Can internlm JanusCoder 14B run on NVIDIA A16 64GB?

YES — Runs Great

C46Usable

Estimated from fit model

internlm JanusCoder 14B needs ~17.8 GB VRAM. NVIDIA A16 64GB has 64.0 GB. With Q4_K_M quantization, expect ~55 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) — 17.8 GB, 54.8 tok/s, Runs well

17.8 GB required64.0 GB available

28% VRAM used

Fit status

Runs well

Decode

54.8 tok/s

TTFT

3533 ms

Safe context

467K

Memory

17.8 GB / 64.0 GB

Memory breakdown

Weights8.5 GB

KV Cache1.6 GB

Runtime1.2 GB

Headroom6.4 GB

See how fast it feels

See how fast it feelsinternlm JanusCoder 14B on NVIDIA A16 64GB
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: 54.8 tok/s decode · 3.5s TTFT (warm) · 137 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	54.8 tok/s	1927 ms	467K
Coding	C	Runs well	54.8 tok/s	3533 ms	467K
Agentic Coding	C	Runs well	54.8 tok/s	5139 ms	467K
Reasoning	C	Runs well	54.8 tok/s	4175 ms	467K
RAG	C	Runs well	54.8 tok/s	6423 ms	467K

Quantization options

How internlm JanusCoder 14B (14B params) fits at each quantization level on NVIDIA A16 64GB (64.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	5.5 GB	Low	C40
Q3_K_S	3	6.9 GB	Low	C40
NVFP4	4

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 options

Hardware that runs internlm JanusCoder 14B well

RTX PRO 6000 Blackwell Workstation Edition 96GBBudget pick

96 GB VRAM (+32)1792 GB/s (+1192)

Raises estimated decode speed by about 222%.176.3 tok/s decode

Raises estimated decode speed by about 222%.

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

~$9,999 MSRP

RTX PRO 6000 Blackwell Server Edition 96GBBest value

96 GB VRAM (+32)1597 GB/s (+997)

Raises estimated decode speed by about 187%.157.1 tok/s decode

Raises estimated decode speed by about 187%.

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

~$9,999 MSRP

NVIDIA H20 96GBNVIDIA upgrade

96 GB VRAM (+32)4000 GB/s (+3400)

Raises estimated decode speed by about 258%.196 tok/s decode

Raises estimated decode speed by about 258%.

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

~$12,000 MSRP

Frequently asked questions

Can NVIDIA A16 64GB run internlm JanusCoder 14B?

Yes, NVIDIA A16 64GB can run internlm JanusCoder 14B with a C grade (Runs well). Expected decode speed: 54.8 tok/s.

How much VRAM does internlm JanusCoder 14B need?

internlm JanusCoder 14B (14B parameters) requires approximately 17.8 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 A16 64GB?

On NVIDIA A16 64GB, internlm JanusCoder 14B achieves approximately 54.8 tokens per second decode speed with a time-to-first-token of 3533ms using Q4_K_M quantization.

Can NVIDIA A16 64GB run internlm JanusCoder 14B for coding?

For coding workloads, internlm JanusCoder 14B on NVIDIA A16 64GB receives a C grade with 54.8 tok/s and 467K context.

What context window can internlm JanusCoder 14B use on NVIDIA A16 64GB?

On NVIDIA A16 64GB, internlm JanusCoder 14B can safely use up to 467K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for NVIDIA A16 64GB See all hardware for internlm JanusCoder 14B

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