Cerebras-GPT 13B needs ~21.1 GB but RTX 5050 8GB only has 8.0 GB. Try a smaller quantization or lighter model.
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
13.1 GB over capacity — needs offload or smaller quantization
Fit status
Too heavy
Decode
3.1 tok/s
TTFT
62945 ms
Safe context
4K
Memory
21.1 GB / 8.0 GB
Offload
60%
Usable VRAM is the main blocker for this model.
Not enough usable memory
The model needs 21.1 GB, but this setup only exposes 8.0 GB of usable VRAM.
Add more VRAM headroom
The first useful upgrade is more dedicated VRAM so you can fit the model without shrinking context or dropping to a much lower quant.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | F | Too heavy | 3.7 tok/s | 28560 ms | 4K |
| Coding | F | Too heavy | 3.1 tok/s | 62945 ms | 4K |
| Agentic Coding | F | Too heavy | 3.1 tok/s | 91557 ms | 4K |
| Reasoning | F | Too heavy | 3.1 tok/s | 74390 ms | 4K |
| RAG | F | Too heavy | 3.1 tok/s | 114446 ms | 4K |
How Cerebras-GPT 13B (13B params) fits at each quantization level on RTX 5050 8GB (8.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_KBest for your GPU | 2 | 5.1 GB | Low | B69 |
Q3_K_S | 3 | 6.4 GB | Low | F0 |
NVFP4 | 4 | 7.3 GB | Medium | F0 |
Q4_K_M | 4 | 7.9 GB | Medium | F0 |
Q5_K_M | 5 | 9.4 GB | High | F0 |
Q6_K | 6 | 10.7 GB | High | F0 |
Q8_0 | 8 | 13.9 GB | Very High | F0 |
F16 | 16 | 26.7 GB | Maximum | F0 |
Opciones de mejora
RTX 4000 Ada 20GBMejor relación calidad-precioHace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.
Sube la velocidad estimada de decodificación alrededor de un 487%.
~$1,250 MSRP
RTX 3090 24GBOpción económicaHace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.
Elimina el offload a memoria del sistema, que suele ser la mayor mejora individual en latencia y throughput.
~$1,499 MSRP
RTX 4090 24GBMejora NVIDIAHace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.
Elimina el offload a memoria del sistema, que suele ser la mayor mejora individual en latencia y throughput.
~$1,599 MSRP
No, Cerebras-GPT 13B requires more memory than RTX 5050 8GB provides.
Cerebras-GPT 13B (13B parameters) requires approximately 21.1 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 RTX 5050 8GB, Cerebras-GPT 13B achieves approximately 3.1 tokens per second decode speed with a time-to-first-token of 62945ms using Q5_K_M quantization.
For coding workloads, Cerebras-GPT 13B on RTX 5050 8GB receives a F grade with 3.1 tok/s and 4K context.
On RTX 5050 8GB, Cerebras-GPT 13B can safely use up to 4K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.
Add more VRAM headroom. The first useful upgrade is more dedicated VRAM so you can fit the model without shrinking context or dropping to a much lower quant.
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/cerebras-gpt-13b-on-rtx-5050-8gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
