Can Cerebras-GPT 13B run on RTX 4000 Ada Laptop 12GB?

NO — Won't Fit

F0Won't run

Estimated from fit model

Cerebras-GPT 13B needs ~21.5 GB but RTX 4000 Ada Laptop 12GB only has 12.0 GB. Try a smaller quantization or lighter model.

Runtime: OllamaCapacity: No fitBandwidth: LowStack: BasicBottleneck: Memory capacity

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) — 21.5 GB, exceeds 12.0 GB available

21.5 GB required12.0 GB available

179% VRAM needed

9.5 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

7.5 tok/s

TTFT

25699 ms

Safe context

Memory

21.5 GB / 12.0 GB

Offload

40%

Memory breakdown

Weights9.4 GB

KV Cache9.8 GB

Runtime1.2 GB

Headroom1.2 GB

See how fast it feels

With memory offload — actual speed may be lower

See how fast it feelsCerebras-GPT 13B on RTX 4000 Ada Laptop 12GB
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: 7.5 tok/s decode · 25.7s TTFT (warm) · 19 tok/s prefill

What limits this setup

Usable VRAM is the main blocker for this model.

Not enough usable memory

The model needs 21.5 GB, but this setup only exposes 12.0 GB of usable VRAM.

Best improvement path

Add more VRAM headroom

The first useful upgrade is more dedicated VRAM so you can fit the model without shrinking context or dropping to a much lower quant.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	F	Too heavy	12.9 tok/s	8156 ms	4K
Coding	F	Too heavy	7.5 tok/s	25699 ms	4K
Agentic Coding	F	Too heavy	5.2 tok/s	54627 ms	4K
Reasoning	F	Too heavy	7.5 tok/s	30371 ms	4K
RAG	F	Too heavy	5.2 tok/s	68284 ms	4K

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 RTX 4000 Ada Laptop 12GB (12.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	5.1 GB	Low	B68
Q3_K_S	3	6.4 GB	Low	B68
NVFP4	4	7.3 GB	Medium	B68
Q4_K_MBest for your GPU	4	7.9 GB	Medium	B68
Q5_K_M	5	9.4 GB	High	F0
Q6_K	6	10.7 GB	High	F0
Q8_0	8	13.9 GB	Very High	F0
F16	16	26.7 GB	Maximum	F0

Upgrade-Optionen

Hardware, die Cerebras-GPT 13B gut ausführt

RTX 4000 Ada 20GBBestes Preis-Leistungs-Verhältnis

20 GB VRAM (+8)

Makes the model fit on the accelerator instead of staying completely out of reach.18.2 tok/s Dekodierung

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

Raises estimated decode speed by about 143%.

ca. $1,250 MSRP

RTX 3090 24GBGünstige Wahl

24 GB VRAM (+12)936 GB/s (+504)

Makes the model fit on the accelerator instead of staying completely out of reach.71.4 tok/s Dekodierung

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.

ca. $1,499 MSRP

RTX 4090 24GBNVIDIA-Upgrade

24 GB VRAM (+12)1008 GB/s (+576)

Makes the model fit on the accelerator instead of staying completely out of reach.83.5 tok/s Dekodierung

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.

ca. $1,599 MSRP

Frequently asked questions

Can RTX 4000 Ada Laptop 12GB run Cerebras-GPT 13B?

No, Cerebras-GPT 13B requires more memory than RTX 4000 Ada Laptop 12GB provides.

How much VRAM does Cerebras-GPT 13B need?

Cerebras-GPT 13B (13B parameters) requires approximately 21.5 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 RTX 4000 Ada Laptop 12GB?

On RTX 4000 Ada Laptop 12GB, Cerebras-GPT 13B achieves approximately 7.5 tokens per second decode speed with a time-to-first-token of 25699ms using Q5_K_M quantization.

Can RTX 4000 Ada Laptop 12GB run Cerebras-GPT 13B for coding?

For coding workloads, Cerebras-GPT 13B on RTX 4000 Ada Laptop 12GB receives a F grade with 7.5 tok/s and 4K context.

What context window can Cerebras-GPT 13B use on RTX 4000 Ada Laptop 12GB?

On RTX 4000 Ada Laptop 12GB, Cerebras-GPT 13B can safely use up to 4K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.

What should I upgrade first if Cerebras-GPT 13B feels slow on RTX 4000 Ada Laptop 12GB?

Add more VRAM headroom. The first useful upgrade is more dedicated VRAM so you can fit the model without shrinking context or dropping to a much lower quant.

See all results for RTX 4000 Ada Laptop 12GB See all hardware for Cerebras-GPT 13B

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