32 GB VRAM (+8)1792 GB/s (+784)
A
Raises estimated decode speed by about 68%.130.8 tok/s decode
Raises estimated decode speed by about 68%.
Adds memory headroom for longer context windows and future model growth.
~$1,999 MSRP
Can Cerebras-GPT 13B run on RTX 3090 Ti 24GB?
YES — Tight Fit
B69Good
○Estimated from fit model
Cerebras-GPT 13B needs ~22.7 GB VRAM. RTX 3090 Ti 24GB has 24.0 GB. With Q5_K_M quantization, expect ~78 tok/s.
Runtime: OllamaCapacity: TightBandwidth: HighStack: BasicBottleneck: Balanced
Share:
Operating mode
Choose the run profile you care about
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
Q5_K_M (High quality) — 22.7 GB, 78.0 tok/s, Tight fit
22.7 GB required24.0 GB available
95% VRAM used
Fit status
Tight fit
Decode
78.0 tok/s
TTFT
2482 ms
Safe context
18K
Memory
22.7 GB / 24.0 GB
Memory breakdown
Weights9.4 GB
KV Cache9.8 GB
Runtime1.2 GB
Headroom2.4 GB
See how fast it feels
See how fast it feelsCerebras-GPT 13B on RTX 3090 Ti 24GB
1st promptCold start — includes initialization
>Solve: If a train travels at 60 mph for 2.5 hours, how far does it go?
Let me work through this step by step.
Given information:
- Speed = 60 mph (miles per hour)
- Time = 2.5 hours
Formula: Distance = Speed x Time
Calculation:
Distance = 60 mph x 2.5 h
= 150 miles
The train travels 150 miles.
Double-check: at 60 mph the train covers 60 miles each hour. In half an hour it covers 30 miles. So in 2.5 hours: 60 + 60 + 30 = 150 miles. Confirmed.
2nd promptWarm — model ready, no init overhead
>What if the train stops for 20 minutes halfway?
The stop doesn't change the total distance covered while moving, but it affects total trip time.
Breakdown:
- First half: 1.25 hours at 60 mph = 75 miles
- Stop: 20 minutes (no distance)
- Second half: 1.25 hours at 60 mph = 75 miles
Total distance: 150 miles (same)
Total time: 2.5 h + 0.33 h = 2.83 hours
Average speed: 150 / 2.83 = 53 mph
The distance stays the same but average speed drops to 53 mph because of the stop.
Estimated: 78.0 tok/s decode · 2.5s TTFT (warm) · 195 tok/s prefill
What limits this setup
This setup is broadly balanced for this model.
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.
Best improvement path
Buy headroom, not only minimum fit
A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
Performance by workload
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | A | Runs well | 78.0 tok/s | 1354 ms | 18K |
| Coding | B | Tight fit | 78.0 tok/s | 2482 ms | 18K |
| Agentic Coding | F | Too heavy | 30.9 tok/s | 9109 ms | 18K |
| Reasoning | B | Tight fit | 78.0 tok/s | 2934 ms | 18K |
| RAG | F | Too heavy | 30.9 tok/s | 11386 ms | 18K |
Quantization options
How Cerebras-GPT 13B (13B params) fits at each quantization level on RTX 3090 Ti 24GB (24.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 5.1 GB | Low | B62 |
Q3_K_S | 3 | 6.4 GB | Low | B62 |
NVFP4 | 4 | 7.3 GB | Medium | B63 |
Q4_K_M | 4 | 7.9 GB | Medium | B63 |
Q5_K_M | 5 | 9.4 GB | High | B64 |
Q6_K | 6 | 10.7 GB | High | B65 |
Q8_0Best for your GPU | 8 | 13.9 GB | Very High | B66 |
F16 | 16 | 26.7 GB | Maximum | F0 |
Get started
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
Hardware that runs Cerebras-GPT 13B well
32 GB VRAM (+8)
A
Adds memory headroom for longer context windows and future model growth.82 tok/s decode
Adds memory headroom for longer context windows and future model growth.
~$2,499 MSRP
32 GB VRAM (+8)
A
Adds memory headroom for longer context windows and future model growth.50.2 tok/s decode
Adds memory headroom for longer context windows and future model growth.
~$4,000 MSRP