Can internlm JanusCoder 14B run on RTX 3060 12GB?

Q: What should I upgrade first if internlm JanusCoder 14B feels slow on RTX 3060 12GB?

Buy headroom, not only minimum fit. A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.

YES — With Offload

C49Usable

Estimated from fit model

internlm JanusCoder 14B needs ~12.3 GB VRAM. RTX 3060 12GB has 12.0 GB. With Q4_K_M quantization, expect ~20 tok/s.

Runtime: llama.cppCapacity: OffloadBandwidth: LowStack: StandardBottleneck: Host offload

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) — 12.3 GB, 19.5 tok/s, Runs with offload (needs ~0.2 GB host RAM)

12.3 GB required12.0 GB available

103% VRAM needed

0.3 GB over capacity — needs offload or smaller quantization

Fit status

Runs with offload (needs ~0.2 GB host RAM)

Decode

19.5 tok/s

TTFT

9939 ms

Safe context

13K

Memory

12.3 GB / 12.0 GB

Memory breakdown

Weights8.5 GB

KV Cache1.6 GB

Runtime0.9 GB

Headroom1.2 GB

See how fast it feels

See how fast it feelsinternlm JanusCoder 14B on RTX 3060 12GB
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: 19.5 tok/s decode · 9.9s TTFT (warm) · 49 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

Very little memory headroom

You can run the model, but there is not much room left for longer context, bigger batches, extra apps, or future model updates.

Best improvement path

Buy headroom, not only minimum fit

A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	C	Runs with offload	27.8 tok/s	3796 ms	13K
Coding	C	Runs with offload	19.9 tok/s	9741 ms	13K
Agentic Coding	D	Very compromised	15.3 tok/s	18448 ms	13K
Reasoning	C	Runs with offload	19.9 tok/s	11512 ms	13K
RAG	D	Very compromised	15.3 tok/s	23060 ms	13K

Quantization options

How internlm JanusCoder 14B (14B params) fits at each quantization level on RTX 3060 12GB (12.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	5.5 GB	Low	C52
Q3_K_S	3	6.9 GB	Low	C52
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 5060 Ti 16GBBudget pick

16 GB VRAM (+4)448 GB/s (+88)

Raises estimated decode speed by about 62%.31.5 tok/s decode

Raises estimated decode speed by about 62%.

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

~$449 MSRP

RTX 4060 Ti 16GBBest value

16 GB VRAM (+4)

Raises estimated decode speed by about 32%.25.8 tok/s decode

Raises estimated decode speed by about 32%.

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

~$499 MSRP

RTX 2000 Ada 16GBNVIDIA upgrade

16 GB VRAM (+4)

Raises estimated decode speed by about 31%.25.6 tok/s decode

Raises estimated decode speed by about 31%.

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

~$625 MSRP

Frequently asked questions

Can RTX 3060 12GB run internlm JanusCoder 14B?

Yes, RTX 3060 12GB can run internlm JanusCoder 14B with a C grade (Runs with offload). Expected decode speed: 19.9 tok/s.

How much VRAM does internlm JanusCoder 14B need?

internlm JanusCoder 14B (14B parameters) requires approximately 12.3 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 3060 12GB?

On RTX 3060 12GB, internlm JanusCoder 14B achieves approximately 19.9 tokens per second decode speed with a time-to-first-token of 9741ms using Q4_K_M quantization.

Can RTX 3060 12GB run internlm JanusCoder 14B for coding?

For coding workloads, internlm JanusCoder 14B on RTX 3060 12GB receives a C grade with 19.9 tok/s and 13K context.

What context window can internlm JanusCoder 14B use on RTX 3060 12GB?

On RTX 3060 12GB, internlm JanusCoder 14B can safely use up to 13K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

What should I upgrade first if internlm JanusCoder 14B feels slow on RTX 3060 12GB?

See all results for RTX 3060 12GB See all hardware for internlm JanusCoder 14B

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