Cerebras-GPT 13B needs ~22.3 GB VRAM. RTX 4000 Ada 20GB has 20.0 GB. With Q5_K_M quantization, expect ~18 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
2.3 GB over capacity — needs offload or smaller quantization
Fit status
Very compromised (needs ~1 GB host RAM)
Decode
18.2 tok/s
TTFT
10634 ms
Safe context
12K
Memory
22.3 GB / 20.0 GB
Offload
10%
It fits through host-memory offload, and offload is the main reason performance drops.
CPU or host-memory offload is active
About 10% 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.
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.
Buy headroom, not only minimum fit
A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
Increase host RAM if you keep offloading
This setup may need roughly 1.0 GB of extra host RAM just for the offloaded portion, before OS and other tools.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | B | Tight fit | 30.6 tok/s | 3451 ms | 12K |
| Coding | C | Very compromised (needs ~1 GB host RAM) | 18.2 tok/s | 10634 ms | 12K |
| Agentic Coding | F | Too heavy | 8.5 tok/s | 33199 ms | 12K |
| Reasoning | C | Very compromised (needs ~1 GB host RAM) | 18.2 tok/s | 12567 ms | 12K |
| RAG | F | Too heavy | 8.5 tok/s | 41499 ms |
How Cerebras-GPT 13B (13B params) fits at each quantization level on RTX 4000 Ada 20GB (20.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 5.1 GB | Low | B63 |
Q3_K_S | 3 | 6.4 GB | Low | B64 |
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 3090 24GBBudget pickRemoves host-memory offload, which is usually the single biggest latency and throughput win.
Raises estimated decode speed by about 292%.
~$1,499 MSRP
RTX 4090 24GBBest valueRemoves host-memory offload, which is usually the single biggest latency and throughput win.
Raises estimated decode speed by about 359%.
~$1,599 MSRP
RTX PRO 4000 Blackwell 24GBNVIDIA upgradeRemoves host-memory offload, which is usually the single biggest latency and throughput win.
Raises estimated decode speed by about 238%.
~$1,599 MSRP
Yes, RTX 4000 Ada 20GB can run Cerebras-GPT 13B with a C grade (Very compromised (needs ~1 GB host RAM)). Expected decode speed: 18.2 tok/s.
Cerebras-GPT 13B (13B parameters) requires approximately 22.3 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 4000 Ada 20GB, Cerebras-GPT 13B achieves approximately 18.2 tokens per second decode speed with a time-to-first-token of 10634ms using Q5_K_M quantization.
For coding workloads, Cerebras-GPT 13B on RTX 4000 Ada 20GB receives a C grade with 18.2 tok/s and 12K context.
On RTX 4000 Ada 20GB, Cerebras-GPT 13B can safely use up to 12K 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-4000-ada-20gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
| 12K |
7.3 GB |
| Medium |
| B65 |
Q4_K_M | 4 | 7.9 GB | Medium | B65 |
Q5_K_M | 5 | 9.4 GB | High | B66 |
Q6_K | 6 | 10.7 GB | High | B67 |
Q8_0Best for your GPU | 8 | 13.9 GB | Very High | B66 |
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.