Can Cerebras-GPT 13B run on RX 9060 XT 16GB?

Q: What should I upgrade first if Cerebras-GPT 13B feels slow on RX 9060 XT 16GB?

Remove offload with more accelerator memory. Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.

YES — With Q3_K_S

C48Usable

Estimated from fit model

Cerebras-GPT 13B needs ~18.9 GB VRAM. RX 9060 XT 16GB has 16.0 GB. With Q3_K_S quantization, expect ~16 tok/s.

Runtime: OllamaCapacity: OffloadBandwidth: LowStack: BasicBottleneck: Host offload

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.

Cerebras-GPT 13B at Q5_K_M needs 21.9 GB — too much for RX 9060 XT 16GB (16.0 GB). Runs at Q3_K_S (18.9 GB) with low quality. 2 quantization levels fit.

Capabilities:

Select quantization to explore

Q5_K_M (High quality) — 21.9 GB, exceeds 16.0 GB available

21.9 GB required16.0 GB available

137% VRAM needed

5.9 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

8.5 tok/s

TTFT

22807 ms

Safe context

Memory

21.9 GB / 16.0 GB

Offload

30%

Memory breakdown

Weights9.4 GB

KV Cache9.8 GB

Runtime1.2 GB

Headroom1.6 GB

See how fast it feels

With memory offload — actual speed may be lower

See how fast it feelsCerebras-GPT 13B on RX 9060 XT 16GB
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: 8.5 tok/s decode · 22.8s TTFT (warm) · 21 tok/s prefill

What limits this setup

It fits through host-memory offload, and offload is the main reason performance drops.

CPU or host-memory offload is active

About 20% of the working set spills out of accelerator memory, which usually hurts latency and sustained decode throughput.

Very little memory headroom

You can run the model, but there is not much room left for longer context, bigger batches, extra apps, or future model updates.

Best improvement path

Remove offload with more accelerator memory

Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.

Buy headroom, not only minimum fit

A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.

Increase host RAM if you keep offloading

This setup may need roughly 1.0 GB of extra host RAM just for the offloaded portion, before OS and other tools.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	C	Runs with offload (needs ~0.6 GB host RAM)	14.4 tok/s	7320 ms	6K
Coding	F	Too heavy	8.5 tok/s	22807 ms	6K
Agentic Coding	F	Too heavy	3.9 tok/s	72038 ms	6K
Reasoning	F	Too heavy	8.5 tok/s	26953 ms	6K
RAG	F	Too heavy	3.9 tok/s	90047 ms	6K

Quantization options

How Cerebras-GPT 13B (13B params) fits at each quantization level on RX 9060 XT 16GB (16.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	5.1 GB	Low	B65
Q3_K_S	3	6.4 GB	Low	B66
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

Upgrade options

Hardware that runs Cerebras-GPT 13B well

RX 7900 XT 20GBBest value

20 GB VRAM (+4)800 GB/s (+480)

Makes the model fit on the accelerator instead of staying completely out of reach.31.1 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Raises estimated decode speed by about 266%.

~$899 MSRP

RX 7900 XTX 24GBBudget pick

24 GB VRAM (+8)960 GB/s (+640)

Makes the model fit on the accelerator instead of staying completely out of reach.75.3 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

~$999 MSRP

Radeon AI PRO R9700 32GBAMD upgrade

32 GB VRAM (+16)640 GB/s (+320)

Makes the model fit on the accelerator instead of staying completely out of reach.41.1 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

~$1,899 MSRP

Frequently asked questions

Can RX 9060 XT 16GB run Cerebras-GPT 13B?

Yes, RX 9060 XT 16GB can run Cerebras-GPT 13B at Q3_K_S quantization (Very compromised (needs ~1 GB host RAM)). The recommended Q5_K_M requires 21.9 GB which exceeds available memory, but at Q3_K_S it needs only 18.9 GB. Expected decode speed: 15.5 tok/s.

How much VRAM does Cerebras-GPT 13B need?

Cerebras-GPT 13B (13B parameters) requires approximately 21.9 GB at Q5_K_M quantization. On RX 9060 XT 16GB, it fits at Q3_K_S using 18.9 GB.

What is the best quantization for Cerebras-GPT 13B?

The recommended quantization is Q5_K_M, but on RX 9060 XT 16GB the best fitting quantization is Q3_K_S, which uses 18.9 GB.

What speed will Cerebras-GPT 13B run at on RX 9060 XT 16GB?

On RX 9060 XT 16GB, Cerebras-GPT 13B achieves approximately 15.5 tokens per second decode speed with a time-to-first-token of 12503ms using Q3_K_S quantization.

Can RX 9060 XT 16GB run Cerebras-GPT 13B for coding?

For coding workloads, Cerebras-GPT 13B on RX 9060 XT 16GB receives a F grade with 8.5 tok/s and 6K context.

What context window can Cerebras-GPT 13B use on RX 9060 XT 16GB?

On RX 9060 XT 16GB, Cerebras-GPT 13B can safely use up to 11K tokens of context at Q3_K_S quantization. The model's official context limit is 131K, but available memory constrains the safe maximum.

See all results for RX 9060 XT 16GB See all hardware for Cerebras-GPT 13B

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