Can CodeLlama 13B Instruct run on GTX 1070 8GB?

NO — Won't Fit

F0Won't run

○Estimated from fit model

CodeLlama 13B Instruct needs ~22.1 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

Q4_K_M (Medium quality) — 22.1 GB, exceeds 8.0 GB available

22.1 GB required8.0 GB available

276% VRAM needed

14.1 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

2.9 tok/s

TTFT

67763 ms

Safe context

Memory

22.1 GB / 8.0 GB

Offload

60%

Memory breakdown

Weights7.9 GB

KV Cache12.2 GB

Runtime1.2 GB

Headroom0.8 GB

See how fast it feels

With memory offload — actual speed may be lower

See how fast it feelsCodeLlama 13B Instruct on GTX 1070 8GB
1st promptCold start — includes initialization
>Write a Python function to sort a list
Here's a Python function that sorts a list using quicksort:

def quicksort(arr):
 if len(arr) <= 1:
 return arr
 pivot = arr[len(arr) // 2]
 left = [x for x in arr if x < pivot]
 middle = [x for x in arr if x == pivot]
 right = [x for x in arr if x > pivot]
 return quicksort(left) + middle + quicksort(right)

# Example usage
numbers = [3, 6, 8, 10, 1, 2, 1]
print(quicksort(numbers)) # [1, 1, 2, 3, 6, 8, 10]

The algorithm picks a pivot, partitions the list into smaller, equal, and larger elements, then recursively sorts each partition and concatenates the results.
2nd promptWarm — model ready, no init overhead
>Now add type hints and a docstring
def quicksort(arr: list[int]) -> list[int]:
 """Sort a list using the quicksort algorithm.

 Args:
 arr: List of integers to sort.

 Returns:
 A new sorted list.
 """
 if len(arr) <= 1:
 return arr
 pivot = arr[len(arr) // 2]
 left = [x for x in arr if x < pivot]
 middle = [x for x in arr if x == pivot]
 right = [x for x in arr if x > pivot]
 return quicksort(left) + middle + quicksort(right)

Added type hints for the input and return type, plus a Google-style docstring.
Estimated: 2.9 tok/s decode · 67.8s TTFT (warm) · 7 tok/s prefill

What limits this setup

Usable VRAM is the main blocker for this model.

Not enough usable memory

The model needs 22.1 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	3.0 tok/s	34989 ms	4K
Coding	F	Too heavy	2.9 tok/s	67763 ms	4K
Agentic Coding	F	Too heavy	2.9 tok/s	98565 ms	4K
Reasoning	F	Too heavy	2.9 tok/s	80084 ms	4K
RAG	F	Too heavy	2.9 tok/s	123206 ms	4K

Quantization options

How CodeLlama 13B Instruct (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	A78
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 CodeLlama 13B Instruct 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.19.2 tok/s decode

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

Raises estimated decode speed by about 562%.

~$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.82.6 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.96.6 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 CodeLlama 13B Instruct

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

⇱ CodeLlama 13B Instruct on GTX 1070 8GB? No — Alternatives

Can CodeLlama 13B Instruct 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 CodeLlama 13B Instruct well

Frequently asked questions