DeepSeek-V3 - AI Language Models Tool

Overview

DeepSeek‑V3 is an open‑weight flagship large language model (base and chat variants) built around a sparse Mixture‑of‑Experts (MoE) design. The released checkpoints describe a 671B total parameter MoE with roughly 37B activated parameters per token, a 128K token context window, and training on ~14.8 trillion tokens; the model was engineered for long‑context reasoning, tool use and efficient inference (FP8 support & multi‑token prediction). DeepSeek publishes both weights and an OpenAI‑compatible API, and the project provides deployment recipes for community inference stacks (SGLang, LMDeploy, vLLM, TensorRT‑LLM) so developers can run the model locally or via hosted endpoints. ([huggingface.co](https://huggingface.co/deepseek-ai/DeepSeek-V3)) Practically, DeepSeek‑V3 targets high‑throughput workloads where cost matters: the team emphasizes FP8 mixed‑precision training and auxiliary‑loss‑free load balancing for MoE routing, and they provide a multi‑token‑prediction (MTP) module that can be used for speculative decoding to accelerate inference. The model card and downstream docs also signal first‑class compatibility with several inference frameworks and commercial usage under a permissive code license plus an explicit model license permitting commercial use. ([huggingface.co](https://huggingface.co/deepseek-ai/DeepSeek-V3))

Model Statistics

• Downloads: 1,282,492
• Likes: 4024
• Pipeline: text-generation

Model Details

Architecture and scale: DeepSeek‑V3 is a sparse MoE LLM with 671 billion total parameters and ~37 billion activated parameters per token; it adopts Multi‑Head Latent Attention (MLA) and a DeepSeekMoE routing design, plus an auxiliary‑loss‑free load balancing strategy intended to reduce MoE balancing degradation. Context & training: official notes list a 128K context window and pretraining on ~14.8T tokens; training leveraged an FP8 mixed‑precision pipeline and reported efficient cross‑node MoE communication. Post‑training and objectives: the release includes a Multi‑Token Prediction (MTP) module (additional ~14B weights in some distributions) and describes supervised fine‑tuning plus RLHF/distillation from a longer‑chain reasoning model to improve stepwise reasoning and output control. Deployment and tooling: DeepSeek provides community deployment recipes and lists first‑party or community support for SGLang, LMDeploy, vLLM, TensorRT‑LLM (BF16/INT4/8 support, FP8 where available), and guidance for AMD and Huawei Ascend hardware. Licensing & usage: code is released under MIT and the model license allows commercial use; DeepSeek also offers an OpenAI‑compatible hosted API for convenient integration. ([huggingface.co](https://huggingface.co/deepseek-ai/DeepSeek-V3))

Key Features

• Sparse Mixture‑of‑Experts with 671B total / 37B activated parameters
• 128K token context window for long‑form documents and research workflows
• FP8 mixed‑precision training and FP8/BF16 inference support for cost efficiency
• Multi‑Token Prediction (MTP) module enabling speculative decoding and faster inference
• OpenAI‑compatible API plus community deployment recipes for SGLang, LMDeploy, vLLM
• Permissive code license (MIT) and a model license that permits commercial use

Example Usage

Example (python):

import os
import openai

# Example: call DeepSeek's OpenAI‑compatible chat completions endpoint
# (set OPENAI_API_KEY to your DeepSeek API key). DeepSeek accepts the
# same chat/completions format; point the client at DeepSeek's base URL.
# See DeepSeek API docs for current model names and options. ([chat-deep.ai](https://chat-deep.ai/guide/how-to-build-chatbots-using-deepseek-ai-api/?utm_source=openai))

openai.api_key = os.getenv('OPENAI_API_KEY')
openai.api_base = 'https://api.deepseek.com'  # DeepSeek OpenAI‑compatible base

resp = openai.ChatCompletion.create(
    model='deepseek-chat',
    messages=[
        {"role": "system", "content": "You are a concise assistant."},
        {"role": "user", "content": "Explain how MoE models reduce inference FLOPs."}
    ],
    max_tokens=400,
    temperature=0.0
)

print(resp.choices[0].message['content'])

Pricing

DeepSeek publishes per‑token API pricing with cache‑aware rates: documentation (DeepSeek API docs) lists example V3.2 pricing at $0.028 per 1M input tokens (cache hit), $0.28 per 1M input tokens (cache miss), and $0.42 per 1M output tokens; DeepSeek also offers free starting credits and supports self‑hosting using the open weights under MIT. (Always confirm the current rates on DeepSeek's official docs before you bill users.) ([api-docs.deepseek.com](https://api-docs.deepseek.com/quick_start/pricing/?utm_source=openai))

Benchmarks

Total parameters: 671B (MoE total params) (Source: https://huggingface.co/deepseek-ai/DeepSeek-V3)

Activated parameters (per token): 37B (activated params) (Source: https://huggingface.co/deepseek-ai/DeepSeek-V3)

Context window: 128K tokens (Source: https://huggingface.co/deepseek-ai/DeepSeek-V3)

MMLU (reported, 5‑shot): 87.1 (5‑shot, English, base benchmark table) (Source: https://huggingface.co/deepseek-ai/DeepSeek-V3)

Code (HumanEval, Pass@1): 65.2 (base model, 0‑shot) (Source: https://huggingface.co/deepseek-ai/DeepSeek-V3)

Key Information

• Category: Language Models
• Type: AI Language Models Tool