Can internlm JanusCoder 14B run on Tesla P40 24GB?

YES — Runs Great

C49Usable

Estimated from fit model

internlm JanusCoder 14B needs ~13.8 GB VRAM. Tesla P40 24GB has 24.0 GB. With Q4_K_M quantization, expect ~24 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: LowStack: BasicBottleneck: Balanced

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) — 13.8 GB, 23.9 tok/s, Runs well

13.8 GB required24.0 GB available

58% VRAM used

Fit status

Runs well

Decode

23.9 tok/s

TTFT

8099 ms

Safe context

116K

Memory

13.8 GB / 24.0 GB

Memory breakdown

Weights8.5 GB

KV Cache1.6 GB

Runtime1.2 GB

Headroom2.4 GB

See how fast it feels

See how fast it feelsinternlm JanusCoder 14B on Tesla P40 24GB
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: 23.9 tok/s decode · 8.1s TTFT (warm) · 60 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

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

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	C	Runs well	23.9 tok/s	4418 ms	116K
Coding	C	Runs well	23.9 tok/s	8099 ms	116K
Agentic Coding	C	Runs well	23.9 tok/s	11780 ms	116K
Reasoning	C	Runs well	23.9 tok/s	9572 ms	116K
RAG	C	Runs well	23.9 tok/s	14726 ms	116K

Quantization options

How internlm JanusCoder 14B (14B params) fits at each quantization level on Tesla P40 24GB (24.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	5.5 GB	Low	C45
Q3_K_S	3	6.9 GB	Low	C46
NVFP4	4	7.8 GB	Medium	C47
Q4_K_M	4	8.5 GB	Medium	C47
Q5_K_M	5	10.1 GB	High	C48
Q6_K	6	11.5 GB	High	C49
Q8_0Best for your GPU	8	15.0 GB	Very High	C50
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

Opções de upgrade

Hardware que roda bem internlm JanusCoder 14B

RTX 5090 32GBOpção econômica

32 GB VRAM (+8)1792 GB/s (+1446)

Raises estimated decode speed by about 488%.140.6 tok/s decodificação

Raises estimated decode speed by about 488%.

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

~$1,999 MSRP

RTX PRO 4500 Blackwell 32GBMelhor custo-benefício

32 GB VRAM (+8)896 GB/s (+550)

Raises estimated decode speed by about 269%.88.1 tok/s decodificação

Raises estimated decode speed by about 269%.

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

~$2,499 MSRP

RTX 5000 Ada 32GBUpgrade NVIDIA

32 GB VRAM (+8)576 GB/s (+230)

Raises estimated decode speed by about 126%.54 tok/s decodificação

Raises estimated decode speed by about 126%.

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

~$4,000 MSRP

Frequently asked questions

Can Tesla P40 24GB run internlm JanusCoder 14B?

Yes, Tesla P40 24GB can run internlm JanusCoder 14B with a C grade (Runs well). Expected decode speed: 23.9 tok/s.

How much VRAM does internlm JanusCoder 14B need?

internlm JanusCoder 14B (14B parameters) requires approximately 13.8 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 Tesla P40 24GB?

On Tesla P40 24GB, internlm JanusCoder 14B achieves approximately 23.9 tokens per second decode speed with a time-to-first-token of 8099ms using Q4_K_M quantization.

Can Tesla P40 24GB run internlm JanusCoder 14B for coding?

For coding workloads, internlm JanusCoder 14B on Tesla P40 24GB receives a C grade with 23.9 tok/s and 116K context.

What context window can internlm JanusCoder 14B use on Tesla P40 24GB?

On Tesla P40 24GB, internlm JanusCoder 14B can safely use up to 116K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for Tesla P40 24GB See all hardware for internlm JanusCoder 14B

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