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URL: https://willitrunai.com/can-run/codellama-7b-instruct-on-rtx-3060-12gb

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Can CodeLlama 7B Instruct run on RTX 3060 12GB?

BARELY — Tight on Memory

B58Good
○Estimated from fit model

CodeLlama 7B Instruct needs ~14.2 GB VRAM. RTX 3060 12GB has 12.0 GB. With Q4_K_M quantization, expect ~29 tok/s.

Runtime: llama.cppCapacity: OffloadBandwidth: LowStack: StandardBottleneck: Host offload
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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) — 14.2 GB, 24.7 tok/s, Very compromised (needs ~0.7 GB host RAM)
14.2 GB required12.0 GB available
118% VRAM needed

2.2 GB over capacity — needs offload or smaller quantization

Fit status

Very compromised (needs ~0.7 GB host RAM)

Decode

24.7 tok/s

TTFT

7851 ms

Safe context

12K

Memory

14.2 GB / 12.0 GB

Offload

20%

Memory breakdown

Weights4.3 GB
KV Cache7.8 GB
Runtime0.9 GB
Headroom1.2 GB

See how fast it feels

See how fast it feelsCodeLlama 7B Instruct on RTX 3060 12GB
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: 24.7 tok/s decode · 7.9s TTFT (warm) · 62 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 {ram} GB of extra host RAM just for the offloaded portion, before OS and other tools.

Performance by workload

WorkloadGradeFitDecodeTTFTContext
ChatATight fit55.6 tok/s1898 ms12K
CodingBVery compromised29.4 tok/s6595 ms12K
Agentic CodingFToo heavy11.7 tok/s24158 ms12K
ReasoningBVery compromised29.4 tok/s7794 ms12K
RAGFToo heavy11.7 tok/s30198 ms12K

Quantization options

How CodeLlama 7B Instruct (7B params) fits at each quantization level on RTX 3060 12GB (12.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
2.7 GB
LowA72
Q3_K_S
3
3.4 GB
LowA73
NVFP4
4

Get started

Copy-paste commands to run CodeLlama 7B Instruct on your machine.

Run

lms load CodeLlama-7b-Instruct-hf && lms server start

Upgrade options

Hardware that runs CodeLlama 7B Instruct well

👁 NVIDIA
RTX 5060 Ti 16GBBudget pick
16 GB VRAM (+4)448 GB/s (+88)
A
Removes host-memory offload, which is usually the single biggest latency and throughput win.65 tok/s decode

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

Raises estimated decode speed by about 163%.

~$449 MSRP

👁 NVIDIA
RTX 4060 Ti 16GBBest value
16 GB VRAM (+4)
A
Removes host-memory offload, which is usually the single biggest latency and throughput win.45.3 tok/s decode

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

Raises estimated decode speed by about 83%.

~$499 MSRP

👁 NVIDIA
RTX 2000 Ada 16GBNVIDIA upgrade
16 GB VRAM (+4)
A
Removes host-memory offload, which is usually the single biggest latency and throughput win.51.3 tok/s decode

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

Raises estimated decode speed by about 108%.

~$625 MSRP

Frequently asked questions

See all results for RTX 3060 12GBSee all hardware for CodeLlama 7B Instruct
3.9 GB
Medium
A74
Q4_K_M
4
4.3 GB
MediumA74
Q5_K_M
5
5.0 GB
HighA75
Q6_K
6
5.7 GB
HighA76
Q8_0Best for your GPU
8
7.5 GB
Very HighA75
F16
16
14.3 GB
MaximumF0

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.