DeepSeek Coder V2 Lite (16B / 2.4B active) VRAM Requirements Guide
Mixture-of-Experts (MoE) coding wizard. Blazing fast inference with high quality.
>98.2% retention. 9.3 GB weights, leaving +3.1 GB headroom on NVIDIA GeForce RTX 5060 Ti 16GB.
Near-lossless 8-bit precision requiring 20.8 GB VRAM for benchmark evaluations.
Uncompressed FP16 weights taking 35.6 GB. Demands multi-GPU or datacenter accelerators.
Architectural Memory Breakdown & Attention Scaling
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.
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.
NVIDIA GeForce RTX 5060 Ti 16GB provides 16.0 GB physical VRAM (+3.1 GB operational margin at 8k context).
Run DeepSeek Coder V2 Lite (16B / 2.4B active) Locally
ollama run deepseek-coder-v2-lite:q4_K_Mvllm serve DeepSeek/deepseek-coder-v2-lite --max-model-len 8192 --gpu-memory-utilization 0.95Frequently Asked Questions
Can DeepSeek Coder V2 Lite run on a 12GB RTX 3060?
Yes! Total memory footprint at Q4_K_M with 8k context is ~10.2 GB, running smoothly on 12GB graphics cards at over 60 tok/s.
Why is MLA attention advantageous for coding models?
Multi-head Latent Attention projects key-value tensors into a compressed latent space, reducing KV-cache memory growth during long repository analysis.
Test Custom Hardware & Context Windows
Simulate DeepSeek Coder V2 Lite (16B / 2.4B active) across all 31 GPUs, adjust context up to 131,072 tokens, or calculate RunPod break-even costs.
Open DeepSeek Coder V2 Lite (16B / 2.4B active) in GPU Garden Matchmaker →