Cerebras-GPT 13B needs ~21.5 GB but RTX 3080 Ti 12GB only has 12.0 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
9.5 GB over capacity — needs offload or smaller quantization
Fit status
Too heavy
Decode
16.1 tok/s
TTFT
12008 ms
Safe context
4K
Memory
21.5 GB / 12.0 GB
Offload
40%
Usable VRAM is the main blocker for this model.
Not enough usable memory
The model needs 21.5 GB, but this setup only exposes 12.0 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 | 27.7 tok/s | 3811 ms | 4K |
| Coding | F | Too heavy | 16.1 tok/s | 12008 ms | 4K |
| Agentic Coding | F | Too heavy | 11.0 tok/s | 25526 ms | 4K |
| Reasoning | F | Too heavy | 16.1 tok/s | 14192 ms | 4K |
| RAG | F | Too heavy | 11.0 tok/s | 31908 ms | 4K |
How Cerebras-GPT 13B (13B params) fits at each quantization level on RTX 3080 Ti 12GB (12.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 5.1 GB | Low | B68 |
Q3_K_S | 3 | 6.4 GB | Low | B68 |
NVFP4 | 4 | 7.3 GB | Medium | B68 |
Q4_K_MBest for your GPU | 4 | 7.9 GB | Medium | B68 |
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 |
升级选项
RTX 4000 Ada 20GB最佳性价比Makes the model fit on the accelerator instead of staying completely out of reach.
Adds memory headroom for longer context windows and future model growth.
~$1,250 MSRP
RTX 3090 24GB经济之选Makes 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.
~$1,499 MSRP
RTX 4090 24GBNVIDIA 升级Makes 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.
~$1,599 MSRP
No, Cerebras-GPT 13B requires more memory than RTX 3080 Ti 12GB provides.
Cerebras-GPT 13B (13B parameters) requires approximately 21.5 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 3080 Ti 12GB, Cerebras-GPT 13B achieves approximately 16.1 tokens per second decode speed with a time-to-first-token of 12008ms using Q5_K_M quantization.
For coding workloads, Cerebras-GPT 13B on RTX 3080 Ti 12GB receives a F grade with 16.1 tok/s and 4K context.
On RTX 3080 Ti 12GB, 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-3080-ti-12gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
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