DeepSeek V3
671B-parameter MoE model with 37B active per token. Trained for roughly $5.6M of compute — a landmark in cost-efficient frontier training. Frontier-class quality at a fraction of the cost of the closed proprietary frontier.
The DeepSeek licence permits commercial use with limited restrictions on military and unlawful applications. Running V3 yourself requires serious hardware (8× H100 at fp8); most teams will use it via the DeepSeek API or providers like Together.
- Parameters
- 671B
- Context length
- 128K
- Modality
- text
- Released
- 2024-12-26
Memory & hardware
- VRAM (fp16)
- 1342 GB
- VRAM (Q4)
- 402.6 GB
- Recommended
- 8× H100 80GB (fp8)
- Quantizations
- fp8, fp16, q4_k_m
Benchmarks
Hosted inference pricing
USD per million tokens.
| Provider | Input | Output | |
|---|---|---|---|
| deepinfraCheapest | $0.49 | $0.89 | |
| together | $1.25 | $1.25 |
Run it yourself
Drop-in commands for the three most common open-source inference paths. The Ollama tag is a best-effort match against the registry; verify the size variant before pulling.
vllm serve deepseek-ai/DeepSeek-V3
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("deepseek-ai/DeepSeek-V3")
model = AutoModelForCausalLM.from_pretrained(
"deepseek-ai/DeepSeek-V3", device_map="auto", torch_dtype="auto"
)deepseek-ai/DeepSeek-V3 Related models
Same family or similar size — useful when shopping around.
Reasoning model trained with reinforcement learning on top of DeepSeek V3-Base. MIT licence — even the weights are unrestricted, making R1 the most permissively-licensed frontier reasoning model. Generates long internal chains-of-thought before answering, trading latency for accuracy on math, code, and reasoning benchmarks. Distilled variants (e.g. R1 Distill Llama 70B) recover most of the quality at much smaller scales.
- Context
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- License
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- License
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- License
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- Context
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- License
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NVIDIA's reward-modelling research vehicle. Trained primarily to be a synthetic-data-generation specialist rather than a chat-first model. Useful for teams building instruction-tuning datasets at scale.
- Context
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- License
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xAI's first open-weights release: a 314B-parameter mixture-of-experts model. Apache 2.0 licensed. Largely a research artefact at this size — most users will run smaller models for production — but useful as a permissively-licensed reference for MoE research.
- Context
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- License
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- VRAM Q4
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