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.

Capabilities:

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

Opções de upgrade

Hardware que roda bem Cerebras-GPT 13B

NVIDIA A100 80GBOpção econômica

80 GB VRAM (+16)2039 GB/s (+1439)

Raises estimated decode speed by about 257%.182 tok/s decodificação

Raises estimated decode speed by about 257%.

Adds memory headroom for longer context windows and future model growth.

~$15,000 MSRP

NVIDIA A800 80GBMelhor custo-benefício

80 GB VRAM (+16)1935 GB/s (+1335)

Raises estimated decode speed by about 223%.164.5 tok/s decodificação

Raises estimated decode speed by about 223%.

Adds memory headroom for longer context windows and future model growth.

~$15,000 MSRP

NVIDIA H800 80GBUpgrade NVIDIA

80 GB VRAM (+16)3000 GB/s (+2400)

Raises estimated decode speed by about 257%.182 tok/s decodificação

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.

See all results for NVIDIA A16 64GB See all hardware for Cerebras-GPT 13B

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