StarCoder2-15B
Trained on 4+ trillion tokens across 600+ programming languages from The Stack v2. Transparent, permissively trained code generation workhorse.
The brutal truth
Enterprise legal teams love the clean training dataset provenance. Good autocomplete, but shorter context than modern models.
Files and Versions
Direct inspected tree artifacts from Hugging Face. Verified filenames, exact byte sizes, model weight shards, and configuration definitions.
Files and versions are hosted on Hugging Face. You can view all branches, commits, model weights, and tokenizer artifacts directly.
Inspect bigcode/starcoder2-15b /tree/main ↗Inspect /tree/main Repository ↗Decision matrix
✓ RUN / USE THIS IF…
- •You require strict data provenance and copyright safety
- •You run local 12GB GPU servers
- •You primarily need fast inline autocomplete
- •Your 24GB GPU comfortably fits the model weights (~10GB VRAM) with dedicated headroom for inference.
✕ WALK AWAY IF…
- •You need 128K+ repository context
- •You want agentic multi-file refactoring
- •Your GPU has less than 10GB VRAM and you cannot tolerate slow CPU/system RAM offload paging.
- •You prefer a plug-and-play web app interface and don't want to manage local model checkpoints, drivers, or runtimes.
True cost of ownership
About
Trained on 4+ trillion tokens across 600+ programming languages from The Stack v2. Transparent, permissively trained code generation workhorse.
What it does
Download the weights from Hugging Face, pick a quantisation that matches your GPU, and run it locally with Ollama, llama.cpp, vLLM, or a compatible runtime. Clean license & IP-safe code autocomplete runs on your own hardware — no per-token bill, no third-party data handling.
Key features
Permissive training data filtering for enterprise IP safety
Excellent low-resource language completion
Runs fast on consumer 12GB GPUs
Use cases
Inline edits, refactors, and test generation inside the editor.
Flag null-safety, security, and style issues before merge.
Turn long procedural functions into typed, testable modules.
Generate integration tests from route definitions and schemas.
Quantization & Hardware Fit
VRAM Hardware Compatibility & Memory Allocation
| Quant | VRAM | Quality / Fit on 24GB | Install / Run Command (OLLAMA) | |
|---|---|---|---|---|
| Q4_K_M● ACTIVE | 10 GB | Standard · ✓ Fits (14GB spare) | ollama run starcoder2:15b | |
| Q5_K_M | 12 GB | Standard · ✓ Fits (12GB spare) | ollama run starcoder2:15b | |
| Q8_0 | 17 GB | Standard · ✓ Fits (7GB spare) | ollama run starcoder2:15b | |
| FP16 | 30 GB | Standard · ✕ Needs 30GB | ollama run starcoder2:15b |
Buyer doubts, cleared
Can I actually run StarCoder2-15B on my 24GB GPU?
What's the fine print on the license?
Does the 16K tokens actually hold up in practice?
User reviews
We evaluated StarCoder2-15B against two hosted alternatives. It won on cost-per-output but lost on latency. For batch work it is a clear yes.
The output reads naturally once you tune the prompt. It has cut my first-draft time roughly in half.
Clean interface, consistent results once you land on a prompt you like. Wish there were more export presets for client handoffs.
Switched our internal workflow to StarCoder2-15B after the last pricing change. Nobody noticed a quality difference, and the bill dropped by half.
