Can internlm JanusCoder 14B run on RTX 5000 Ada 32GB?

YES — Runs Great

C50Usable

Estimated from fit model

internlm JanusCoder 14B needs ~14.6 GB VRAM. RTX 5000 Ada 32GB has 32.0 GB. With Q4_K_M quantization, expect ~54 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) — 14.6 GB, 54.0 tok/s, Runs well

14.6 GB required32.0 GB available

46% VRAM used

Fit status

Runs well

Decode

54.0 tok/s

TTFT

3588 ms

Safe context

186K

Memory

14.6 GB / 32.0 GB

Memory breakdown

Weights8.5 GB

KV Cache1.6 GB

Runtime1.2 GB

Headroom3.2 GB

See how fast it feels

See how fast it feelsinternlm JanusCoder 14B on RTX 5000 Ada 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: 54.0 tok/s decode · 3.6s TTFT (warm) · 135 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	54.0 tok/s	1957 ms	186K
Coding	C	Runs well	54.0 tok/s	3588 ms	186K
Agentic Coding	C	Runs well	54.0 tok/s	5219 ms	186K
Reasoning	C	Runs well	54.0 tok/s	4240 ms	186K
RAG	C	Runs well	54.0 tok/s	6524 ms	186K

Quantization options

How internlm JanusCoder 14B (14B params) fits at each quantization level on RTX 5000 Ada 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 (+979)

Raises estimated decode speed by about 183%.153 tok/s Dekodierung

Raises estimated decode speed by about 183%.

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

ca. $10,000 MSRP

Frequently asked questions

Can RTX 5000 Ada 32GB run internlm JanusCoder 14B?

Yes, RTX 5000 Ada 32GB can run internlm JanusCoder 14B with a C grade (Runs well). Expected decode speed: 54.0 tok/s.

How much VRAM does internlm JanusCoder 14B need?

internlm JanusCoder 14B (14B parameters) requires approximately 14.6 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 RTX 5000 Ada 32GB?

On RTX 5000 Ada 32GB, internlm JanusCoder 14B achieves approximately 54.0 tokens per second decode speed with a time-to-first-token of 3588ms using Q4_K_M quantization.

Can RTX 5000 Ada 32GB run internlm JanusCoder 14B for coding?

For coding workloads, internlm JanusCoder 14B on RTX 5000 Ada 32GB receives a C grade with 54.0 tok/s and 186K context.

What context window can internlm JanusCoder 14B use on RTX 5000 Ada 32GB?

On RTX 5000 Ada 32GB, internlm JanusCoder 14B can safely use up to 186K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for RTX 5000 Ada 32GB See all hardware for internlm JanusCoder 14B

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