Can StarCoder 15B run on Tesla P40 24GB?

Q: What should I upgrade first if StarCoder 15B feels slow on Tesla P40 24GB?

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 Q4_K_M

B63Good

Estimated from fit model

StarCoder 15B needs ~27.4 GB VRAM. Tesla P40 24GB has 24.0 GB. With Q4_K_M quantization, expect ~12 tok/s.

Runtime: OllamaCapacity: OffloadBandwidth: LowStack: 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.

StarCoder 15B at Q5_K_M needs 29.0 GB — too much for Tesla P40 24GB (24.0 GB). Runs at Q4_K_M (27.4 GB) with medium quality. 4 quantization levels fit.

Capabilities:

Select quantization to explore

Q5_K_M (High quality) — 29.0 GB, exceeds 24.0 GB available

29.0 GB required24.0 GB available

121% VRAM needed

5.0 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

9.2 tok/s

TTFT

20936 ms

Safe context

Memory

29.0 GB / 24.0 GB

Offload

20%

Memory breakdown

Weights10.8 GB

KV Cache14.6 GB

Runtime1.2 GB

Headroom2.4 GB

See how fast it feels

With memory offload — actual speed may be lower

See how fast it feelsStarCoder 15B 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: 9.2 tok/s decode · 20.9s TTFT (warm) · 23 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 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	A	Tight fit	19.3 tok/s	5477 ms	8K
Coding	F	Too heavy	9.2 tok/s	20936 ms	8K
Agentic Coding	F	Too heavy	3.8 tok/s	74652 ms	8K
Reasoning	F	Too heavy	9.2 tok/s	24743 ms	8K
RAG	F	Too heavy	3.8 tok/s	93315 ms	8K

Quantization options

How StarCoder 15B (15B params) fits at each quantization level on Tesla P40 24GB (24.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	5.9 GB	Low	A71
Q3_K_S	3	7.4 GB	Low	A72
NVFP4	4

Get started

Copy-paste commands to run StarCoder 15B on your machine.

Run

lms load starcoder && lms server start

Upgrade options

Hardware that runs StarCoder 15B well

RTX 5090 32GBBudget pick

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

Makes the model fit on the accelerator instead of staying completely out of reach.113.4 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.

~$1,999 MSRP

RTX PRO 4500 Blackwell 32GBBest value

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

Makes the model fit on the accelerator instead of staying completely out of reach.71.1 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.

~$2,499 MSRP

RTX 5000 Ada 32GBNVIDIA upgrade

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

Makes the model fit on the accelerator instead of staying completely out of reach.43.5 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.

~$4,000 MSRP

Frequently asked questions

Can Tesla P40 24GB run StarCoder 15B?

Yes, Tesla P40 24GB can run StarCoder 15B at Q4_K_M quantization (Very compromised (needs ~1.1 GB host RAM)). The recommended Q5_K_M requires 29.0 GB which exceeds available memory, but at Q4_K_M it needs only 27.4 GB. Expected decode speed: 12.2 tok/s.

How much VRAM does StarCoder 15B need?

StarCoder 15B (15B parameters) requires approximately 29.0 GB at Q5_K_M quantization. On Tesla P40 24GB, it fits at Q4_K_M using 27.4 GB.

What is the best quantization for StarCoder 15B?

The recommended quantization is Q5_K_M, but on Tesla P40 24GB the best fitting quantization is Q4_K_M, which uses 27.4 GB.

What speed will StarCoder 15B run at on Tesla P40 24GB?

On Tesla P40 24GB, StarCoder 15B achieves approximately 12.2 tokens per second decode speed with a time-to-first-token of 15912ms using Q4_K_M quantization.

Can Tesla P40 24GB run StarCoder 15B for coding?

For coding workloads, StarCoder 15B on Tesla P40 24GB receives a F grade with 9.2 tok/s and 8K context.

What context window can StarCoder 15B use on Tesla P40 24GB?

On Tesla P40 24GB, StarCoder 15B can safely use up to 8K tokens of context at Q4_K_M quantization. The model's official context limit is 8K, but available memory constrains the safe maximum.

See all results for Tesla P40 24GB See all hardware for StarCoder 15B

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