DeepSeek-Coder-V2-Lite Instruct
16BDeepSeek
MoE architecture coding model. Active params ~2.4B, total ~16B. Exceptional code quality.
164K
Max Context
3
Quant Variants
GGUF Q8_0
Best Quality
99.8%
Accuracy Retained
Quantization Variants
Per-quant VRAM, quality loss, and inference speed on RTX 4090
Measured = site benchmarks · Estimated = formula · Community = public reports
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Running DeepSeek-Coder-V2-Lite Instruct locally
At Q4_K_M and 4K of context, DeepSeek-Coder-V2-Lite Instruct needs about 10.9 GB — 9.0 GB of weights, 0.84 GB of KV cache and a 1.0 GB activation buffer. The smallest card in this index that clears that comfortably is the RTX 5080 at 16 GB, and 46 of the 63 cards here do. These are calculated figures, not measurements: the estimate stops counting a card as comfortable at 88% of its VRAM, which is roughly the room a desktop session needs.
What longer context costs
Going from 4K to 32K adds about 5.91 GB, taking the total to 17.4 GB. Weights do not move with context — only the KV cache does, and it grows linearly, so this is the number to watch when planning for long documents. The model's native window is 160K; holding all of it at Q4_K_M would need about 47 GB.
Which build to download
This index tracks 2 formats for it — GGUF, AWQ — across 3 levels. Q8_0 carries the lowest published perplexity loss at 0.2%. The fastest level measured here is AWQ INT4 at 192 tok/s on an RTX 4090, batch 1. GGUF runs on llama.cpp and Ollama across NVIDIA, AMD and Apple silicon; AWQ and GPTQ target vLLM on CUDA and ROCm; EXL2 is ExLlamaV2 and CUDA only.
Common questions
- How much VRAM does DeepSeek-Coder-V2-Lite Instruct need?
- About 10.9 GB at Q4_K_M with 4K of context and batch 1, rising to roughly 17.4 GB at 32K. That figure is weights plus KV cache plus a 10% activation buffer, calculated from the model's architecture rather than measured on a card.
- Will DeepSeek-Coder-V2-Lite Instruct run on a 16GB GPU?
- Yes — at Q4_K_M and 4K context it needs about 10.9 GB, which leaves 5.1 GB spare on a RTX 5080. That is the smallest card in this index that clears it comfortably; 46 of 63 do.
- Which quantization of DeepSeek-Coder-V2-Lite Instruct should I use?
- Q8_0 has the lowest published quality loss (0.2%), and Q4_K_M is the level most people run. All 3 levels in the index are Q4_K_M, Q8_0, AWQ INT4.
Where this model fits
Sized at Q4_K_M with a 4K context window, smallest card first. Comfortable means the estimate uses at most 88% of the memory.
- Runs comfortably on
- RTX 508016GB · +5.1RTX 5070 Ti16GB · +5.1RTX 5060 Ti 16G16GB · +5.1RTX 4080 Super16GB · +5.1RTX 4070 Ti Super16GB · +5.1
- Tight but possible
- RTX 507012GBRTX 4070 Ti12GBRTX 4070 Super12GB
- Just misses
- Mac M3 8G+4.9GB overRTX 3080 10G+0.9GB over
- Compared here
- GGUF vs AWQ
- Try it yourself
- Size it in the calculatorGenerate the run command
How to actually run this
Deployment guides for this model and this class of hardware.
This page's figures change when the model or the runtime does.Last updated 2026-09-21 RSS → /feed.xml