Can CodeLlama 7B Instruct run on RTX 4070 Super 12GB?

Q: What should I upgrade first if CodeLlama 7B Instruct feels slow on RTX 4070 Super 12GB?

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.

BARELY — Tight on Memory

B59Good

Estimated from fit model

CodeLlama 7B Instruct needs ~14.2 GB VRAM. RTX 4070 Super 12GB has 12.0 GB. With Q4_K_M quantization, expect ~44 tok/s.

Runtime: llama.cppCapacity: OffloadBandwidth: MediumStack: StandardBottleneck: Host offload

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, 44.1 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

44.1 tok/s

TTFT

4389 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 4070 Super 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: 44.1 tok/s decode · 4.4s TTFT (warm) · 110 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 0.7 GB of extra host RAM just for the offloaded portion, before OS and other tools.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	A	Tight fit	83.6 tok/s	1263 ms	12K
Coding	B	Very compromised (needs ~0.7 GB host RAM)	44.1 tok/s	4389 ms	12K
Agentic Coding	F	Too heavy	17.5 tok/s	16077 ms	12K
Reasoning	B	Very compromised (needs ~0.7 GB host RAM)	44.1 tok/s	5187 ms	12K
RAG	F	Too heavy	17.5 tok/s	20096 ms

Quantization options

How CodeLlama 7B Instruct (7B params) fits at each quantization level on RTX 4070 Super 12GB (12.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	2.7 GB	Low	A72
Q3_K_S	3	3.4 GB	Low	A73
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

RTX 5060 Ti 16GBBudget pick

16 GB VRAM (+4)

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 47%.

~$449 MSRP

RTX 4060 Ti 16GBBest value

16 GB VRAM (+4)

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.

Adds memory headroom for longer context windows and future model growth.

~$499 MSRP

RTX 2000 Ada 16GBNVIDIA upgrade

16 GB VRAM (+4)

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.

Adds memory headroom for longer context windows and future model growth.

~$625 MSRP

Frequently asked questions

Can RTX 4070 Super 12GB run CodeLlama 7B Instruct?

Yes, RTX 4070 Super 12GB can run CodeLlama 7B Instruct with a B grade (Very compromised (needs ~0.7 GB host RAM)). Expected decode speed: 44.1 tok/s.

How much VRAM does CodeLlama 7B Instruct need?

CodeLlama 7B Instruct (7B parameters) requires approximately 14.2 GB of memory with Q4_K_M quantization.

What is the best quantization for CodeLlama 7B Instruct?

The recommended quantization for CodeLlama 7B Instruct is Q4_K_M, which balances quality and memory efficiency.

What speed will CodeLlama 7B Instruct run at on RTX 4070 Super 12GB?

On RTX 4070 Super 12GB, CodeLlama 7B Instruct achieves approximately 44.1 tokens per second decode speed with a time-to-first-token of 4389ms using Q4_K_M quantization.

Can RTX 4070 Super 12GB run CodeLlama 7B Instruct for coding?

For coding workloads, CodeLlama 7B Instruct on RTX 4070 Super 12GB receives a B grade with 44.1 tok/s and 12K context.

What context window can CodeLlama 7B Instruct use on RTX 4070 Super 12GB?

On RTX 4070 Super 12GB, CodeLlama 7B Instruct can safely use up to 12K tokens of context. The model's official context limit is 16K, but available memory constrains the safe maximum.

See all results for RTX 4070 Super 12GB See all hardware for CodeLlama 7B Instruct

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