Can Cerebras-GPT 13B run on NVIDIA V100 32GB?

YES — Runs Great

A72Great

Estimated from fit model

Cerebras-GPT 13B needs ~23.5 GB VRAM. NVIDIA V100 32GB has 32.0 GB. With Q5_K_M quantization, expect ~66 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) — 23.5 GB, 65.7 tok/s, Runs well

23.5 GB required32.0 GB available

73% VRAM used

Fit status

Runs well

Decode

65.7 tok/s

TTFT

2946 ms

Safe context

30K

Memory

23.5 GB / 32.0 GB

Memory breakdown

Weights9.4 GB

KV Cache9.8 GB

Runtime1.2 GB

Headroom3.2 GB

See how fast it feels

See how fast it feelsCerebras-GPT 13B on NVIDIA V100 32GB
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: 65.7 tok/s decode · 2.9s TTFT (warm) · 164 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	65.7 tok/s	1607 ms	30K
Coding	A	Runs well	65.7 tok/s	2946 ms	30K
Agentic Coding	B	Runs with offload (needs ~0.4 GB host RAM)	52.5 tok/s	5363 ms	30K
Reasoning	A	Runs well	65.7 tok/s	3482 ms	30K
RAG	B	Runs with offload (needs ~0.4 GB host RAM)	52.5 tok/s	6704 ms	30K

Quantization options

How Cerebras-GPT 13B (13B params) fits at each quantization level on NVIDIA V100 32GB (32.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	5.1 GB	Low	B60
Q3_K_S	3	6.4 GB	Low	B60
NVFP4	4	7.3 GB	Medium	B61
Q4_K_M	4	7.9 GB	Medium	B61
Q5_K_M	5	9.4 GB	High	B62
Q6_K	6	10.7 GB	High	B62
Q8_0	8	13.9 GB	Very High	B64
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 V100 32GB can run

Model	Params	Grade	Decode
Qwen3-Coder 30B A3B Instruct	30.5B	S	91.2 tok/s
Qwen 3.5 27B	27B	S	39.5 tok/s
Qwen 3.6 27B	27B	S	39.7 tok/s
Qwen 3.6 35B A3B	35B	S	76.6 tok/s
Qwen3-VL 30B A3B Instruct	30B	S	94.3 tok/s

Frequently asked questions

Can NVIDIA V100 32GB run Cerebras-GPT 13B?

Yes, NVIDIA V100 32GB can run Cerebras-GPT 13B with a A grade (Runs well). Expected decode speed: 65.7 tok/s.

How much VRAM does Cerebras-GPT 13B need?

Cerebras-GPT 13B (13B parameters) requires approximately 23.5 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 V100 32GB?

On NVIDIA V100 32GB, Cerebras-GPT 13B achieves approximately 65.7 tokens per second decode speed with a time-to-first-token of 2946ms using Q5_K_M quantization.

Can NVIDIA V100 32GB run Cerebras-GPT 13B for coding?

For coding workloads, Cerebras-GPT 13B on NVIDIA V100 32GB receives a A grade with 65.7 tok/s and 30K context.

What context window can Cerebras-GPT 13B use on NVIDIA V100 32GB?

On NVIDIA V100 32GB, Cerebras-GPT 13B can safely use up to 30K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.

See all results for NVIDIA V100 32GB See all hardware for Cerebras-GPT 13B

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