Cerebras-GPT 13B needs ~18.9 GB VRAM. Radeon RX 7900M 16GB has 16.0 GB. With Q3_K_S quantization, expect ~26 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
5.9 GB over capacity — needs offload or smaller quantization
Fit status
Too heavy
Decode
14.3 tok/s
TTFT
13529 ms
Safe context
6K
Memory
21.9 GB / 16.0 GB
Offload
30%
It fits through host-memory offload, and offload is the main reason performance drops.
CPU or host-memory offload is active
About 20% 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 | Runs with offload (needs ~0.6 GB host RAM) | 24.3 tok/s | 4342 ms | 6K |
| Coding | F | Too heavy | 14.3 tok/s | 13529 ms | 6K |
| Agentic Coding | F | Too heavy | 6.6 tok/s | 42734 ms | 6K |
| Reasoning | F | Too heavy | 14.3 tok/s | 15989 ms | 6K |
| RAG | F | Too heavy | 6.6 tok/s | 53418 ms | 6K |
How Cerebras-GPT 13B (13B params) fits at each quantization level on Radeon RX 7900M 16GB (16.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 5.1 GB | Low | B65 |
Q3_K_S | 3 | 6.4 GB | Low | B66 |
NVFP4 | 4 | 7.3 GB | Medium | B67 |
Q4_K_M | 4 | 7.9 GB | Medium | B68 |
Q5_K_M | 5 | 9.4 GB | High | B67 |
Q6_KBest for your GPU | 6 | 10.7 GB | High | B67 |
Q8_0 | 8 | 13.9 GB | Very High | F0 |
F16 | 16 | 26.7 GB | Maximum | F0 |
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-Optionen
Makes the model fit on the accelerator instead of staying completely out of reach.
Raises estimated decode speed by about 117%.
ca. $899 MSRP
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.
ca. $999 MSRP
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.
ca. $1,899 MSRP
Yes, Radeon RX 7900M 16GB can run Cerebras-GPT 13B at Q3_K_S quantization (Very compromised (needs ~1 GB host RAM)). The recommended Q5_K_M requires 21.9 GB which exceeds available memory, but at Q3_K_S it needs only 18.9 GB. Expected decode speed: 26.1 tok/s.
Cerebras-GPT 13B (13B parameters) requires approximately 21.9 GB at Q5_K_M quantization. On Radeon RX 7900M 16GB, it fits at Q3_K_S using 18.9 GB.
The recommended quantization is Q5_K_M, but on Radeon RX 7900M 16GB the best fitting quantization is Q3_K_S, which uses 18.9 GB.
On Radeon RX 7900M 16GB, Cerebras-GPT 13B achieves approximately 26.1 tokens per second decode speed with a time-to-first-token of 7417ms using Q3_K_S quantization.
For coding workloads, Cerebras-GPT 13B on Radeon RX 7900M 16GB receives a F grade with 14.3 tok/s and 6K context.
On Radeon RX 7900M 16GB, Cerebras-GPT 13B can safely use up to 11K tokens of context at Q3_K_S quantization. The model's official context limit is 131K, but available memory constrains the safe maximum.
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.
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/cerebras-gpt-13b-on-rx-7900m-16gb" 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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