Cerebras-GPT 13B needs ~23.5 GB VRAM. RTX 5090 32GB has 32.0 GB. With Q5_K_M quantization, expect ~131 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
130.8 tok/s
TTFT
1480 ms
Safe context
30K
Memory
23.5 GB / 32.0 GB
This setup is broadly balanced for this model.
No major red flags
This recommendation has enough memory headroom and acceptable estimated speed for the selected workload.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | A | Runs well | 130.8 tok/s | 807 ms | 30K |
| Coding | A | Runs well | 130.8 tok/s | 1480 ms | 30K |
| Agentic Coding | A | Runs with offload (needs ~0.4 GB host RAM) | 92.3 tok/s | 3051 ms | 30K |
| Reasoning | A | Runs well | 130.8 tok/s | 1749 ms | 30K |
| RAG | A | Runs with offload (needs ~0.4 GB host RAM) | 92.3 tok/s | 3814 ms | 30K |
How Cerebras-GPT 13B (13B params) fits at each quantization level on RTX 5090 32GB (32.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 5.1 GB | Low | B60 |
Q3_K_S | 3 | 6.4 GB | Low | B60 |
NVFP4 | 4 | 7.3 GB | Medium | B61 |
Q4_K_M | 4 | 7.9 GB | Medium | B61 |
Q5_K_M | 5 | 9.4 GB | High | B62 |
Q6_K | 6 | 10.7 GB | High | B62 |
Q8_0 | 8 | 13.9 GB | Very High | B64 |
F16Best for your GPU | 16 | 26.7 GB | Maximum | B65 |
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 99Your hardware
| Model | Params | Grade | Decode | Capabilities |
|---|---|---|---|---|
 Qwen3-Coder 30B A3B Instruct | 30.5B | S | 181.6 tok/s | |
| Qwen 3.5 27B | 27B | S | 78.7 tok/s | |
| Qwen 3.6 27B | 27B | S | 79 tok/s | |
| Qwen 3.6 35B A3B | 35B | S | 128.2 tok/s | |
| Qwen3-VL 30B A3B Instruct | 30B | S | 187.8 tok/s |
Yes, RTX 5090 32GB can run Cerebras-GPT 13B with a A grade (Runs well). Expected decode speed: 130.8 tok/s.
Cerebras-GPT 13B (13B parameters) requires approximately 23.5 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 5090 32GB, Cerebras-GPT 13B achieves approximately 130.8 tokens per second decode speed with a time-to-first-token of 1480ms using Q5_K_M quantization.
For coding workloads, Cerebras-GPT 13B on RTX 5090 32GB receives a A grade with 130.8 tok/s and 30K context.
On RTX 5090 32GB, Cerebras-GPT 13B can safely use up to 30K 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-5090-32gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview: