Can Cerebras-GPT 13B run on NVIDIA H20 96GB?

YES — Runs Great

B65Good

Estimated from fit model

Cerebras-GPT 13B needs ~29.9 GB VRAM. NVIDIA H20 96GB has 96.0 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) — 29.9 GB, 182.0 tok/s, Runs well

29.9 GB required96.0 GB available

31% VRAM used

Fit status

Runs well

Decode

182.0 tok/s

TTFT

1064 ms

Safe context

124K

Memory

29.9 GB / 96.0 GB

Memory breakdown

Weights9.4 GB

KV Cache9.8 GB

Runtime1.2 GB

Headroom9.6 GB

See how fast it feels

See how fast it feelsCerebras-GPT 13B on NVIDIA H20 96GB
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	124K
Coding	B	Runs well	182.0 tok/s	1064 ms	124K
Agentic Coding	B	Runs well	182.0 tok/s	1547 ms	124K
Reasoning	B	Runs well	182.0 tok/s	1257 ms	124K
RAG	B	Runs well	182.0 tok/s	1934 ms	124K

Quantization options

How Cerebras-GPT 13B (13B params) fits at each quantization level on NVIDIA H20 96GB (96.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	5.1 GB	Low	B55
Q3_K_S	3	6.4 GB	Low	B55
NVFP4	4

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 H20 96GB run Cerebras-GPT 13B?

Yes, NVIDIA H20 96GB 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 29.9 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 H20 96GB?

On NVIDIA H20 96GB, 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 H20 96GB run Cerebras-GPT 13B for coding?

For coding workloads, Cerebras-GPT 13B on NVIDIA H20 96GB receives a B grade with 182.0 tok/s and 124K context.

What context window can Cerebras-GPT 13B use on NVIDIA H20 96GB?

On NVIDIA H20 96GB, Cerebras-GPT 13B can safely use up to 124K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.

See all results for NVIDIA H20 96GB See all hardware for Cerebras-GPT 13B

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