Can CodeLlama 7B Instruct run on NVIDIA T4 16GB?

Q: What should I upgrade first if CodeLlama 7B Instruct feels slow on NVIDIA T4 16GB?

Buy headroom, not only minimum fit. A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.

YES — Tight Fit

A75Great

Estimated from fit model

CodeLlama 7B Instruct needs ~14.9 GB VRAM. NVIDIA T4 16GB has 16.0 GB. With Q4_K_M quantization, expect ~49 tok/s.

Runtime: OllamaCapacity: TightBandwidth: LowStack: BasicBottleneck: Balanced

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) — 14.9 GB, 48.7 tok/s, Tight fit

14.9 GB required16.0 GB available

93% VRAM used

Fit status

Tight fit

Decode

48.7 tok/s

TTFT

3974 ms

Safe context

16K

Memory

14.9 GB / 16.0 GB

Memory breakdown

Weights4.3 GB

KV Cache7.8 GB

Runtime1.2 GB

Headroom1.6 GB

See how fast it feels

See how fast it feelsCodeLlama 7B Instruct on NVIDIA T4 16GB
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: 48.7 tok/s decode · 4.0s TTFT (warm) · 122 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

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.

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

Buy headroom, not only minimum fit

A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	A	Runs well	48.7 tok/s	2168 ms	16K
Coding	A	Tight fit	48.7 tok/s	3974 ms	16K
Agentic Coding	F	Too heavy	16.5 tok/s	17077 ms	16K
Reasoning	A	Tight fit	48.7 tok/s	4697 ms	16K
RAG	F	Too heavy	16.5 tok/s	21346 ms	16K

Quantization options

How CodeLlama 7B Instruct (7B params) fits at each quantization level on NVIDIA T4 16GB (16.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	2.7 GB	Low	A70
Q3_K_S	3	3.4 GB	Low	A71
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

Your hardware

More models your NVIDIA T4 16GB can run

Model	Params	Grade	Decode	Capabilities
Qwen 3.5 9B	9B	S	40.7 tok/s
Qwen 3 14B	14B	S	26.3 tok/s

Frequently asked questions

Can NVIDIA T4 16GB run CodeLlama 7B Instruct?

Yes, NVIDIA T4 16GB can run CodeLlama 7B Instruct with a A grade (Tight fit). Expected decode speed: 48.7 tok/s.

How much VRAM does CodeLlama 7B Instruct need?

CodeLlama 7B Instruct (7B parameters) requires approximately 14.9 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 NVIDIA T4 16GB?

On NVIDIA T4 16GB, CodeLlama 7B Instruct achieves approximately 48.7 tokens per second decode speed with a time-to-first-token of 3974ms using Q4_K_M quantization.

Can NVIDIA T4 16GB run CodeLlama 7B Instruct for coding?

For coding workloads, CodeLlama 7B Instruct on NVIDIA T4 16GB receives a A grade with 48.7 tok/s and 16K context.

What context window can CodeLlama 7B Instruct use on NVIDIA T4 16GB?

On NVIDIA T4 16GB, CodeLlama 7B Instruct can safely use up to 16K tokens of context. The model's official context limit is 16K, but available memory constrains the safe maximum.

What should I upgrade first if CodeLlama 7B Instruct feels slow on NVIDIA T4 16GB?

See all results for NVIDIA T4 16GB See all hardware for CodeLlama 7B Instruct

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