Can internlm JanusCoder 14B run on NVIDIA A2 16GB?

YES — Runs Great

C51Usable

Estimated from fit model

internlm JanusCoder 14B needs ~13.0 GB VRAM. NVIDIA A2 16GB has 16.0 GB. With Q4_K_M quantization, expect ~18 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: Very lowStack: BasicBottleneck: Memory bandwidth

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) — 13.0 GB, 18.3 tok/s, Runs well

13.0 GB required16.0 GB available

81% VRAM used

Fit status

Runs well

Decode

18.3 tok/s

TTFT

10598 ms

Safe context

45K

Memory

13.0 GB / 16.0 GB

Memory breakdown

Weights8.5 GB

KV Cache1.6 GB

Runtime1.2 GB

Headroom1.6 GB

See how fast it feels

See how fast it feelsinternlm JanusCoder 14B on NVIDIA A2 16GB
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: 18.3 tok/s decode · 10.6s TTFT (warm) · 46 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	18.3 tok/s	5781 ms	45K
Coding	C	Runs well	18.3 tok/s	10598 ms	45K
Agentic Coding	C	Tight fit	18.3 tok/s	15416 ms	45K
Reasoning	C	Runs well	18.3 tok/s	12525 ms	45K
RAG	C	Tight fit	18.3 tok/s	19270 ms	45K

Quantization options

How internlm JanusCoder 14B (14B params) fits at each quantization level on NVIDIA A2 16GB (16.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	5.5 GB	Low	C49
Q3_K_S	3	6.9 GB	Low	C50
NVFP4	4	7.8 GB	Medium	C51
Q4_K_M	4	8.5 GB	Medium	C51
Q5_K_M	5	10.1 GB	High	C51
Q6_KBest for your GPU	6	11.5 GB	High	C50
Q8_0	8	15.0 GB	Very High	F0
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

RTX 4000 Ada 20GBGünstige Wahl

20 GB VRAM (+4)360 GB/s (+160)

Raises estimated decode speed by about 80%.32.9 tok/s Dekodierung

Raises estimated decode speed by about 80%.

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

ca. $1,250 MSRP

RTX 3090 24GBBestes Preis-Leistungs-Verhältnis

24 GB VRAM (+8)936 GB/s (+736)

Raises estimated decode speed by about 319%.76.7 tok/s Dekodierung

Raises estimated decode speed by about 319%.

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

ca. $1,499 MSRP

RTX PRO 4000 Blackwell 24GBNVIDIA-Upgrade

24 GB VRAM (+8)672 GB/s (+472)

Raises estimated decode speed by about 261%.66.1 tok/s Dekodierung

Raises estimated decode speed by about 261%.

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

ca. $1,599 MSRP

Frequently asked questions

Can NVIDIA A2 16GB run internlm JanusCoder 14B?

Yes, NVIDIA A2 16GB can run internlm JanusCoder 14B with a C grade (Runs well). Expected decode speed: 18.3 tok/s.

How much VRAM does internlm JanusCoder 14B need?

internlm JanusCoder 14B (14B parameters) requires approximately 13.0 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 A2 16GB?

On NVIDIA A2 16GB, internlm JanusCoder 14B achieves approximately 18.3 tokens per second decode speed with a time-to-first-token of 10598ms using Q4_K_M quantization.

Can NVIDIA A2 16GB run internlm JanusCoder 14B for coding?

For coding workloads, internlm JanusCoder 14B on NVIDIA A2 16GB receives a C grade with 18.3 tok/s and 45K context.

What context window can internlm JanusCoder 14B use on NVIDIA A2 16GB?

On NVIDIA A2 16GB, internlm JanusCoder 14B can safely use up to 45K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for NVIDIA A2 16GB See all hardware for internlm JanusCoder 14B

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