Can internlm JanusCoder 14B run on AMD Instinct MI325X 256GB?

YES — Runs Great

C45Usable

○Estimated from fit model

internlm JanusCoder 14B needs ~36.7 GB VRAM. AMD Instinct MI325X 256GB has 256.0 GB. With Q4_K_M quantization, expect ~196 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) — 36.7 GB, 196.0 tok/s, Runs well

36.7 GB required256.0 GB available

14% VRAM used

Fit status

Runs well

Decode

196.0 tok/s

TTFT

988 ms

Safe context

2.2M

Memory

36.7 GB / 256.0 GB

Memory breakdown

Weights8.5 GB

KV Cache1.6 GB

Runtime0.9 GB

Headroom25.6 GB

See how fast it feels

See how fast it feelsinternlm JanusCoder 14B on AMD Instinct MI325X 256GB
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: 196.0 tok/s decode · 988ms TTFT (warm) · 490 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	C	Runs well	196.0 tok/s	539 ms	2.2M
Coding	C	Runs well	196.0 tok/s	988 ms	2.2M
Agentic Coding	C	Runs well	196.0 tok/s	1437 ms	2.2M
Reasoning	C	Runs well	196.0 tok/s	1167 ms	2.2M
RAG	C	Runs well	196.0 tok/s	1796 ms	2.2M

Quantization options

How internlm JanusCoder 14B (14B params) fits at each quantization level on AMD Instinct MI325X 256GB (256.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	5.5 GB	Low	D36
Q3_K_S	3	6.9 GB	Low	D36
NVFP4	4	7.8 GB	Medium	D36
Q4_K_M	4	8.5 GB	Medium	D36
Q5_K_M	5	10.1 GB	High	D36
Q6_K	6	11.5 GB	High	D36
Q8_0	8	15.0 GB	Very High	D36
F16Best for your GPU	16	28.7 GB	Maximum	D37

Get started

Copy-paste commands to run internlm JanusCoder 14B on your machine.

Run

lms load hf-bartowski--internlm-januscoder-14b-gguf && lms server start

Frequently asked questions

See all results for AMD Instinct MI325X 256GB See all hardware for internlm JanusCoder 14B

URL: https://willitrunai.com/can-run/hf-bartowski--internlm-januscoder-14b-gguf-on-instinct-mi325x-256gb

⇱ internlm JanusCoder 14B on AMD Instinct MI325X 256GB? YES