Cerebras-GPT 13B needs ~22.7 GB VRAM. RTX 5090 Laptop 24GB has 24.0 GB. With Q5_K_M quantization, expect ~82 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
Tight fit
Decode
82.0 tok/s
TTFT
2360 ms
Safe context
18K
Memory
22.7 GB / 24.0 GB
This setup is broadly balanced for this model.
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.
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 | A | Runs well | 82.0 tok/s | 1288 ms | 18K |
| Coding | B | Tight fit | 82.0 tok/s | 2360 ms | 18K |
| Agentic Coding | F | Too heavy | 32.5 tok/s | 8661 ms | 18K |
| Reasoning | B | Tight fit | 82.0 tok/s | 2790 ms | 18K |
| RAG | F | Too heavy | 32.5 tok/s | 10827 ms | 18K |
How Cerebras-GPT 13B (13B params) fits at each quantization level on RTX 5090 Laptop 24GB (24.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 5.1 GB | Low | B62 |
Q3_K_S | 3 | 6.4 GB | Low | B62 |
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
RTX 5090 32GBBudget pickRaises estimated decode speed by about 60%.
Adds memory headroom for longer context windows and future model growth.
~$1,999 MSRP
RTX PRO 4500 Blackwell 32GBBest valueAdds memory headroom for longer context windows and future model growth.
~$2,499 MSRP
RTX 5000 Ada 32GBNVIDIA upgradeAdds memory headroom for longer context windows and future model growth.
~$4,000 MSRP
Yes, RTX 5090 Laptop 24GB can run Cerebras-GPT 13B with a B grade (Tight fit). Expected decode speed: 82.0 tok/s.
Cerebras-GPT 13B (13B parameters) requires approximately 22.7 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 RTX 5090 Laptop 24GB, Cerebras-GPT 13B achieves approximately 82.0 tokens per second decode speed with a time-to-first-token of 2360ms using Q5_K_M quantization.
For coding workloads, Cerebras-GPT 13B on RTX 5090 Laptop 24GB receives a B grade with 82.0 tok/s and 18K context.
On RTX 5090 Laptop 24GB, Cerebras-GPT 13B can safely use up to 18K 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-rtx-5090-laptop-24gb" 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 |
| B63 |
Q4_K_M | 4 | 7.9 GB | Medium | B63 |
Q5_K_M | 5 | 9.4 GB | High | B64 |
Q6_K | 6 | 10.7 GB | High | B65 |
Q8_0Best for your GPU | 8 | 13.9 GB | Very High | B66 |
F16 | 16 | 26.7 GB | Maximum | F0 |
Buy headroom, not only minimum fit. A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.