Can CodeLlama 13B Instruct run on NVIDIA B200 180GB?

YES — Runs Great

A72Great

Estimated from fit model

CodeLlama 13B Instruct needs ~39.3 GB VRAM. NVIDIA B200 180GB has 180.0 GB. With Q4_K_M quantization, expect ~182 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) — 39.3 GB, 182.0 tok/s, Runs well

39.3 GB required180.0 GB available

22% VRAM used

Fit status

Runs well

Decode

182.0 tok/s

TTFT

1064 ms

Safe context

16K

Memory

39.3 GB / 180.0 GB

Memory breakdown

Weights7.9 GB

KV Cache12.2 GB

Runtime1.2 GB

Headroom18.0 GB

See how fast it feels

See how fast it feelsCodeLlama 13B Instruct on NVIDIA B200 180GB
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: 182.0 tok/s decode · 1.1s TTFT (warm) · 455 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	182.0 tok/s	580 ms	16K
Coding	A	Runs well	182.0 tok/s	1064 ms	16K
Agentic Coding	A	Runs well	182.0 tok/s	1547 ms	16K
Reasoning	A	Runs well	182.0 tok/s	1257 ms	16K
RAG	A	Runs well	182.0 tok/s	1934 ms	16K

Quantization options

How CodeLlama 13B Instruct (13B params) fits at each quantization level on NVIDIA B200 180GB (180.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	5.1 GB	Low	B62
Q3_K_S	3	6.4 GB	Low	B62
NVFP4	4	7.3 GB	Medium	B62
Q4_K_M	4	7.9 GB	Medium	B62
Q5_K_M	5	9.4 GB	High	B62
Q6_K	6	10.7 GB	High	B62
Q8_0	8	13.9 GB	Very High	B62
F16Best for your GPU	16	26.7 GB	Maximum	B63

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 B200 180GB can run

Model	Params	Grade	Decode
Devstral 2 123B Instruct	123B	S	97.4 tok/s
Qwen3-Coder 30B A3B Instruct	30.5B	S	1016.1 tok/s
Qwen 3.5 27B	27B	S	378 tok/s
Qwen 3.6 27B	27B	S	378 tok/s
Qwen 3.5 122B A10B	122B	S	270.2 tok/s

Frequently asked questions

Can NVIDIA B200 180GB run CodeLlama 13B Instruct?

Yes, NVIDIA B200 180GB can run CodeLlama 13B Instruct with a A grade (Runs well). Expected decode speed: 182.0 tok/s.

How much VRAM does CodeLlama 13B Instruct need?

CodeLlama 13B Instruct (13B parameters) requires approximately 39.3 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 B200 180GB?

On NVIDIA B200 180GB, CodeLlama 13B Instruct achieves approximately 182.0 tokens per second decode speed with a time-to-first-token of 1064ms using Q4_K_M quantization.

Can NVIDIA B200 180GB run CodeLlama 13B Instruct for coding?

For coding workloads, CodeLlama 13B Instruct on NVIDIA B200 180GB receives a A grade with 182.0 tok/s and 16K context.

What context window can CodeLlama 13B Instruct use on NVIDIA B200 180GB?

On NVIDIA B200 180GB, 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 B200 180GB See all hardware for CodeLlama 13B Instruct

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