StarCoder2 15B needs ~24.6 GB VRAM. Intel Data Center GPU Max 1550 128GB has 128.0 GB. With Q4_K_M quantization, expect ~210 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
210.0 tok/s
TTFT
922 ms
Safe context
957K
Memory
24.6 GB / 128.0 GB
The raw memory story may look fine, but the software ecosystem is still a constraint here.
Runtime ecosystem is narrower than CUDA
Intel GPUs can look attractive on memory per dollar, but local AI tooling, kernels, and model coverage are still broader and easier on CUDA today.
Prefer CUDA if you want the path of least resistance
If your goal is maximum runtime coverage, easier troubleshooting, and better support for new local AI releases, CUDA is usually still the safer upgrade path.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | C | Runs well | 210.0 tok/s | 503 ms | 957K |
| Coding | C | Runs well | 210.0 tok/s | 922 ms | 957K |
| Agentic Coding | C | Runs well | 210.0 tok/s | 1341 ms | 957K |
| Reasoning | C | Runs well | 210.0 tok/s | 1090 ms | 957K |
| RAG | C | Runs well | 210.0 tok/s | 1676 ms | 957K |
How StarCoder2 15B (15B params) fits at each quantization level on Intel Data Center GPU Max 1550 128GB (128.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 5.9 GB | Low | D38 |
Q3_K_S | 3 | 7.4 GB | Low | D38 |
NVFP4 | 4 |
Copy-paste commands to run StarCoder2 15B on your machine.
Run
lms load hf-second-state--starcoder2-15b-gguf && lms server startYes, Intel Data Center GPU Max 1550 128GB can run StarCoder2 15B with a C grade (Runs well). Expected decode speed: 210.0 tok/s.
StarCoder2 15B (15B parameters) requires approximately 24.6 GB of memory with Q4_K_M quantization.
The recommended quantization for StarCoder2 15B is Q4_K_M, which balances quality and memory efficiency.
On Intel Data Center GPU Max 1550 128GB, StarCoder2 15B achieves approximately 210.0 tokens per second decode speed with a time-to-first-token of 922ms using Q4_K_M quantization.
For coding workloads, StarCoder2 15B on Intel Data Center GPU Max 1550 128GB receives a C grade with 210.0 tok/s and 957K context.
On Intel Data Center GPU Max 1550 128GB, StarCoder2 15B can safely use up to 957K tokens of context. The model's official context limit is —, 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/hf-second-state--starcoder2-15b-gguf-on-max-1550-128gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
8.4 GB |
| Medium |
| D38 |
Q4_K_M | 4 | 9.2 GB | Medium | D38 |
Q5_K_M | 5 | 10.8 GB | High | D38 |
Q6_K | 6 | 12.3 GB | High | D38 |
Q8_0 | 8 | 16.1 GB | Very High | D39 |
F16Best for your GPU | 16 | 30.7 GB | Maximum | C40 |
Prefer CUDA if you want the path of least resistance. If your goal is maximum runtime coverage, easier troubleshooting, and better support for new local AI releases, CUDA is usually still the safer upgrade path.
Often yes, if your goal is the easiest setup and the widest runtime support. Intel can offer attractive memory capacity, but CUDA still tends to win on tooling maturity, guides, kernels, and model coverage for local AI.