Budget Pick
NVIDIA GeForce RTX 3080 Ti12 GB VRAM · ~81.1 tok/s
Lowest cost that meets recommended VRAM
Check price on AmazonCompatibility Check
Can I Run StarCoder2 15B?
StarCoder2 15B is a 15B parameter model from the StarCoder family. Check if your hardware can handle it.
Share this hardware check
Send this page to a friend or teammate so they can check whether StarCoder2 15B fits their hardware too.
Social proof
59% of 1,608 scanned PCs run StarCoder2 15B fully on GPU.
1,219 keep at least some work on GPU. Based on anonymous compatibility checks.
Full GPU
949Partial GPU
11Hybrid CPU+GPU
259CPU Only
256Can't Run
133Test Your Hardware
Detecting your hardware...
Hardware Requirements
Beginner tip: minimum values mean the model can start, while recommended values usually feel smoother during real use. VRAM is your GPU's dedicated memory; RAM is your system memory used as fallback. See the full glossary.
| Quantization | File Size | Min VRAM | Recommended VRAM | Min RAM | Context |
|---|---|---|---|---|---|
| Q4_K_MEasiest | 9 GB | 10.5 GB | 12 GB | 12 GB | 4K / 16K |
| Q8_0 | 16 GB | 17.5 GB | 20 GB | 20 GB | 4K / 16K |
Not sure your GPU has enough VRAM? Compare GPUs that can run StarCoder2 15B.
Recommended GPUs for StarCoder2 15B
These GPUs meet the recommended 12 GB VRAM for the Q4_K_M quantization. Estimated speeds are approximate and assume full GPU offloading.
Fastest Pick
NVIDIA GeForce RTX 509032 GB VRAM · ~159.3 tok/s
Highest estimated throughput
Check price on AmazonBest Value
NVIDIA GeForce RTX 3080 12GB12 GB VRAM · ~81.1 tok/s
Best speed per dollar of VRAM
Check price on AmazonNeed a detailed comparison? See all GPU rankings for StarCoder2 15B.
Strong OpenClaw Model Candidate
StarCoder2 15B is a common OpenClaw pick for local agent workflows. Use this model with Ollama, llama.cpp, or LM Studio, then confirm full OpenClaw hardware compatibility.
Why choose StarCoder2 15B?
General-purpose local model brief
- • Pilot testing with your own tasks
- • Controlled local experiments
Quantization tip: Benchmark at least two quantizations and validate with a task-specific eval set before production use.