RTX 5060 Ti 16GB for DeepSeek Coder V2 Lite

DeepSeek Coder V2 Lite is a mixture-of-experts coding model: 16B total parameters, 2.4B active per forward pass. The MoE design delivers strong coding performance with decode speed closer to a 3B dense model. On the RTX 5060 Ti 16GB at our hosting it fits at AWQ with reasonable concurrency.

Contents

• MoE VRAM reality
• Fit
• Deployment
• Performance

MoE VRAM

MoE models need the full parameter set in VRAM even though only some experts activate per token. DeepSeek Coder V2 Lite has 16B total – VRAM for weights scales to the full size, not the active subset.

Fit

Deployment

python -m vllm.entrypoints.openai.api_server \
  --model deepseek-ai/DeepSeek-Coder-V2-Lite-Instruct \
  --quantization awq \
  --max-model-len 16384 \
  --trust-remote-code \
  --gpu-memory-utilization 0.92

Performance

Decode speed benefits from MoE architecture – only 2.4B parameters are “hot” per token so effective speed resembles a 3B dense model:

• AWQ batch 1 decode: ~130-150 t/s
• AWQ batch 8 aggregate: ~650 t/s
• TTFT 1k prompt: ~200 ms

For coding workloads on the 5060 Ti, DeepSeek Coder V2 Lite is a strong choice – better quality-per-token than dense 7B coders while running at similar speed.

See full DeepSeek Coder V2 VRAM guide.

See also: Qwen Coder 7B, R1 Distill 7B.