Can internlm JanusCoder 14B run on NVIDIA GH200 96GB?

YES — Runs Great

C46Usable

Estimated from fit model

internlm JanusCoder 14B needs ~21.0 GB VRAM. NVIDIA GH200 96GB has 96.0 GB. With Q4_K_M quantization, expect ~196 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: HighStack: 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) — 21.0 GB, 196.0 tok/s, Runs well

21.0 GB required96.0 GB available

22% VRAM used

Fit status

Runs well

Decode

196.0 tok/s

TTFT

988 ms

Safe context

748K

Memory

21.0 GB / 96.0 GB

Memory breakdown

Weights8.5 GB

KV Cache1.6 GB

Runtime1.2 GB

Headroom9.6 GB

See how fast it feels

See how fast it feelsinternlm JanusCoder 14B on NVIDIA GH200 96GB
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	748K
Coding	C	Runs well	196.0 tok/s	988 ms	748K
Agentic Coding	C	Runs well	196.0 tok/s	1437 ms	748K
Reasoning	C	Runs well	196.0 tok/s	1167 ms	748K
RAG	C	Runs well	196.0 tok/s	1796 ms	748K

Quantization options

How internlm JanusCoder 14B (14B params) fits at each quantization level on NVIDIA GH200 96GB (96.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	5.5 GB	Low	D39
Q3_K_S	3	6.9 GB	Low	D39
NVFP4	4	7.8 GB	Medium	D39
Q4_K_M	4	8.5 GB	Medium	D39
Q5_K_M	5	10.1 GB	High	D39
Q6_K	6	11.5 GB	High	D39
Q8_0	8	15.0 GB	Very High	D40
F16Best for your GPU	16	28.7 GB	Maximum	C41

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

Can NVIDIA GH200 96GB run internlm JanusCoder 14B?

Yes, NVIDIA GH200 96GB can run internlm JanusCoder 14B with a C grade (Runs well). Expected decode speed: 196.0 tok/s.

How much VRAM does internlm JanusCoder 14B need?

internlm JanusCoder 14B (14B parameters) requires approximately 21.0 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 NVIDIA GH200 96GB?

On NVIDIA GH200 96GB, internlm JanusCoder 14B achieves approximately 196.0 tokens per second decode speed with a time-to-first-token of 988ms using Q4_K_M quantization.

Can NVIDIA GH200 96GB run internlm JanusCoder 14B for coding?

For coding workloads, internlm JanusCoder 14B on NVIDIA GH200 96GB receives a C grade with 196.0 tok/s and 748K context.

What context window can internlm JanusCoder 14B use on NVIDIA GH200 96GB?

On NVIDIA GH200 96GB, internlm JanusCoder 14B can safely use up to 748K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for NVIDIA GH200 96GB See all hardware for internlm JanusCoder 14B

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