Can internlm JanusCoder 14B run on AMD Instinct MI60 32GB?

YES — Runs Great

C50Usable

○Estimated from fit model

internlm JanusCoder 14B needs ~14.3 GB VRAM. AMD Instinct MI60 32GB has 32.0 GB. With Q4_K_M quantization, expect ~59 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) — 14.3 GB, 58.8 tok/s, Runs well

14.3 GB required32.0 GB available

45% VRAM used

Fit status

Runs well

Decode

58.8 tok/s

TTFT

3295 ms

Safe context

189K

Memory

14.3 GB / 32.0 GB

Memory breakdown

Weights8.5 GB

KV Cache1.6 GB

Runtime0.9 GB

Headroom3.2 GB

See how fast it feels

See how fast it feelsinternlm JanusCoder 14B on AMD Instinct MI60 32GB
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: 58.8 tok/s decode · 3.3s TTFT (warm) · 147 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	58.8 tok/s	1797 ms	189K
Coding	C	Runs well	58.8 tok/s	3295 ms	189K
Agentic Coding	C	Runs well	58.8 tok/s	4793 ms	189K
Reasoning	C	Runs well	58.8 tok/s	3894 ms	189K
RAG	C	Runs well	58.8 tok/s	5991 ms	189K

Quantization options

How internlm JanusCoder 14B (14B params) fits at each quantization level on AMD Instinct MI60 32GB (32.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	5.5 GB	Low	C43
Q3_K_S	3	6.9 GB	Low	C44
NVFP4	4	7.8 GB	Medium	C44
Q4_K_M	4	8.5 GB	Medium	C45
Q5_K_M	5	10.1 GB	High	C45
Q6_K	6	11.5 GB	High	C46
Q8_0Best for your GPU	8	15.0 GB	Very High	C48
F16	16	28.7 GB	Maximum	F0

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 MI60 32GB See all hardware for internlm JanusCoder 14B

URL: https://willitrunai.com/can-run/hf-bartowski--internlm-januscoder-14b-gguf-on-instinct-mi60-32gb

⇱ internlm JanusCoder 14B on AMD Instinct MI60 32GB? YES