Cerebras-GPT 13B needs ~33.1 GB VRAM. Intel Data Center GPU Max 1550 128GB has 128.0 GB. With Q5_K_M quantization, expect ~182 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
182.0 tok/s
TTFT
1064 ms
Safe context
131K
Memory
33.1 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 | B | Runs well | 182.0 tok/s | 580 ms | 131K |
| Coding | B | Runs well | 182.0 tok/s | 1064 ms | 131K |
| Agentic Coding | B | Runs well | 182.0 tok/s | 1547 ms | 131K |
| Reasoning | B | Runs well | 182.0 tok/s | 1257 ms | 131K |
| RAG | B | Runs well | 182.0 tok/s | 1934 ms | 131K |
How Cerebras-GPT 13B (13B 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 | 5.1 GB | Low | C54 |
Q3_K_S | 3 | 6.4 GB | Low | C54 |
NVFP4 | 4 |
Copy-paste commands to run Cerebras-GPT 13B on your machine.
Run
docker run --rm -it ghcr.io/ggerganov/llama.cpp:full \
--hf-repo "cerebras/Cerebras-GPT-13B" \
--hf-file "Cerebras-GPT-13B-Q5_K_M.gguf" \
-c 4096 -ngl 99Yes, Intel Data Center GPU Max 1550 128GB can run Cerebras-GPT 13B with a B grade (Runs well). Expected decode speed: 182.0 tok/s.
Cerebras-GPT 13B (13B parameters) requires approximately 33.1 GB of memory with Q5_K_M quantization.
The recommended quantization for Cerebras-GPT 13B is Q5_K_M, which balances quality and memory efficiency.
On Intel Data Center GPU Max 1550 128GB, Cerebras-GPT 13B achieves approximately 182.0 tokens per second decode speed with a time-to-first-token of 1064ms using Q5_K_M quantization.
For coding workloads, Cerebras-GPT 13B on Intel Data Center GPU Max 1550 128GB receives a B grade with 182.0 tok/s and 131K context.
On Intel Data Center GPU Max 1550 128GB, Cerebras-GPT 13B can safely use up to 131K tokens of context. The model's official context limit is 131K, 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/cerebras-gpt-13b-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:
7.3 GB |
| Medium |
| C54 |
Q4_K_M | 4 | 7.9 GB | Medium | C54 |
Q5_K_M | 5 | 9.4 GB | High | C54 |
Q6_K | 6 | 10.7 GB | High | C54 |
Q8_0 | 8 | 13.9 GB | Very High | C55 |
F16Best for your GPU | 16 | 26.7 GB | Maximum | B56 |
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.