Can CodeLlama 13B Instruct run on AMD Instinct MI300X 192GB?

YES — Runs Great

A71Great

○Estimated from fit model

CodeLlama 13B Instruct needs ~40.2 GB VRAM. AMD Instinct MI300X 192GB has 192.0 GB. With Q4_K_M quantization, expect ~182 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: HighStack: StandardBottleneck: 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) — 40.2 GB, 182.0 tok/s, Runs well

40.2 GB required192.0 GB available

21% VRAM used

Fit status

Runs well

Decode

182.0 tok/s

TTFT

1064 ms

Safe context

16K

Memory

40.2 GB / 192.0 GB

Memory breakdown

Weights7.9 GB

KV Cache12.2 GB

Runtime0.9 GB

Headroom19.2 GB

See how fast it feels

See how fast it feelsCodeLlama 13B Instruct on AMD Instinct MI300X 192GB
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 AMD Instinct MI300X 192GB (192.0 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 AMD Instinct MI300X 192GB can run

Model	Params	Grade	Decode
👁 Mistral Devstral 2 123B Instruct	123B	S	59.9 tok/s
👁 Alibaba Qwen3-Coder 30B A3B Instruct	30.5B	S	625.1 tok/s
👁 Alibaba Qwen 3.5 27B	27B	S	271.1 tok/s
👁 Alibaba Qwen 3.6 27B	27B	S	169 tok/s
👁 Alibaba Qwen 3.5 122B A10B	122B	S	166.2 tok/s

Frequently asked questions

See all results for AMD Instinct MI300X 192GB See all hardware for CodeLlama 13B Instruct

URL: https://willitrunai.com/can-run/codellama-13b-instruct-on-instinct-mi300x-192gb

⇱ CodeLlama 13B Instruct on AMD Instinct MI300X 192GB? YES