RTX 5090 32GBコスパ重視32 GB VRAM (+8)1792 GB/s (+1024)
A
Raises estimated decode speed by about 100%.130.8 tok/sデコード
Raises estimated decode speed by about 100%.
Adds memory headroom for longer context windows and future model growth.
〜$1,999 MSRP
Can Cerebras-GPT 13B run on RTX A5500 24GB?
YES — Tight Fit
B69Good
Estimated from fit model
Cerebras-GPT 13B needs ~22.7 GB VRAM. RTX A5500 24GB has 24.0 GB. With Q5_K_M quantization, expect ~65 tok/s.
Runtime: OllamaCapacity: TightBandwidth: MediumStack: BasicBottleneck: Balanced
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, 65.3 tok/s, Tight fit
22.7 GB required24.0 GB available
95% VRAM used
Fit status
Tight fit
Decode
65.3 tok/s
TTFT
2966 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 A5500 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: 65.3 tok/s decode · 3.0s TTFT (warm) · 163 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 | 65.3 tok/s | 1618 ms | 18K |
| Coding | B | Tight fit | 65.3 tok/s | 2966 ms | 18K |
| Agentic Coding | F | Too heavy | 25.9 tok/s | 10882 ms | 18K |
| Reasoning | B | Tight fit | 65.3 tok/s | 3505 ms | 18K |
| RAG | F | Too heavy | 25.9 tok/s | 13603 ms | 18K |
Quantization options
How Cerebras-GPT 13B (13B params) fits at each quantization level on RTX A5500 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 99アップグレードオプション
Cerebras-GPT 13Bを快適に動かすハードウェア
RTX 5090 32GBコスパ重視RTX PRO 4500 Blackwell 32GBコスパ最良32 GB VRAM (+8)896 GB/s (+128)
A
Raises estimated decode speed by about 26%.82 tok/sデコード
Raises estimated decode speed by about 26%.
Adds memory headroom for longer context windows and future model growth.
〜$2,499 MSRP
RTX 5000 Ada 32GBNVIDIAアップグレード32 GB VRAM (+8)
A
Adds memory headroom for longer context windows and future model growth.50.2 tok/sデコード
Adds memory headroom for longer context windows and future model growth.
〜$4,000 MSRP
Frequently asked questions
Can RTX A5500 24GB run Cerebras-GPT 13B?
Yes, RTX A5500 24GB can run Cerebras-GPT 13B with a B grade (Tight fit). Expected decode speed: 65.3 tok/s.
How much VRAM does Cerebras-GPT 13B need?
Cerebras-GPT 13B (13B parameters) requires approximately 22.7 GB of memory with Q5_K_M quantization.
What is the best quantization for Cerebras-GPT 13B?
The recommended quantization for Cerebras-GPT 13B is Q5_K_M, which balances quality and memory efficiency.
What speed will Cerebras-GPT 13B run at on RTX A5500 24GB?
On RTX A5500 24GB, Cerebras-GPT 13B achieves approximately 65.3 tokens per second decode speed with a time-to-first-token of 2966ms using Q5_K_M quantization.
Can RTX A5500 24GB run Cerebras-GPT 13B for coding?
For coding workloads, Cerebras-GPT 13B on RTX A5500 24GB receives a B grade with 65.3 tok/s and 18K context.
What context window can Cerebras-GPT 13B use on RTX A5500 24GB?
On RTX A5500 24GB, Cerebras-GPT 13B can safely use up to 18K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.
What should I upgrade first if Cerebras-GPT 13B feels slow on RTX A5500 24GB?
Buy headroom, not only minimum fit. A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
Embed this result▼
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/cerebras-gpt-13b-on-rtx-a5500-24gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview: