Can CodeLlama 7B Instruct run on Quadro RTX 8000 48GB?

YES — Runs Great

A73Great

○Estimated from fit model

CodeLlama 7B Instruct needs ~18.1 GB VRAM. Quadro RTX 8000 48GB has 48.0 GB. With Q4_K_M quantization, expect ~98 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: MediumStack: 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) — 18.1 GB, 98.0 tok/s, Runs well

18.1 GB required48.0 GB available

38% VRAM used

Fit status

Runs well

Decode

98.0 tok/s

TTFT

1976 ms

Safe context

16K

Memory

18.1 GB / 48.0 GB

Memory breakdown

Weights4.3 GB

KV Cache7.8 GB

Runtime1.2 GB

Headroom4.8 GB

See how fast it feels

See how fast it feelsCodeLlama 7B Instruct on Quadro RTX 8000 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: 98.0 tok/s decode · 2.0s TTFT (warm) · 245 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

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

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	A	Runs well	98.0 tok/s	1078 ms	16K
Coding	A	Runs well	98.0 tok/s	1976 ms	16K
Agentic Coding	A	Runs well	98.0 tok/s	2873 ms	16K
Reasoning	A	Runs well	98.0 tok/s	2335 ms	16K
RAG	A	Runs well	98.0 tok/s	3592 ms	16K

Quantization options

How CodeLlama 7B Instruct (7B params) fits at each quantization level on Quadro RTX 8000 48GB (48.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	2.7 GB	Low	B65
Q3_K_S	3	3.4 GB	Low	B65
NVFP4	4	3.9 GB	Medium	B65
Q4_K_M	4	4.3 GB	Medium	B65
Q5_K_M	5	5.0 GB	High	B65
Q6_K	6	5.7 GB	High	B65
Q8_0	8	7.5 GB	Very High	B66
F16Best for your GPU	16	14.3 GB	Maximum	B67

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 Quadro RTX 8000 48GB can run

Model	Params	Grade	Decode
👁 Alibaba Qwen3-Coder 30B A3B Instruct	30.5B	S	70.1 tok/s
👁 Alibaba Qwen 3.5 27B	27B	S	30.4 tok/s
👁 Alibaba Qwen 3.6 27B	27B	S	30.5 tok/s
👁 Alibaba Qwen 3.6 35B A3B	35B	S	58.9 tok/s
👁 Alibaba Qwen3-VL 30B A3B Instruct	30B	S	72.5 tok/s

Frequently asked questions

See all results for Quadro RTX 8000 48GB See all hardware for CodeLlama 7B Instruct

URL: https://willitrunai.com/can-run/codellama-7b-instruct-on-quadro-rtx-8000-48gb

⇱ CodeLlama 7B Instruct on Quadro RTX 8000 48GB? YES