Can internlm JanusCoder 14B run on RTX 3070 Ti 8GB?

Q: What should I upgrade first if internlm JanusCoder 14B feels slow on RTX 3070 Ti 8GB?

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 Q2_K

C41Usable

Estimated from fit model

internlm JanusCoder 14B needs ~9.1 GB VRAM. RTX 3070 Ti 8GB has 8.0 GB. With Q2_K quantization, expect ~39 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 12.2 GB — too much for RTX 3070 Ti 8GB (8.0 GB). Runs at Q2_K (9.1 GB) with low quality.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 12.2 GB, exceeds 8.0 GB available

12.2 GB required8.0 GB available

153% VRAM needed

4.2 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

15.9 tok/s

TTFT

12201 ms

Safe context

Memory

12.2 GB / 8.0 GB

Offload

30%

Memory breakdown

Weights8.5 GB

KV Cache1.6 GB

Runtime1.2 GB

Headroom0.8 GB

See how fast it feels

With memory offload — actual speed may be lower

See how fast it feelsinternlm JanusCoder 14B on RTX 3070 Ti 8GB
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: 15.9 tok/s decode · 12.2s TTFT (warm) · 40 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 0.7 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	F	Too heavy	18.4 tok/s	5747 ms	4K
Coding	F	Too heavy	15.9 tok/s	12201 ms	4K
Agentic Coding	F	Too heavy	12.2 tok/s	23155 ms	4K
Reasoning	F	Too heavy	15.9 tok/s	14419 ms	4K
RAG	F	Too heavy	12.2 tok/s	28943 ms	4K

Quantization options

How internlm JanusCoder 14B (14B params) fits at each quantization level on RTX 3070 Ti 8GB (8.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	5.5 GB	Low	F0
Q3_K_S	3	6.9 GB	Low	F0
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 3060 12GBBudget pick

12 GB VRAM (+4)

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 (+8)

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 (+8)

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 3070 Ti 8GB run internlm JanusCoder 14B?

Yes, RTX 3070 Ti 8GB can run internlm JanusCoder 14B at Q2_K quantization (Very compromised (needs ~0.7 GB host RAM)). The recommended Q4_K_M requires 12.2 GB which exceeds available memory, but at Q2_K it needs only 9.1 GB. Expected decode speed: 39.0 tok/s.

How much VRAM does internlm JanusCoder 14B need?

internlm JanusCoder 14B (14B parameters) requires approximately 12.2 GB at Q4_K_M quantization. On RTX 3070 Ti 8GB, it fits at Q2_K using 9.1 GB.

What is the best quantization for internlm JanusCoder 14B?

The recommended quantization is Q4_K_M, but on RTX 3070 Ti 8GB the best fitting quantization is Q2_K, which uses 9.1 GB.

What speed will internlm JanusCoder 14B run at on RTX 3070 Ti 8GB?

On RTX 3070 Ti 8GB, internlm JanusCoder 14B achieves approximately 39.0 tokens per second decode speed with a time-to-first-token of 4968ms using Q2_K quantization.

Can RTX 3070 Ti 8GB run internlm JanusCoder 14B for coding?

For coding workloads, internlm JanusCoder 14B on RTX 3070 Ti 8GB receives a F grade with 15.9 tok/s and 4K context.

What context window can internlm JanusCoder 14B use on RTX 3070 Ti 8GB?

On RTX 3070 Ti 8GB, internlm JanusCoder 14B can safely use up to 5K tokens of context at Q2_K quantization. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for RTX 3070 Ti 8GB See all hardware for internlm JanusCoder 14B

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