Can internlm JanusCoder 14B run on RTX 4050 Laptop 6GB?

Q: What should I upgrade first if internlm JanusCoder 14B feels slow on RTX 4050 Laptop 6GB?

Add more VRAM headroom. The first useful upgrade is more dedicated VRAM so you can fit the model without shrinking context or dropping to a much lower quant.

NO — Won't Fit

F0Won't run

Estimated from fit model

internlm JanusCoder 14B needs ~12.0 GB but RTX 4050 Laptop 6GB only has 6.0 GB. Try a smaller quantization or lighter model.

Runtime: OllamaCapacity: No fitBandwidth: Very lowStack: BasicBottleneck: Memory capacity

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) — 12.0 GB, exceeds 6.0 GB available

12.0 GB required6.0 GB available

200% VRAM needed

6.0 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

2.9 tok/s

TTFT

67432 ms

Safe context

Memory

12.0 GB / 6.0 GB

Offload

50%

Memory breakdown

Weights8.5 GB

KV Cache1.6 GB

Runtime1.2 GB

Headroom0.6 GB

See how fast it feels

With memory offload — actual speed may be lower

See how fast it feelsinternlm JanusCoder 14B on RTX 4050 Laptop 6GB
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: 2.9 tok/s decode · 67.4s TTFT (warm) · 7 tok/s prefill

What limits this setup

Usable VRAM is the main blocker for this model.

Not enough usable memory

The model needs 12.0 GB, but this setup only exposes 6.0 GB of usable VRAM.

Best improvement path

Add more VRAM headroom

The first useful upgrade is more dedicated VRAM so you can fit the model without shrinking context or dropping to a much lower quant.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	F	Too heavy	3.3 tok/s	31680 ms	4K
Coding	F	Too heavy	2.9 tok/s	67432 ms	4K
Agentic Coding	F	Too heavy	2.5 tok/s	114387 ms	4K
Reasoning	F	Too heavy	2.9 tok/s	79693 ms	4K
RAG	F	Too heavy	2.5 tok/s	142983 ms	4K

Quantization options

How internlm JanusCoder 14B (14B params) fits at each quantization level on RTX 4050 Laptop 6GB (6.0 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

Upgrade options

Hardware that runs internlm JanusCoder 14B well

RTX 3060 12GBBudget pick

12 GB VRAM (+6)360 GB/s (+168)

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 (+10)448 GB/s (+256)

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 (+10)288 GB/s (+96)

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 4050 Laptop 6GB run internlm JanusCoder 14B?

No, internlm JanusCoder 14B requires more memory than RTX 4050 Laptop 6GB provides.

How much VRAM does internlm JanusCoder 14B need?

internlm JanusCoder 14B (14B parameters) requires approximately 12.0 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 4050 Laptop 6GB?

On RTX 4050 Laptop 6GB, internlm JanusCoder 14B achieves approximately 2.9 tokens per second decode speed with a time-to-first-token of 67432ms using Q4_K_M quantization.

Can RTX 4050 Laptop 6GB run internlm JanusCoder 14B for coding?

For coding workloads, internlm JanusCoder 14B on RTX 4050 Laptop 6GB receives a F grade with 2.9 tok/s and 4K context.

What context window can internlm JanusCoder 14B use on RTX 4050 Laptop 6GB?

On RTX 4050 Laptop 6GB, internlm JanusCoder 14B can safely use up to 4K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for RTX 4050 Laptop 6GB See all hardware for internlm JanusCoder 14B

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