | >98.2% Perplexity | NVIDIA GeForce RTX 5060 Ti 16GB |
| 8-Bit (Q8_0) | Near Lossless | 20.77 GB | ~99.9% Perplexity | Workstation / High-Tier GPU |
| 16-Bit (FP16/BF16) | Uncompressed Full | 35.62 GB | 100% Fidelity | Multi-GPU / Datacenter |

## Architectural Memory Analysis
DeepSeek Coder V2 Lite is a specialized coding Mixture-of-Experts (MoE) model comprising 15.7B total parameters with only 2.4B active parameters per token. Structured across 27 layers with Multi-head Latent Attention (MLA), its 4-bit Q4_K_M weight footprint takes ~9.5 GB. It executes at high speeds on 12GB–16GB GPUs, delivering code generation quality that rivals dense 14B models.

## Tier 1 Free Software Optimization ($0)
In llama.cpp, pass `--flash-attn` to accelerate MLA latent projection decoding. Ensure your GPU driver keeps all 15.7B expert weights in VRAM to avoid PCIe bus stalls during dynamic expert routing.

## 1-Click Launch Terminal Snippets
- Ollama: `ollama run deepseek-coder-v2-lite:q4_K_M`
- vLLM: `vllm serve DeepSeek/deepseek-coder-v2-lite --max-model-len 8192 --gpu-memory-utilization 0.95`

## Verified Hardware & Cloud Pathways
- Amazon Hardware: [RTX 5060 Ti 16GB graphics card](https://www.amazon.com/s?k=RTX+5060+Ti+16GB+graphics+card&tag=sparecables-20) (rel="noopener sponsored")
- RunPod Cloud Rental: [Deploy Instance](https://runpod.io?ref=ynypzu94) (rel="noopener sponsored")
- Vast.ai Cloud Rental: [Deploy Instance](https://cloud.vast.ai/?ref_id=723130) (rel="noopener sponsored")

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*Interactive Sizing Calculator available at: https://www.gpugarden.com/vram/deepseek-coder-v2-lite/*