What are the hardware requirements for DeepSeek Coder V2 Base?

DeepSeek Coder V2 Base requires approximately 387 GB of VRAM and supports a context window of 160K tokens. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed DeepSeek Coder V2 Base and how large is it?

DeepSeek Coder V2 Base is developed by deepseek-ai, a model with 235.7b parameters. The model is published as open weights on Hugging Face and indexed on LLM Explorer with full benchmark history.

How does DeepSeek Coder V2 Base perform on standard benchmarks?

DeepSeek Coder V2 Base has the following published scores: LMSYS ELO 1265. Compare against reference models on this page or on the LLM Explorer leaderboards.

DeepSeek Coder V2 Base by deepseek-ai — VRAM 387GB, 160K context

Name: DeepSeek Coder V2 Base
Author: deepseek-ai

DeepSeek Coder V2 Base is an open-source language model by deepseek-ai. Features: 235.7b LLM, VRAM: 387GB, Context: 160K, License: other, Code Generating, LLM Explorer Score: 0.27, ELO: 1265.

Arxiv:2401.06066 Codegen Conversational Custom code Deepseek v2 Endpoints compatible Region:us Safetensors Sharded Tensorflow

Model Card on HF 🤗: https://huggingface.co/deepseek-ai/DeepSeek-Coder-V2-Base

DeepSeek Coder V2 Base Benchmarks

LMSys ELO 1265 vs 1272 (so35) -0.6%

^nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").

What is the LLM Explorer Rank (Score)

DeepSeek Coder V2 Base Parameters and Internals

Model Type

Mixture-of-Experts (MoE), code language model

Use Cases

Areas:

code-specific tasks, math and reasoning, programming languages extension

Applications:

AI code assistance, software development, research in code intelligence

Primary Use Cases:

Code completion, Code insertion, Chatbot assistance for coding queries

Limitations:

Optimal performance requires specified hardware, Compatibility with certain APIs necessary

Additional Notes

Supported languages expanded to 338 from 86. Allows for commercial use.

Supported Languages

languages_supported (, programming languages: 338, extended from 86), competence_level (high proficiency in code-specific tasks)

Training Details

Data Sources:

DeepSeekMoE framework, intermediate checkpoint of DeepSeek-V2, additional 6 trillion tokens

Data Volume:

6 trillion tokens

Methodology:

Mixture-of-experts mechanism for enhanced coding and reasoning

Context Length:

128000

Hardware Used:

BF16 format inference requires 8*80GB GPUs

Model Architecture:

Mixture-of-Experts with active parameters

Input Output

Input Format:

Prompt-based input

Accepted Modalities:

text

Output Format:

Model-generated text responses

Performance Tips:

Use of specified HF or vLLM frameworks for optimal inference.

LLM Name	DeepSeek Coder V2 Base
Repository 🤗	https://huggingface.co/deepseek-ai/DeepSeek-Coder-V2-Base
Model Size	235.7b
Required VRAM	387 GB
Updated	2026-05-28
Maintainer	deepseek-ai
Model Type	deepseek_v2
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Generates Code	Yes
Model Architecture	DeepseekV2ForCausalLM
License	other
Context Length	163840
Model Max Length	163840
Transformers Version	4.39.3
Vocabulary Size	102400
Torch Data Type	bfloat16

Rank the DeepSeek Coder V2 Base capabilities

Have you tried this model? Rate its performance — this feedback helps the ML community find the right model for their needs.

Instruction Following and Task Automation
Factuality and Completeness of Knowledge
Censorship and Alignment
Data Analysis and Insight Generation
Text Generation
Text Summarization and Feature Extraction
Code Generation
Multi-Language Support and Translation

Best Alternatives to DeepSeek Coder V2 Base

Best Alternatives	Context / RAM	Downloads	Likes
DeepSeek Coder V2 Instruct	160K / 387 GB	5268	688
...eepSeek Coder V2 Instruct 0724	160K / 378.4 GB	358	117

Note: green Score (e.g. "73.2") means that the model is better than deepseek-ai/DeepSeek-Coder-V2-Base.