Can Cerebras-GPT 13B run on NVIDIA A100 40GB?

YES — Runs Great

A71Great

Estimated from fit model

Cerebras-GPT 13B needs ~24.3 GB VRAM. NVIDIA A100 40GB has 40.0 GB. With Q5_K_M quantization, expect ~142 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: HighStack: 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.

Capabilities:

Select quantization to explore

Q5_K_M (High quality) — 24.3 GB, 142.3 tok/s, Runs well

24.3 GB required40.0 GB available

61% VRAM used

Fit status

Runs well

Decode

142.3 tok/s

TTFT

1360 ms

Safe context

42K

Memory

24.3 GB / 40.0 GB

Memory breakdown

Weights9.4 GB

KV Cache9.8 GB

Runtime1.2 GB

Headroom4.0 GB

See how fast it feels

See how fast it feelsCerebras-GPT 13B on NVIDIA A100 40GB
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: 142.3 tok/s decode · 1.4s TTFT (warm) · 356 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	142.3 tok/s	742 ms	42K
Coding	A	Runs well	142.3 tok/s	1360 ms	42K
Agentic Coding	A	Tight fit	142.3 tok/s	1978 ms	42K
Reasoning	A	Runs well	142.3 tok/s	1607 ms	42K
RAG	A	Tight fit	142.3 tok/s	2473 ms	42K

Quantization options

How Cerebras-GPT 13B (13B params) fits at each quantization level on NVIDIA A100 40GB (40.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	5.1 GB	Low	B59
Q3_K_S	3	6.4 GB	Low	B59
NVFP4	4	7.3 GB	Medium	B59
Q4_K_M	4	7.9 GB	Medium	B59
Q5_K_M	5	9.4 GB	High	B60
Q6_K	6	10.7 GB	High	B60
Q8_0	8	13.9 GB	Very High	B62
F16Best for your GPU	16	26.7 GB	Maximum	B65

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

Your hardware

More models your NVIDIA A100 40GB can run

Model	Params	Grade	Decode
Qwen3-Coder 30B A3B Instruct	30.5B	S	197.5 tok/s
Qwen 3.5 27B	27B	S	85.7 tok/s
Qwen 3.6 27B	27B	S	85.9 tok/s
Qwen 3.6 35B A3B	35B	S	166 tok/s
Qwen3-VL 30B A3B Instruct	30B	S	204.3 tok/s

Frequently asked questions

Can NVIDIA A100 40GB run Cerebras-GPT 13B?

Yes, NVIDIA A100 40GB can run Cerebras-GPT 13B with a A grade (Runs well). Expected decode speed: 142.3 tok/s.

How much VRAM does Cerebras-GPT 13B need?

Cerebras-GPT 13B (13B parameters) requires approximately 24.3 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 A100 40GB?

On NVIDIA A100 40GB, Cerebras-GPT 13B achieves approximately 142.3 tokens per second decode speed with a time-to-first-token of 1360ms using Q5_K_M quantization.

Can NVIDIA A100 40GB run Cerebras-GPT 13B for coding?

For coding workloads, Cerebras-GPT 13B on NVIDIA A100 40GB receives a A grade with 142.3 tok/s and 42K context.

What context window can Cerebras-GPT 13B use on NVIDIA A100 40GB?

On NVIDIA A100 40GB, Cerebras-GPT 13B can safely use up to 42K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.

See all results for NVIDIA A100 40GB See all hardware for Cerebras-GPT 13B

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