DeepSeek-Coder-V2-Lite-Instruct
deepseek-aiothertext

DeepSeek-Coder-V2-Lite-Instruct

Updated Jun 25, 2026
Parameters
15.7B
Context
163,840
License
other
Updated
Jun 25, 2026

Will it run on your hardware?

Pick your GPU memory - see which quantizations fit, and the cheapest card for the rest

Runs on your 24 GB - best at Q8_0
3 of 4 quantizations fit DeepSeek-Coder-V2-Lite-Instruct with real runtime overhead.
Q4_K_M
9 GB
Q5_K_M
11 GB
Q8_0
17 GB
FP16
31 GB
fits tight too big

Need an exact figure for your context length? Use the VRAM calculator.

Run it locally

Copy-paste - running in under a minute

vLLMOpenAI-compatible API
vllm serve deepseek-ai/DeepSeek-Coder-V2-Lite-Instruct

New to this? Start with Ollama · serve to many users with vLLM.

Deep dive

Notes, sources, and the full write-up

DeepSeek-Coder-V2-Lite-Instruct is a 15.7B-parameter other model from deepseek-ai. At Q4_K_M it needs roughly 9 GB of VRAM, placing it in the 8–12 GB GPU hardware tier.

Popularity

DeepSeek-Coder-V2-Lite-Instruct has 1,197,020 downloads in the last month on HuggingFace and 613 likes.

Frequently asked

Quick answers to common questions

How much VRAM does DeepSeek-Coder-V2-Lite-Instruct need?

DeepSeek-Coder-V2-Lite-Instruct with 15.7B parameters needs approximately 9 GB at Q4_K_M quantization. Use our VRAM calculator for an exact estimate.

Is DeepSeek-Coder-V2-Lite-Instruct better than other deepseek-ai models?

DeepSeek-Coder-V2-Lite-Instruct has 15.7B parameters with 163,840 context - a strong choice for general use.

What license is DeepSeek-Coder-V2-Lite-Instruct under?

DeepSeek-Coder-V2-Lite-Instruct is released under the other license, making it suitable for most commercial and personal projects.

What hardware runs DeepSeek-Coder-V2-Lite-Instruct well?

With 15.7B parameters, DeepSeek-Coder-V2-Lite-Instruct requires adequate VRAM. High-end GPUs like the RTX 4090 (24GB), RTX 5090 (32GB), or Mac Studio with unified memory are good options. Check our hardware directory for specific recommendations.

What is the best quantization for DeepSeek-Coder-V2-Lite-Instruct?

Q4_K_M is the recommended sweet spot - ~98% of FP16 quality at ~27% of the size. Q5_K_M (~11 GB) is an option if you have spare VRAM. Use our VRAM calculator to compare.

How long can DeepSeek-Coder-V2-Lite-Instruct's context window handle?

DeepSeek-Coder-V2-Lite-Instruct supports a 163,840-token context window - enough for very long documents, codebases, or multi-turn conversations. Real-world usable context may vary by implementation.

What models compete with DeepSeek-Coder-V2-Lite-Instruct?

DeepSeek-Coder-V2-Lite-Instruct competes with other models in its class. Browse our model directory for comparisons, benchmarks, and community reviews to find the best fit.

Nearby options

Similar models and compatible hardware by spec

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