Can CodeLlama 13B Instruct run on RTX PRO 5000 Blackwell 48GB?

YES — Runs Great

A79Great

○Estimated from fit model

CodeLlama 13B Instruct needs ~26.1 GB VRAM. RTX PRO 5000 Blackwell 48GB has 48.0 GB. With Q4_K_M quantization, expect ~142 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) — 26.1 GB, 142.4 tok/s, Runs well

26.1 GB required48.0 GB available

54% VRAM used

Fit status

Runs well

Decode

142.4 tok/s

TTFT

1360 ms

Safe context

16K

Memory

26.1 GB / 48.0 GB

Memory breakdown

Weights7.9 GB

KV Cache12.2 GB

Runtime1.2 GB

Headroom4.8 GB

See how fast it feels

See how fast it feelsCodeLlama 13B Instruct on RTX PRO 5000 Blackwell 48GB
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: 142.4 tok/s decode · 1.4s TTFT (warm) · 356 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	142.4 tok/s	742 ms	16K
Coding	A	Runs well	142.4 tok/s	1360 ms	16K
Agentic Coding	A	Runs well	142.4 tok/s	1978 ms	16K
Reasoning	A	Runs well	142.4 tok/s	1607 ms	16K
RAG	A	Runs well	142.4 tok/s	2473 ms	16K

Quantization options

How CodeLlama 13B Instruct (13B params) fits at each quantization level on RTX PRO 5000 Blackwell 48GB (48.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	5.1 GB	Low	B67
Q3_K_S	3	6.4 GB	Low	B67
NVFP4	4

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 RTX PRO 5000 Blackwell 48GB can run

Model	Params	Grade	Decode	Capabilities
👁 Alibaba Qwen3-Coder 30B A3B Instruct	30.5B	S	170.7 tok/s
👁 Alibaba Qwen 3.5 27B	27B	S	74 tok/s

Frequently asked questions

See all results for RTX PRO 5000 Blackwell 48GB See all hardware for CodeLlama 13B Instruct

URL: https://willitrunai.com/can-run/codellama-13b-instruct-on-rtx-pro-5000-blackwell-48gb

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