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OP·Fredoline Eruo
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RUNLOCALAI · v38
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deepseek
15.7B parameters
Commercial OK
·Reviewed May 2026

DeepSeek V2 Lite Chat

DeepSeek-V2-Lite-Chat is a 15.7B-total, 2.4B-active mixture-of-experts chat model using DeepSeek's Multi-head Latent Attention (MLA) architecture for KV-cache efficiency. Native context is 32K with YaRN scaling published up to 160K, and it ships under the DeepSeek model license which permits commercial use.

License: deepseek-license·Context: 32,768 tokens
BLK · VERDICT

Our verdict

OP · Fredoline Eruo|VERIFIED MAY 29, 2026
unrated

Misclassified as an SLM by parameter count, but the 2.4B active-parameter compute profile makes it a fair entry in this tier for inference latency on a workstation. Punches like a 7B dense model while activating only a third of the FLOPs.

Overview

DeepSeek-V2-Lite-Chat is a 15.7B-total, 2.4B-active mixture-of-experts chat model using DeepSeek's Multi-head Latent Attention (MLA) architecture for KV-cache efficiency. Native context is 32K with YaRN scaling published up to 160K, and it ships under the DeepSeek model license which permits commercial use.

Strengths

  • MoE design delivers ~7B-class quality at 2.4B active compute cost
  • MLA attention dramatically reduces KV-cache memory at long context
  • DeepSeek license permits commercial use (with conditions)
  • Native 32K context with documented YaRN extension to 160K

Weaknesses

  • Total VRAM is ~30GB at fp16 — does NOT fit on consumer 12GB GPUs at full precision
  • MoE routing means inference engines without expert support fall back to slow paths
  • DeepSeek license has acceptable-use restrictions; not a true OSI license
  • Custom-code repo requires trust_remote_code=True, which some pipelines block

Quantization variants

Each quantization trades model quality for file size and VRAM. Q4_K_M is the most popular starting point.

QuantizationFile sizeVRAM required
Q4_K_M8.6 GB11 GB

Get the model

HuggingFace

Original weights

huggingface.co/deepseek-ai/DeepSeek-V2-Lite-Chat

Source repository — direct quantization required.

Hardware that runs this

Cards with enough VRAM for at least one quantization of DeepSeek V2 Lite Chat.

NVIDIA GB200 NVL72
13824GB · nvidia
AMD Instinct MI355X
288GB · amd
AMD Instinct MI325X
256GB · amd
AMD Instinct MI300X
192GB · amd
NVIDIA B200
192GB · nvidia
NVIDIA H100 NVL
188GB · nvidia
NVIDIA H200
141GB · nvidia
NVIDIA H200 NVL (PCIe)
141GB · nvidia

Frequently asked

What's the minimum VRAM to run DeepSeek V2 Lite Chat?

11GB of VRAM is enough to run DeepSeek V2 Lite Chat at the Q4_K_M quantization (file size 8.6 GB). Higher-quality quantizations need more.

Can I use DeepSeek V2 Lite Chat commercially?

Yes — DeepSeek V2 Lite Chat ships under the deepseek-license, which permits commercial use. Always read the license text before deployment.

What's the context length of DeepSeek V2 Lite Chat?

DeepSeek V2 Lite Chat supports a context window of 32,768 tokens (about 33K).

Source: huggingface.co/deepseek-ai/DeepSeek-V2-Lite-Chat

Reviewed by RunLocalAI Editorial. See our editorial policy for how we research and verify model claims.

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Before you buy

Verify DeepSeek V2 Lite Chat runs on your specific hardware before committing money.

Will it run on my hardware? →Custom hardware comparison →GPU recommender (4 questions) →
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