Can internlm JanusCoder 14B run on Radeon AI PRO R9700 32GB?

YES — Runs Great

C49Usable

Estimated from fit model

internlm JanusCoder 14B needs ~14.3 GB VRAM. Radeon AI PRO R9700 32GB has 32.0 GB. With Q4_K_M quantization, expect ~44 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: MediumStack: 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, 44.2 tok/s, Runs well

14.3 GB required32.0 GB available

45% VRAM used

Fit status

Runs well

Decode

44.2 tok/s

TTFT

4379 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 Radeon AI PRO R9700 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: 44.2 tok/s decode · 4.4s TTFT (warm) · 111 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	44.2 tok/s	2388 ms	189K
Coding	C	Runs well	44.2 tok/s	4379 ms	189K
Agentic Coding	C	Runs well	44.2 tok/s	6369 ms	189K
Reasoning	C	Runs well	44.2 tok/s	5175 ms	189K
RAG	C	Runs well	44.2 tok/s	7961 ms	189K

Quantization options

How internlm JanusCoder 14B (14B params) fits at each quantization level on Radeon AI PRO R9700 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

Upgrade-Optionen

Hardware, die internlm JanusCoder 14B gut ausführt

NVIDIA A100 40GBGünstige Wahl

40 GB VRAM (+8)1555 GB/s (+915)

Raises estimated decode speed by about 246%.153 tok/s Dekodierung

Raises estimated decode speed by about 246%.

Adds memory headroom for longer context windows and future model growth.

ca. $10,000 MSRP

Frequently asked questions

Can Radeon AI PRO R9700 32GB run internlm JanusCoder 14B?

Yes, Radeon AI PRO R9700 32GB can run internlm JanusCoder 14B with a C grade (Runs well). Expected decode speed: 44.2 tok/s.

How much VRAM does internlm JanusCoder 14B need?

internlm JanusCoder 14B (14B parameters) requires approximately 14.3 GB of memory with Q4_K_M quantization.

What is the best quantization for internlm JanusCoder 14B?

The recommended quantization for internlm JanusCoder 14B is Q4_K_M, which balances quality and memory efficiency.

What speed will internlm JanusCoder 14B run at on Radeon AI PRO R9700 32GB?

On Radeon AI PRO R9700 32GB, internlm JanusCoder 14B achieves approximately 44.2 tokens per second decode speed with a time-to-first-token of 4379ms using Q4_K_M quantization.

Can Radeon AI PRO R9700 32GB run internlm JanusCoder 14B for coding?

For coding workloads, internlm JanusCoder 14B on Radeon AI PRO R9700 32GB receives a C grade with 44.2 tok/s and 189K context.

What context window can internlm JanusCoder 14B use on Radeon AI PRO R9700 32GB?

On Radeon AI PRO R9700 32GB, internlm JanusCoder 14B can safely use up to 189K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for Radeon AI PRO R9700 32GB See all hardware for internlm JanusCoder 14B

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