Cerebras-GPT 13B needs ~18.9 GB VRAM. Intel Arc Pro B50 16GB has 16.0 GB. With Q3_K_S quantization, expect ~10 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
5.9 GB over capacity — needs offload or smaller quantization
Fit status
Too heavy
Decode
5.3 tok/s
TTFT
36524 ms
Safe context
6K
Memory
21.9 GB / 16.0 GB
Offload
30%
It fits through host-memory offload, and offload is the main reason performance drops.
CPU or host-memory offload is active
About 20% of the working set spills out of accelerator memory, which usually hurts latency and sustained decode throughput.
Very little memory headroom
You can run the model, but there is not much room left for longer context, bigger batches, extra apps, or future model updates.
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.
Remove offload with more accelerator memory
Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.
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.
Buy headroom, not only minimum fit
A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | C | Runs with offload (needs ~0.6 GB host RAM) | 8.9 tok/s | 11916 ms | 6K |
| Coding | F | Too heavy | 5.3 tok/s | 36524 ms | 6K |
| Agentic Coding | F | Too heavy | 2.5 tok/s | 112638 ms | 6K |
| Reasoning | F | Too heavy | 5.3 tok/s | 43165 ms | 6K |
| RAG | F | Too heavy | 2.5 tok/s | 140797 ms | 6K |
How Cerebras-GPT 13B (13B params) fits at each quantization level on Intel Arc Pro B50 16GB (16.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 5.1 GB | Low | B65 |
Q3_K_S | 3 | 6.4 GB | Low | B66 |
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 99Upgrade options
Intel Arc Pro B60 24GBBudget pickMakes the model fit on the accelerator instead of staying completely out of reach.
Removes host-memory offload, which is usually the single biggest latency and throughput win.
~$599 MSRP
RTX 5090 32GBBiggest leapMakes the model fit on the accelerator instead of staying completely out of reach.
Removes host-memory offload, which is usually the single biggest latency and throughput win.
~$1,999 MSRP
Intel Data Center GPU Max 1550 128GBBest valueMakes the model fit on the accelerator instead of staying completely out of reach.
Removes host-memory offload, which is usually the single biggest latency and throughput win.
~$15,000 MSRP
Gaudi 3 128GBIntel upgradeMakes the model fit on the accelerator instead of staying completely out of reach.
Removes host-memory offload, which is usually the single biggest latency and throughput win.
~$15,000 MSRP
Yes, Intel Arc Pro B50 16GB can run Cerebras-GPT 13B at Q3_K_S quantization (Very compromised (needs ~1 GB host RAM)). The recommended Q5_K_M requires 21.9 GB which exceeds available memory, but at Q3_K_S it needs only 18.9 GB. Expected decode speed: 9.6 tok/s.
Cerebras-GPT 13B (13B parameters) requires approximately 21.9 GB at Q5_K_M quantization. On Intel Arc Pro B50 16GB, it fits at Q3_K_S using 18.9 GB.
The recommended quantization is Q5_K_M, but on Intel Arc Pro B50 16GB the best fitting quantization is Q3_K_S, which uses 18.9 GB.
On Intel Arc Pro B50 16GB, Cerebras-GPT 13B achieves approximately 9.6 tokens per second decode speed with a time-to-first-token of 20215ms using Q3_K_S quantization.
For coding workloads, Cerebras-GPT 13B on Intel Arc Pro B50 16GB receives a F grade with 5.3 tok/s and 6K context.
On Intel Arc Pro B50 16GB, Cerebras-GPT 13B can safely use up to 11K tokens of context at Q3_K_S quantization. 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-arc-pro-b50-16gb" 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 |
| B67 |
Q4_K_M | 4 | 7.9 GB | Medium | B68 |
Q5_K_M | 5 | 9.4 GB | High | B67 |
Q6_KBest for your GPU | 6 | 10.7 GB | High | B67 |
Q8_0 | 8 | 13.9 GB | Very High | F0 |
F16 | 16 | 26.7 GB | Maximum | F0 |
Remove offload with more accelerator memory. Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.
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.