20 GB VRAM (+4)
C
Adds memory headroom for longer context windows and future model growth.30.7 tok/s decode
Adds memory headroom for longer context windows and future model growth.
~$1,250 MSRP
Can starcoder2 15b instruct v0.1 run on Tesla P100 16GB?
YES — Tight Fit
C51Usable
○Estimated from fit model
starcoder2 15b instruct v0.1 needs ~13.7 GB VRAM. Tesla P100 16GB has 16.0 GB. With Q4_K_M quantization, expect ~47 tok/s.
Runtime: OllamaCapacity: TightBandwidth: MediumStack: BasicBottleneck: Balanced
Share:
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.
Select quantization to explore
Q4_K_M (Medium quality) — 13.7 GB, 47.2 tok/s, Tight fit
13.7 GB required16.0 GB available
86% VRAM used
Fit status
Tight fit
Decode
47.2 tok/s
TTFT
4102 ms
Safe context
37K
Memory
13.7 GB / 16.0 GB
Memory breakdown
Weights9.2 GB
KV Cache1.8 GB
Runtime1.2 GB
Headroom1.6 GB
See how fast it feels
See how fast it feelsstarcoder2 15b instruct v0.1 on Tesla P100 16GB
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: 47.2 tok/s decode · 4.1s TTFT (warm) · 118 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 | 47.2 tok/s | 2237 ms | 37K |
| Coding | C | Tight fit | 47.2 tok/s | 4102 ms | 37K |
| Agentic Coding | C | Runs with offload | 47.2 tok/s | 5966 ms | 37K |
| Reasoning | C | Tight fit | 47.2 tok/s | 4847 ms | 37K |
| RAG | C | Runs with offload | 47.2 tok/s | 7458 ms | 37K |
Quantization options
How starcoder2 15b instruct v0.1 (15B params) fits at each quantization level on Tesla P100 16GB (16.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 5.9 GB | Low | C49 |
Q3_K_S | 3 | 7.4 GB | Low | C51 |
NVFP4 | 4 |
Get started
Copy-paste commands to run starcoder2 15b instruct v0.1 on your machine.
Run
lms load hf-bartowski--starcoder2-15b-instruct-v0-1-gguf && lms server startUpgrade options
Hardware that runs starcoder2 15b instruct v0.1 well
24 GB VRAM (+8)936 GB/s (+204)
C
Raises estimated decode speed by about 52%.71.6 tok/s decode
Raises estimated decode speed by about 52%.
Adds memory headroom for longer context windows and future model growth.
~$1,499 MSRP
24 GB VRAM (+8)
C
Raises estimated decode speed by about 31%.61.7 tok/s decode
Raises estimated decode speed by about 31%.
Adds memory headroom for longer context windows and future model growth.
~$1,599 MSRP