DeepSeek Coder V2
Coding-focused MoE model with 21B active parameters out of 236B total. Supports 338 programming languages with strong performance across mainstream stacks (Python, TypeScript, Go, Rust, Java, C++) and competent results on niche languages where most open models falter. The DeepSeek licence applies — commercial use permitted with some application restrictions.
- Parameters
- 236B
- Context length
- 128K
- Modality
- text
- Released
- 2024-06-17
- Tokenizer
- LlamaTokenizerFast
Memory & hardware
- VRAM (fp16)
- 472 GB
- VRAM (Q4)
- 141.6 GB
- Recommended
- 4× A100 80GB or 2× H100
- Quantizations
- fp16, q8_0, q4_k_m
Benchmarks
Hosted inference pricing
No provider we track publishes a per-token price for this model today. What each one used to offer is listed below.
No longer listed
Providers that used to serve this model. We don't republish their old rates — the dates below are the provider's own.
- deepinfraAbsent from DeepInfra’s pricing page and model API when we checked on 20 September 2026.Source ↗
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-Coder-V2-Instruct
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("deepseek-ai/DeepSeek-Coder-V2-Instruct")
model = AutoModelForCausalLM.from_pretrained(
"deepseek-ai/DeepSeek-Coder-V2-Instruct", device_map="auto", torch_dtype="auto"
)deepseek-ai/DeepSeek-Coder-V2-Instruct Related models
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