Can internlm JanusCoder 14B run on RTX 4060 Ti 16GB?

YES — Runs Great

C52Usable

Estimated from fit model

internlm JanusCoder 14B needs ~12.7 GB VRAM. RTX 4060 Ti 16GB has 16.0 GB. With Q4_K_M quantization, expect ~25 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: LowStack: 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) — 12.7 GB, 25.8 tok/s, Runs well

12.7 GB required16.0 GB available

79% VRAM used

Fit status

Runs well

Decode

25.8 tok/s

TTFT

7490 ms

Safe context

48K

Memory

12.7 GB / 16.0 GB

Memory breakdown

Weights8.5 GB

KV Cache1.6 GB

Runtime0.9 GB

Headroom1.6 GB

See how fast it feels

See how fast it feelsinternlm JanusCoder 14B on RTX 4060 Ti 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: 25.8 tok/s decode · 7.5s TTFT (warm) · 65 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	25.8 tok/s	4085 ms	48K
Coding	C	Runs well	24.6 tok/s	7864 ms	48K
Agentic Coding	C	Tight fit	25.8 tok/s	10894 ms	48K
Reasoning	C	Runs well	25.8 tok/s	8851 ms	48K
RAG	C	Tight fit	25.8 tok/s	13617 ms	48K

Quantization options

How internlm JanusCoder 14B (14B params) fits at each quantization level on RTX 4060 Ti 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

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 3090 24GBBudget pick

24 GB VRAM (+8)936 GB/s (+648)

Raises estimated decode speed by about 191%.75.2 tok/s decode

Raises estimated decode speed by about 191%.

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

~$1,499 MSRP

RTX 4090 24GBBest value

24 GB VRAM (+8)1008 GB/s (+720)

Raises estimated decode speed by about 265%.94.2 tok/s decode

Raises estimated decode speed by about 265%.

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

~$1,599 MSRP

RTX PRO 4000 Blackwell 24GBNVIDIA upgrade

24 GB VRAM (+8)672 GB/s (+384)

Raises estimated decode speed by about 156%.66.1 tok/s decode

Raises estimated decode speed by about 156%.

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

~$1,599 MSRP

Frequently asked questions

Can RTX 4060 Ti 16GB run internlm JanusCoder 14B?

Yes, RTX 4060 Ti 16GB can run internlm JanusCoder 14B with a C grade (Runs well). Expected decode speed: 24.6 tok/s.

How much VRAM does internlm JanusCoder 14B need?

internlm JanusCoder 14B (14B parameters) requires approximately 12.7 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 4060 Ti 16GB?

On RTX 4060 Ti 16GB, internlm JanusCoder 14B achieves approximately 24.6 tokens per second decode speed with a time-to-first-token of 7864ms using Q4_K_M quantization.

Can RTX 4060 Ti 16GB run internlm JanusCoder 14B for coding?

For coding workloads, internlm JanusCoder 14B on RTX 4060 Ti 16GB receives a C grade with 24.6 tok/s and 48K context.

What context window can internlm JanusCoder 14B use on RTX 4060 Ti 16GB?

On RTX 4060 Ti 16GB, internlm JanusCoder 14B can safely use up to 48K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for RTX 4060 Ti 16GB See all hardware for internlm JanusCoder 14B

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