Can Cerebras-GPT 13B run on GTX 1070 8GB?

NO — Won't Fit

F0Won't run

○Estimated from fit model

Cerebras-GPT 13B needs ~21.1 GB but GTX 1070 8GB only has 8.0 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) — 21.1 GB, exceeds 8.0 GB available

21.1 GB required8.0 GB available

264% VRAM needed

13.1 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

2.5 tok/s

TTFT

78415 ms

Safe context

Memory

21.1 GB / 8.0 GB

Offload

60%

Memory breakdown

Weights9.4 GB

KV Cache9.8 GB

Runtime1.2 GB

Headroom0.8 GB

See how fast it feels

With memory offload — actual speed may be lower

See how fast it feelsCerebras-GPT 13B on GTX 1070 8GB
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: 2.5 tok/s decode · 78.4s TTFT (warm) · 6 tok/s prefill

What limits this setup

Usable VRAM is the main blocker for this model.

Not enough usable memory

The model needs 21.1 GB, but this setup only exposes 8.0 GB of usable VRAM.

Older PCIe generation

PCIe 3.0 is workable, but it compounds the penalty when you offload heavily or try to scale across multiple cards.

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	2.5 tok/s	41658 ms	4K
Coding	F	Too heavy	2.5 tok/s	78415 ms	4K
Agentic Coding	F	Too heavy	2.5 tok/s	114058 ms	4K
Reasoning	F	Too heavy	2.5 tok/s	92672 ms	4K
RAG	F	Too heavy	2.5 tok/s	142572 ms	4K

Quantization options

How Cerebras-GPT 13B (13B params) fits at each quantization level on GTX 1070 8GB (8.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_KBest for your GPU	2	5.1 GB	Low	B69
Q3_K_S	3	6.4 GB	Low	F0
NVFP4	4	7.3 GB	Medium	F0
Q4_K_M	4	7.9 GB	Medium	F0
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 options

Hardware that runs Cerebras-GPT 13B well

👁 NVIDIA
RTX 4000 Ada 20GBBest value

20 GB VRAM (+12)360 GB/s (+104)

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

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

Raises estimated decode speed by about 628%.

~$1,250 MSRP

👁 NVIDIA
RTX 3090 24GBBudget pick

24 GB VRAM (+16)936 GB/s (+680)

Makes the model fit on the accelerator instead of staying completely out of reach.71.4 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.

~$1,499 MSRP

👁 NVIDIA
RTX 4090 24GBNVIDIA upgrade

24 GB VRAM (+16)1008 GB/s (+752)

Makes the model fit on the accelerator instead of staying completely out of reach.83.5 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.

~$1,599 MSRP

Frequently asked questions

See all results for GTX 1070 8GB See all hardware for Cerebras-GPT 13B

URL: https://willitrunai.com/can-run/cerebras-gpt-13b-on-gtx-1070-8gb

⇱ Cerebras-GPT 13B on GTX 1070 8GB? No — Alternatives

Can Cerebras-GPT 13B run on GTX 1070 8GB?

NO — Won't Fit

Choose the run profile you care about

Memory breakdown

See how fast it feels

What limits this setup

Best improvement path

Performance by workload

Quantization options

Hardware that runs Cerebras-GPT 13B well

Frequently asked questions