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URL: https://willitrunai.com/can-run/codellama-7b-instruct-on-rtx-3080-10gb

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Can CodeLlama 7B Instruct run on RTX 3080 10GB?

NO — Won't Fit

F0Won't run
○Estimated from fit model

CodeLlama 7B Instruct needs ~14.3 GB but RTX 3080 10GB only has 10.0 GB. Try a smaller quantization or lighter model.

Runtime: OllamaCapacity: No fitBandwidth: MediumStack: BasicBottleneck: Memory capacity
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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) — 14.3 GB, exceeds 10.0 GB available
14.3 GB required10.0 GB available
143% VRAM needed

4.3 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

47.9 tok/s

TTFT

4041 ms

Safe context

7K

Memory

14.3 GB / 10.0 GB

Offload

30%

Memory breakdown

Weights4.3 GB
KV Cache7.8 GB
Runtime1.2 GB
Headroom1.0 GB

See how fast it feels

With memory offload — actual speed may be lower
See how fast it feelsCodeLlama 7B Instruct on RTX 3080 10GB
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: 47.9 tok/s decode · 4.0s TTFT (warm) · 120 tok/s prefill

What limits this setup

Usable VRAM is the main blocker for this model.

Not enough usable memory

The model needs 14.3 GB, but this setup only exposes 10.0 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

WorkloadGradeFitDecodeTTFTContext
ChatARuns with offload93.9 tok/s1125 ms7K
CodingFToo heavy47.9 tok/s4041 ms7K
Agentic CodingFToo heavy20.3 tok/s13878 ms7K
ReasoningFToo heavy47.9 tok/s4776 ms7K
RAGFToo heavy20.3 tok/s17348 ms7K

Quantization options

How CodeLlama 7B Instruct (7B params) fits at each quantization level on RTX 3080 10GB (10.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
2.7 GB
LowA74
Q3_K_S
3
3.4 GB
LowA75
NVFP4
4

Upgrade options

Hardware that runs CodeLlama 7B Instruct well

👁 NVIDIA
RTX 5060 Ti 16GBBudget pick
16 GB VRAM (+6)
A
Makes the model fit on the accelerator instead of staying completely out of reach.65 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.

~$449 MSRP

👁 NVIDIA
RTX 4060 Ti 16GBBest value
16 GB VRAM (+6)
A
Makes the model fit on the accelerator instead of staying completely out of reach.49.2 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.

~$499 MSRP

👁 NVIDIA
RTX 2000 Ada 16GBNVIDIA upgrade
16 GB VRAM (+6)
A
Makes the model fit on the accelerator instead of staying completely out of reach.51.3 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.

~$625 MSRP

Frequently asked questions

See all results for RTX 3080 10GBSee all hardware for CodeLlama 7B Instruct
3.9 GB
Medium
A76
Q4_K_M
4
4.3 GB
MediumA76
Q5_K_M
5
5.0 GB
HighA76
Q6_KBest for your GPU
6
5.7 GB
HighA76
Q8_0
8
7.5 GB
Very HighF0
F16
16
14.3 GB
MaximumF0

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.