DeepSeek-Coder V2 (class): Hardware & Business Fit
- Code
- Long context
Representative entry for the DeepSeek coding family. Sizes vary widely across releases — verify the exact variant and its footprint before deploying.
- Parameters
- ~16B
- Context
- ~128K tokens
- Deployment
- local
- VRAM @ 4-bit
- ~11GB
What DeepSeek-Coder V2 (class) is good for
- ▸Code completion
- ▸Repo-aware assistance
- ▸Refactoring
Best quantization choices
Approximate memory per quantization (weights + KV cache at modest context). Treat as ±.
| Quant | ~Memory | When to use |
|---|---|---|
| Q4_K_M | ~11GB | Best size/quality trade-off — the usual default for local serving. |
| Q8_0 | ~18GB | Higher fidelity; ~1.7× the memory of 4-bit. |
| FP16 | ~33GB | Full precision; largest footprint, best quality. |
Run DeepSeek-Coder V2 (class) locally
Pull and run with Ollama, or grab the weights from Hugging Face.
$ ollama run deepseek-coder-v2:16bdeepseek-ai/DeepSeek-Coder-V2-Lite-InstructCompatible hardware
Devices from our catalog graded for DeepSeek-Coder V2 (class), best fit first.
- NVIDIA B200 (placeholder)NVIDIA · Datacenter GPUs
Fits at FP16 (~33GB) with ~136GB headroom — about 5 concurrent instances.
FP16 · ~33GBRuns well - Supermicro 8x H100 SuperServerSupermicro · AI Servers
Fits at FP16 (~33GB) with ~530.2GB headroom — about 17 concurrent instances.
FP16 · ~33GBRuns well - Dell PowerEdge XE9680Dell · AI Servers
Fits at FP16 (~33GB) with ~530.2GB headroom — about 17 concurrent instances.
FP16 · ~33GBRuns well - AMD Instinct MI300XAMD · Datacenter GPUs
Fits at FP16 (~33GB) with ~136GB headroom — about 5 concurrent instances.
FP16 · ~33GBRuns well - Cloud B200 (Blackwell profile, to verify)Cloud · Cloud GPU Profiles
Fits at FP16 (~33GB) with ~125.4GB headroom — about 4 concurrent instances.
FP16 · ~33GBRuns well - NVIDIA H200 (141GB)NVIDIA · Datacenter GPUs
Fits at FP16 (~33GB) with ~91.1GB headroom — about 3 concurrent instances.
FP16 · ~33GBRuns well - Cloud H200 141GB (profile)Cloud · Cloud GPU Profiles
Fits at FP16 (~33GB) with ~91.1GB headroom — about 3 concurrent instances.
FP16 · ~33GBRuns well - NVIDIA H100 (80GB)NVIDIA · Datacenter GPUs
Fits at FP16 (~33GB) with ~37.4GB headroom — about 2 concurrent instances.
FP16 · ~33GBRuns well - Cloud H100 80GB (profile)Cloud · Cloud GPU Profiles
Fits at FP16 (~33GB) with ~37.4GB headroom — about 2 concurrent instances.
FP16 · ~33GBRuns well - NVIDIA RTX PRO 6000 BlackwellNVIDIA · Professional GPUs
Fits at FP16 (~33GB) with ~51.5GB headroom — about 2 concurrent instances.
FP16 · ~33GBRuns well
Use inside the AI Business OS
DeepSeek-Coder V2 (class) suits these AI Business OS agent archetypes:
A model is only the engine. Inside the AI Business OS it is wrapped with permissions, tools, connectors, RAG and audit so it can actually do business work safely — see how the AI Business OS works →
Frequently asked questions
What hardware do I need to run DeepSeek-Coder V2 (class)?+
At 4-bit you need roughly ~11GB of usable memory. The minimum self-hostable option in our catalog is the Intel Arc A770 16GB. For a comfortable run we recommend the NVIDIA B200 (placeholder).
Which quantization should I use for DeepSeek-Coder V2 (class)?+
Q4_K_M is the usual default — the best size/quality trade-off. Step up to Q8_0 or FP16 if you have spare memory and want higher fidelity.
Should I run DeepSeek-Coder V2 (class) locally or in the cloud?+
Local-first is recommended for DeepSeek-Coder V2 (class). It fits comfortably on hardware you can own, keeping data private and costs predictable.
Other sizes in the DeepSeek family
All DeepSeek models →Same family, different size. Pick the variant that fits your hardware.
Related models
Similar picks — family siblings and nearest-size models of the same kind.
Use DeepSeek-Coder V2 (class) inside your AI Business OS
BrainOutput helps you run DeepSeek-Coder V2 (class) as a private business agent — wrapped with the tools, connectors, RAG and guardrails it needs to do real work on hardware you control.
Use this model in your AI Business OS