NVIDIA A100 80GBコスパ重視80 GB VRAM (+16)2039 GB/s (+1439)
B
Raises estimated decode speed by about 257%.182 tok/sデコード
Raises estimated decode speed by about 257%.
Adds memory headroom for longer context windows and future model growth.
〜$15,000 MSRP
Can Cerebras-GPT 13B run on NVIDIA A16 64GB?
YES — Runs Great
B65Good
Estimated from fit model
Cerebras-GPT 13B needs ~26.7 GB VRAM. NVIDIA A16 64GB has 64.0 GB. With Q5_K_M quantization, expect ~51 tok/s.
Runtime: OllamaCapacity: RoomyBandwidth: 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) — 26.7 GB, 51.0 tok/s, Runs well
26.7 GB required64.0 GB available
42% VRAM used
Fit status
Runs well
Decode
51.0 tok/s
TTFT
3796 ms
Safe context
77K
Memory
26.7 GB / 64.0 GB
Memory breakdown
Weights9.4 GB
KV Cache9.8 GB
Runtime1.2 GB
Headroom6.4 GB
See how fast it feels
See how fast it feelsCerebras-GPT 13B on NVIDIA A16 64GB
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: 51.0 tok/s decode · 3.8s TTFT (warm) · 128 tok/s prefill
What limits this setup
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.
Best improvement path
Performance by workload
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | B | Runs well | 51.0 tok/s | 2071 ms | 77K |
| Coding | B | Runs well | 51.0 tok/s | 3796 ms | 77K |
| Agentic Coding | B | Runs well | 51.0 tok/s | 5522 ms | 77K |
| Reasoning | B | Runs well | 51.0 tok/s | 4486 ms | 77K |
| RAG | B | Runs well | 51.0 tok/s | 6902 ms | 77K |
Quantization options
How Cerebras-GPT 13B (13B params) fits at each quantization level on NVIDIA A16 64GB (64.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 5.1 GB | Low | B57 |
Q3_K_S | 3 | 6.4 GB | Low | B57 |
NVFP4 | 4 | 7.3 GB | Medium | B57 |
Q4_K_M | 4 | 7.9 GB | Medium | B57 |
Q5_K_M | 5 | 9.4 GB | High | B57 |
Q6_K | 6 | 10.7 GB | High | B57 |
Q8_0 | 8 | 13.9 GB | Very High | B58 |
F16Best for your GPU | 16 | 26.7 GB | Maximum | B61 |
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
NVIDIA A100 80GBコスパ重視NVIDIA A800 80GBコスパ最良80 GB VRAM (+16)1935 GB/s (+1335)
B
Raises estimated decode speed by about 223%.164.5 tok/sデコード
Raises estimated decode speed by about 223%.
Adds memory headroom for longer context windows and future model growth.
〜$15,000 MSRP
NVIDIA H800 80GBNVIDIAアップグレード80 GB VRAM (+16)3000 GB/s (+2400)
B
Raises estimated decode speed by about 257%.182 tok/sデコード
Raises estimated decode speed by about 257%.
Adds memory headroom for longer context windows and future model growth.
〜$30,000 MSRP
Frequently asked questions
Can NVIDIA A16 64GB run Cerebras-GPT 13B?
Yes, NVIDIA A16 64GB can run Cerebras-GPT 13B with a B grade (Runs well). Expected decode speed: 51.0 tok/s.
How much VRAM does Cerebras-GPT 13B need?
Cerebras-GPT 13B (13B parameters) requires approximately 26.7 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 NVIDIA A16 64GB?
On NVIDIA A16 64GB, Cerebras-GPT 13B achieves approximately 51.0 tokens per second decode speed with a time-to-first-token of 3796ms using Q5_K_M quantization.
Can NVIDIA A16 64GB run Cerebras-GPT 13B for coding?
For coding workloads, Cerebras-GPT 13B on NVIDIA A16 64GB receives a B grade with 51.0 tok/s and 77K context.
What context window can Cerebras-GPT 13B use on NVIDIA A16 64GB?
On NVIDIA A16 64GB, Cerebras-GPT 13B can safely use up to 77K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.
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