Can Cerebras-GPT 13B run on NVIDIA A100 80GB?

YES — Runs Great

B66Good

Estimated from fit model

Cerebras-GPT 13B needs ~28.3 GB VRAM. NVIDIA A100 80GB has 80.0 GB. With Q5_K_M quantization, expect ~182 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) — 28.3 GB, 182.0 tok/s, Runs well

28.3 GB required80.0 GB available

35% VRAM used

Fit status

Runs well

Decode

182.0 tok/s

TTFT

1064 ms

Safe context

101K

Memory

28.3 GB / 80.0 GB

Memory breakdown

Weights9.4 GB

KV Cache9.8 GB

Runtime1.2 GB

Headroom8.0 GB

See how fast it feels

See how fast it feelsCerebras-GPT 13B on NVIDIA A100 80GB
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: 182.0 tok/s decode · 1.1s TTFT (warm) · 455 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	182.0 tok/s	580 ms	101K
Coding	B	Runs well	182.0 tok/s	1064 ms	101K
Agentic Coding	B	Runs well	182.0 tok/s	1547 ms	101K
Reasoning	B	Runs well	182.0 tok/s	1257 ms	101K
RAG	B	Runs well	182.0 tok/s	1934 ms	101K

Inference speed

Cerebras-GPT 13B inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for Cerebras-GPT 13B at Q5_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~131 tok/s. Speed is memory-bandwidth bound, so cards that fit the whole model in VRAM run far faster than ones that offload to system RAM.

GPU / Mac	Memory	Quant	Speed (tok/s)	Fits?
RTX 5090 32GB	32 GB	Q5_K_M	130.8	Fits
RTX 4090 24GB	24 GB	Q5_K_M	83.5	Tight
RX 7900 XTX 24GB	24 GB	Q5_K_M	75.3	Tight
RTX 3090 24GB	24 GB	Q5_K_M	71.4	Tight
Mac Studio M3 Ultra 256GB	256 GB	Q5_K_M	60.7	Fits
Mac Studio M2 Ultra 128GB	128 GB	Q5_K_M	50.6	Fits
Mac Studio M1 Ultra 128GB	128 GB	Q5_K_M	47.9	Fits
MacBook Pro M4 Max 128GB	128 GB	Q5_K_M	33.0	Fits
MacBook Pro M4 Max 64GB	64 GB	Q5_K_M	33.0	Fits
MacBook Pro M3 Max 64GB	64 GB	Q5_K_M	26.2	Fits
RTX 4080 Super 16GB	16 GB	Q5_K_M	25.7	Too big
MacBook Pro M1 Max 64GB	64 GB	Q5_K_M	24.0	Fits
MacBook Pro M4 Pro 48GB	48 GB	Q5_K_M	20.2	Fits
RTX 4070 12GB	12 GB	Q5_K_M	9.0	Too big
RTX 3060 12GB	12 GB	Q5_K_M	5.7	Too big
RTX 4060 8GB	8 GB	Q5_K_M	3.2	Too big

Estimates for single-stream decoding at Q5_K_M; real tokens/sec varies with prompt length, context, batch size, and runtime build. Prompt processing (prefill) is faster than the decode figures shown here.

Quantization options

How Cerebras-GPT 13B (13B params) fits at each quantization level on NVIDIA A100 80GB (80.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	5.1 GB	Low	B56
Q3_K_S	3	6.4 GB	Low	B56
NVFP4	4	7.3 GB	Medium	B56
Q4_K_M	4	7.9 GB	Medium	B56
Q5_K_M	5	9.4 GB	High	B56
Q6_K	6	10.7 GB	High	B56
Q8_0	8	13.9 GB	Very High	B57
F16Best for your GPU	16	26.7 GB	Maximum	B59

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

Frequently asked questions

Can NVIDIA A100 80GB run Cerebras-GPT 13B?

Yes, NVIDIA A100 80GB can run Cerebras-GPT 13B with a B grade (Runs well). Expected decode speed: 182.0 tok/s.

How much VRAM does Cerebras-GPT 13B need?

Cerebras-GPT 13B (13B parameters) requires approximately 28.3 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 80GB?

On NVIDIA A100 80GB, Cerebras-GPT 13B achieves approximately 182.0 tokens per second decode speed with a time-to-first-token of 1064ms using Q5_K_M quantization.

Can NVIDIA A100 80GB run Cerebras-GPT 13B for coding?

For coding workloads, Cerebras-GPT 13B on NVIDIA A100 80GB receives a B grade with 182.0 tok/s and 101K context.

What context window can Cerebras-GPT 13B use on NVIDIA A100 80GB?

On NVIDIA A100 80GB, Cerebras-GPT 13B can safely use up to 101K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.

See all results for NVIDIA A100 80GB See all hardware for Cerebras-GPT 13B

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