StarCoder2 15B needs ~15.6 GB VRAM. NVIDIA A30 24GB has 24.0 GB. With Q5_K_M quantization, expect ~75 tok/s.
Operating mode
Interactive favors responsiveness, while light API and scale-out lean harder on serving readiness. The fit stays the same, but the recommendation lens changes.
Current mode
Balanced
Balanced for general local use. Keeps the ranking neutral across personal and serving workflows.
Select quantization to explore
Fit status
Runs well
Decode
75.0 tok/s
TTFT
2580 ms
Safe context
16K
Memory
15.6 GB / 24.0 GB
This setup is broadly balanced for this model.
No major red flags
This recommendation has enough memory headroom and acceptable estimated speed for the selected workload.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | B | Runs well | 75.0 tok/s | 1407 ms | 16K |
| Coding | B | Runs well | 75.0 tok/s | 2580 ms | 16K |
| Agentic Coding | B | Runs well | 75.0 tok/s | 3753 ms | 16K |
| Reasoning | B | Runs well | 75.0 tok/s | 3049 ms | 16K |
| RAG | B | Runs well | 75.0 tok/s | 4691 ms | 16K |
How StarCoder2 15B (15B params) fits at each quantization level on NVIDIA A30 24GB (24.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 5.9 GB | Low | C47 |
Q3_K_S | 3 | 7.4 GB | Low | C48 |
NVFP4 | 4 | 8.4 GB | Medium | C49 |
Q4_K_M | 4 | 9.2 GB | Medium | C49 |
Q5_K_M | 5 | 10.8 GB | High | C51 |
Q6_K | 6 | 12.3 GB | High | C52 |
Q8_0Best for your GPU | 8 | 16.1 GB | Very High | C51 |
F16 | 16 | 30.7 GB | Maximum | F0 |
Copy-paste commands to run StarCoder2 15B on your machine.
Run
docker run --rm -it ghcr.io/ggerganov/llama.cpp:full \
--hf-repo "bigcode/starcoder2-15b" \
--hf-file "starcoder2-15b-Q5_K_M.gguf" \
-c 4096 -ngl 99Yes, NVIDIA A30 24GB can run StarCoder2 15B with a B grade (Runs well). Expected decode speed: 75.0 tok/s.
StarCoder2 15B (15B parameters) requires approximately 15.6 GB of memory with Q5_K_M quantization.
The recommended quantization for StarCoder2 15B is Q5_K_M, which balances quality and memory efficiency.
On NVIDIA A30 24GB, StarCoder2 15B achieves approximately 75.0 tokens per second decode speed with a time-to-first-token of 2580ms using Q5_K_M quantization.
For coding workloads, StarCoder2 15B on NVIDIA A30 24GB receives a B grade with 75.0 tok/s and 16K context.
On NVIDIA A30 24GB, StarCoder2 15B can safely use up to 16K tokens of context. The model's official context limit is 16K, but available memory constrains the safe maximum.
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/starcoder2-15b-on-a30-24gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview: