StarCoder2 15B: Hardware & Business Fit
- Code
The largest StarCoder2, trained on permissively-licensed code across many languages. Review the BigCode OpenRAIL-M license restrictions before commercial use.
- Parameters
- ~15B
- Context
- ~16K tokens
- Deployment
- local
- VRAM @ 4-bit
- ~10GB
What StarCoder2 15B is good for
- ▸Code completion at quality
- ▸Repo-aware assistance
- ▸Refactoring help
Best quantization choices
Approximate memory per quantization (weights + KV cache at modest context). Treat as ±.
| Quant | ~Memory | When to use |
|---|---|---|
| Q4_K_M | ~10GB | Best size/quality trade-off — the usual default for local serving. |
| Q8_0 | ~17GB | Higher fidelity; ~1.7× the memory of 4-bit. |
| FP16 | ~30GB | Full precision; largest footprint, best quality. |
Run StarCoder2 15B locally
Pull and run with Ollama, or grab the weights from Hugging Face.
$ ollama run starcoder2:15bbigcode/starcoder2-15bCompatible hardware
Devices from our catalog graded for StarCoder2 15B, best fit first.
- NVIDIA B200 (placeholder)NVIDIA · Datacenter GPUs
Fits at FP16 (~30GB) with ~139GB headroom — about 5 concurrent instances.
FP16 · ~30GBRuns well - Supermicro 8x H100 SuperServerSupermicro · AI Servers
Fits at FP16 (~30GB) with ~533.2GB headroom — about 18 concurrent instances.
FP16 · ~30GBRuns well - Dell PowerEdge XE9680Dell · AI Servers
Fits at FP16 (~30GB) with ~533.2GB headroom — about 18 concurrent instances.
FP16 · ~30GBRuns well - AMD Instinct MI300XAMD · Datacenter GPUs
Fits at FP16 (~30GB) with ~139GB headroom — about 5 concurrent instances.
FP16 · ~30GBRuns well - Cloud B200 (Blackwell profile, to verify)Cloud · Cloud GPU Profiles
Fits at FP16 (~30GB) with ~128.4GB headroom — about 5 concurrent instances.
FP16 · ~30GBRuns well - NVIDIA H200 (141GB)NVIDIA · Datacenter GPUs
Fits at FP16 (~30GB) with ~94.1GB headroom — about 4 concurrent instances.
FP16 · ~30GBRuns well - Cloud H200 141GB (profile)Cloud · Cloud GPU Profiles
Fits at FP16 (~30GB) with ~94.1GB headroom — about 4 concurrent instances.
FP16 · ~30GBRuns well - NVIDIA H100 (80GB)NVIDIA · Datacenter GPUs
Fits at FP16 (~30GB) with ~40.4GB headroom — about 2 concurrent instances.
FP16 · ~30GBRuns well - Cloud H100 80GB (profile)Cloud · Cloud GPU Profiles
Fits at FP16 (~30GB) with ~40.4GB headroom — about 2 concurrent instances.
FP16 · ~30GBRuns well - NVIDIA RTX PRO 6000 BlackwellNVIDIA · Professional GPUs
Fits at FP16 (~30GB) with ~54.5GB headroom — about 2 concurrent instances.
FP16 · ~30GBRuns well
Use inside the AI Business OS
StarCoder2 15B 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 StarCoder2 15B?+
At 4-bit you need roughly ~10GB of usable memory. The minimum self-hostable option in our catalog is the NVIDIA GeForce RTX 3060 12GB. For a comfortable run we recommend the NVIDIA B200 (placeholder).
Which quantization should I use for StarCoder2 15B?+
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 StarCoder2 15B locally or in the cloud?+
Local-first is recommended for StarCoder2 15B. It fits comfortably on hardware you can own, keeping data private and costs predictable.
Other sizes in the StarCoder family
All StarCoder 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 StarCoder2 15B inside your AI Business OS
BrainOutput helps you run StarCoder2 15B 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