Can internlm JanusCoder 14B run on RTX 3080 10GB?

Q: What should I upgrade first if internlm JanusCoder 14B feels slow on RTX 3080 10GB?

Remove offload with more accelerator memory. Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.

YES — With NVFP4

C41Usable

Estimated from fit model

internlm JanusCoder 14B needs ~11.7 GB VRAM. RTX 3080 10GB has 10.0 GB. With NVFP4 quantization, expect ~42 tok/s.

Runtime: OllamaCapacity: OffloadBandwidth: MediumStack: BasicBottleneck: 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.

internlm JanusCoder 14B at Q4_K_M needs 12.4 GB — too much for RTX 3080 10GB (10.0 GB). Runs at NVFP4 (11.7 GB) with medium quality. 3 quantization levels fit.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 12.4 GB, exceeds 10.0 GB available

12.4 GB required10.0 GB available

124% VRAM needed

2.4 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

32.4 tok/s

TTFT

5983 ms

Safe context

Memory

12.4 GB / 10.0 GB

Offload

20%

Memory breakdown

Weights8.5 GB

KV Cache1.6 GB

Runtime1.2 GB

Headroom1.0 GB

See how fast it feels

With memory offload — actual speed may be lower

See how fast it feelsinternlm JanusCoder 14B on RTX 3080 10GB
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: 32.4 tok/s decode · 6.0s TTFT (warm) · 81 tok/s prefill

What limits this setup

It fits through host-memory offload, and offload is the main reason performance drops.

CPU or host-memory offload is active

About 10% of the working set spills out of accelerator memory, which usually hurts latency and sustained decode throughput.

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

Remove offload with more accelerator memory

Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.

Buy headroom, not only minimum fit

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

Increase host RAM if you keep offloading

This setup may need roughly 1.1 GB of extra host RAM just for the offloaded portion, before OS and other tools.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	C	Very compromised (needs ~1.2 GB host RAM)	37.4 tok/s	2825 ms	4K
Coding	F	Too heavy	32.4 tok/s	5983 ms	4K
Agentic Coding	F	Too heavy	24.9 tok/s	11308 ms	4K
Reasoning	F	Too heavy	32.4 tok/s	7070 ms	4K
RAG	F	Too heavy	24.9 tok/s	14135 ms	4K

Quantization options

How internlm JanusCoder 14B (14B params) fits at each quantization level on RTX 3080 10GB (10.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	5.5 GB	Low	C52
Q3_K_SBest for your GPU	3	6.9 GB	Low	C52

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 3060 12GBBudget pick

12 GB VRAM (+2)

Makes the model fit on the accelerator instead of staying completely out of reach.18.9 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

~$329 MSRP

RTX 5060 Ti 16GBBest value

16 GB VRAM (+6)

Makes the model fit on the accelerator instead of staying completely out of reach.32.5 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

~$449 MSRP

RTX 4060 Ti 16GBNVIDIA upgrade

16 GB VRAM (+6)

Makes the model fit on the accelerator instead of staying completely out of reach.24.6 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

~$499 MSRP

Frequently asked questions

Can RTX 3080 10GB run internlm JanusCoder 14B?

Yes, RTX 3080 10GB can run internlm JanusCoder 14B at NVFP4 quantization (Very compromised (needs ~1.1 GB host RAM)). The recommended Q4_K_M requires 12.4 GB which exceeds available memory, but at NVFP4 it needs only 11.7 GB. Expected decode speed: 41.8 tok/s.

How much VRAM does internlm JanusCoder 14B need?

internlm JanusCoder 14B (14B parameters) requires approximately 12.4 GB at Q4_K_M quantization. On RTX 3080 10GB, it fits at NVFP4 using 11.7 GB.

What is the best quantization for internlm JanusCoder 14B?

The recommended quantization is Q4_K_M, but on RTX 3080 10GB the best fitting quantization is NVFP4, which uses 11.7 GB.

What speed will internlm JanusCoder 14B run at on RTX 3080 10GB?

On RTX 3080 10GB, internlm JanusCoder 14B achieves approximately 41.8 tokens per second decode speed with a time-to-first-token of 4628ms using NVFP4 quantization.

Can RTX 3080 10GB run internlm JanusCoder 14B for coding?

For coding workloads, internlm JanusCoder 14B on RTX 3080 10GB receives a F grade with 32.4 tok/s and 4K context.

What context window can internlm JanusCoder 14B use on RTX 3080 10GB?

On RTX 3080 10GB, internlm JanusCoder 14B can safely use up to 4K tokens of context at NVFP4 quantization. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for RTX 3080 10GB See all hardware for internlm JanusCoder 14B

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