Can CodeLlama 13B Instruct run on NVIDIA A100 40GB?

YES — Runs Great

A81Great

Estimated from fit model

CodeLlama 13B Instruct needs ~25.3 GB VRAM. NVIDIA A100 40GB has 40.0 GB. With Q4_K_M quantization, expect ~165 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: HighStack: 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) — 25.3 GB, 164.7 tok/s, Runs well

25.3 GB required40.0 GB available

63% VRAM used

Fit status

Runs well

Decode

164.7 tok/s

TTFT

1175 ms

Safe context

16K

Memory

25.3 GB / 40.0 GB

Memory breakdown

Weights7.9 GB

KV Cache12.2 GB

Runtime1.2 GB

Headroom4.0 GB

See how fast it feels

See how fast it feelsCodeLlama 13B Instruct on NVIDIA A100 40GB
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: 164.7 tok/s decode · 1.2s TTFT (warm) · 412 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

No major red flags

This recommendation has enough memory headroom and acceptable estimated speed for the selected workload.

Best improvement path

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	A	Runs well	164.7 tok/s	641 ms	16K
Coding	A	Runs well	164.7 tok/s	1175 ms	16K
Agentic Coding	A	Tight fit	164.7 tok/s	1710 ms	16K
Reasoning	A	Runs well	164.7 tok/s	1389 ms	16K
RAG	A	Tight fit	164.7 tok/s	2137 ms	16K

Quantization options

How CodeLlama 13B Instruct (13B params) fits at each quantization level on NVIDIA A100 40GB (40.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	5.1 GB	Low	B68
Q3_K_S	3	6.4 GB	Low	B68
NVFP4	4	7.3 GB	Medium	B68
Q4_K_M	4	7.9 GB	Medium	B68
Q5_K_M	5	9.4 GB	High	B69
Q6_K	6	10.7 GB	High	B69
Q8_0	8	13.9 GB	Very High	A71
F16Best for your GPU	16	26.7 GB	Maximum	A74

Get started

Copy-paste commands to run CodeLlama 13B Instruct on your machine.

Run

lms load CodeLlama-13b-Instruct-hf && lms server start

Your hardware

More models your NVIDIA A100 40GB can run

Model	Params	Grade	Decode
Qwen3-Coder 30B A3B Instruct	30.5B	S	197.5 tok/s
Qwen 3.5 27B	27B	S	85.7 tok/s
Qwen 3.6 27B	27B	S	85.9 tok/s
Qwen 3.6 35B A3B	35B	S	166 tok/s
Qwen3-VL 30B A3B Instruct	30B	S	204.3 tok/s

Frequently asked questions

Can NVIDIA A100 40GB run CodeLlama 13B Instruct?

Yes, NVIDIA A100 40GB can run CodeLlama 13B Instruct with a A grade (Runs well). Expected decode speed: 164.7 tok/s.

How much VRAM does CodeLlama 13B Instruct need?

CodeLlama 13B Instruct (13B parameters) requires approximately 25.3 GB of memory with Q4_K_M quantization.

What is the best quantization for CodeLlama 13B Instruct?

The recommended quantization for CodeLlama 13B Instruct is Q4_K_M, which balances quality and memory efficiency.

What speed will CodeLlama 13B Instruct run at on NVIDIA A100 40GB?

On NVIDIA A100 40GB, CodeLlama 13B Instruct achieves approximately 164.7 tokens per second decode speed with a time-to-first-token of 1175ms using Q4_K_M quantization.

Can NVIDIA A100 40GB run CodeLlama 13B Instruct for coding?

For coding workloads, CodeLlama 13B Instruct on NVIDIA A100 40GB receives a A grade with 164.7 tok/s and 16K context.

What context window can CodeLlama 13B Instruct use on NVIDIA A100 40GB?

On NVIDIA A100 40GB, CodeLlama 13B 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.

See all results for NVIDIA A100 40GB See all hardware for CodeLlama 13B Instruct

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