Can internlm JanusCoder 14B run on RTX 2080 Ti 11GB?

BARELY — Tight on Memory

D40Poor

Estimated from fit model

internlm JanusCoder 14B needs ~12.2 GB VRAM. RTX 2080 Ti 11GB has 11.0 GB. With Q4_K_M quantization, expect ~27 tok/s.

Runtime: llama.cppCapacity: OffloadBandwidth: MediumStack: 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) — 12.2 GB, 27.3 tok/s, Very compromised (needs ~0.8 GB host RAM)

12.2 GB required11.0 GB available

111% VRAM needed

1.2 GB over capacity — needs offload or smaller quantization

Fit status

Very compromised (needs ~0.8 GB host RAM)

Decode

27.3 tok/s

TTFT

7082 ms

Safe context

Memory

12.2 GB / 11.0 GB

Offload

10%

Memory breakdown

Weights8.5 GB

KV Cache1.6 GB

Runtime0.9 GB

Headroom1.1 GB

See how fast it feels

See how fast it feelsinternlm JanusCoder 14B on RTX 2080 Ti 11GB
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: 27.3 tok/s decode · 7.1s TTFT (warm) · 68 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.

Older PCIe generation

PCIe 3.0 is workable, but it compounds the penalty when you offload heavily or try to scale across multiple cards.

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 0.8 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	Runs with offload (needs ~0.3 GB host RAM)	31.9 tok/s	3314 ms	4K
Coding	D	Very compromised (needs ~0.8 GB host RAM)	27.3 tok/s	7082 ms	4K
Agentic Coding	F	Too heavy	20.7 tok/s	13595 ms	4K
Reasoning	D	Very compromised (needs ~0.8 GB host RAM)	27.3 tok/s	8369 ms	4K
RAG	F	Too heavy	20.7 tok/s	16993 ms	4K

Quantization options

How internlm JanusCoder 14B (14B params) fits at each quantization level on RTX 2080 Ti 11GB (11.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	5.5 GB	Low	C52
Q3_K_S	3	6.9 GB	Low	C52
NVFP4Best for your GPU	4	7.8 GB	Medium	C51
Q4_K_M	4	8.5 GB	Medium	F0
Q5_K_M	5	10.1 GB	High	F0
Q6_K	6	11.5 GB	High	F0
Q8_0	8	15.0 GB	Very High	F0
F16	16	28.7 GB	Maximum	F0

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

Opciones de mejora

Hardware que ejecuta bien internlm JanusCoder 14B

RTX 3060 12GBOpción económica

12 GB VRAM (+1)

Elimina el offload a memoria del sistema, que suele ser la mayor mejora individual en latencia y throughput.19.5 tok/s decodificación

Elimina el offload a memoria del sistema, que suele ser la mayor mejora individual en latencia y throughput.

~$329 MSRP

RTX 5060 Ti 16GBMejor relación calidad-precio

16 GB VRAM (+5)

Elimina el offload a memoria del sistema, que suele ser la mayor mejora individual en latencia y throughput.31.5 tok/s decodificación

Elimina el offload a memoria del sistema, que suele ser la mayor mejora individual en latencia y throughput.

Añade margen de memoria para más contexto y para que el modelo envejezca mejor.

~$449 MSRP

RTX 4060 Ti 16GBMejora NVIDIA

16 GB VRAM (+5)

Elimina el offload a memoria del sistema, que suele ser la mayor mejora individual en latencia y throughput.25.8 tok/s decodificación

Elimina el offload a memoria del sistema, que suele ser la mayor mejora individual en latencia y throughput.

Añade margen de memoria para más contexto y para que el modelo envejezca mejor.

~$499 MSRP

Frequently asked questions

Can RTX 2080 Ti 11GB run internlm JanusCoder 14B?

Yes, RTX 2080 Ti 11GB can run internlm JanusCoder 14B with a D grade (Very compromised (needs ~0.8 GB host RAM)). Expected decode speed: 27.3 tok/s.

How much VRAM does internlm JanusCoder 14B need?

internlm JanusCoder 14B (14B parameters) requires approximately 12.2 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 2080 Ti 11GB?

On RTX 2080 Ti 11GB, internlm JanusCoder 14B achieves approximately 27.3 tokens per second decode speed with a time-to-first-token of 7082ms using Q4_K_M quantization.

Can RTX 2080 Ti 11GB run internlm JanusCoder 14B for coding?

For coding workloads, internlm JanusCoder 14B on RTX 2080 Ti 11GB receives a D grade with 27.3 tok/s and 4K context.

What context window can internlm JanusCoder 14B use on RTX 2080 Ti 11GB?

On RTX 2080 Ti 11GB, 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.

What should I upgrade first if internlm JanusCoder 14B feels slow on RTX 2080 Ti 11GB?

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.

See all results for RTX 2080 Ti 11GB See all hardware for internlm JanusCoder 14B

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