Can StarCoder 15B run on Tesla P40 24GB?

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

👁 NVIDIA
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

👁 NVIDIA
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

👁 NVIDIA
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

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

URL: https://willitrunai.com/can-run/starcoder-15b-on-tesla-p40-24gb

⇱

Can StarCoder 15B run on Tesla P40 24GB?

YES — With Q4_K_M

Choose the run profile you care about

Memory breakdown

See how fast it feels

What limits this setup

Best improvement path

Performance by workload

Quantization options

Get started

Hardware that runs StarCoder 15B well

Frequently asked questions