RTX 4000 Ada 20GBGünstige Wahl20 GB VRAM (+4)
C
Adds memory headroom for longer context windows and future model growth.30.7 tok/s Dekodierung
Adds memory headroom for longer context windows and future model growth.
ca. $1,250 MSRP
Can starcoder2 15b instruct v0.1 run on RTX 5080 Laptop 16GB?
YES — Tight Fit
C53Usable
Estimated from fit model
starcoder2 15b instruct v0.1 needs ~13.7 GB VRAM. RTX 5080 Laptop 16GB has 16.0 GB. With Q4_K_M quantization, expect ~71 tok/s.
Runtime: OllamaCapacity: TightBandwidth: MediumStack: 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.
Select quantization to explore
Q4_K_M (Medium quality) — 13.7 GB, 70.5 tok/s, Tight fit
13.7 GB required16.0 GB available
86% VRAM used
Fit status
Tight fit
Decode
70.5 tok/s
TTFT
2746 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 RTX 5080 Laptop 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: 70.5 tok/s decode · 2.7s TTFT (warm) · 176 tok/s prefill
What limits this setup
This setup is broadly balanced for this model.
No major red flags
This recommendation has enough memory headroom and acceptable estimated speed for the selected workload.
Best improvement path
Performance by workload
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | B | Runs well | 70.5 tok/s | 1498 ms | 37K |
| Coding | C | Tight fit | 70.5 tok/s | 2746 ms | 37K |
| Agentic Coding | C | Runs with offload | 70.5 tok/s | 3994 ms | 37K |
| Reasoning | C | Tight fit | 70.5 tok/s | 3245 ms | 37K |
| RAG | C | Runs with offload | 70.5 tok/s | 4993 ms | 37K |
Quantization options
How starcoder2 15b instruct v0.1 (15B params) fits at each quantization level on RTX 5080 Laptop 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 | 8.4 GB | Medium | C51 |
Q4_K_M | 4 | 9.2 GB | Medium | C51 |
Q5_K_M | 5 | 10.8 GB | High | C50 |
Q6_KBest for your GPU | 6 | 12.3 GB | High | C50 |
Q8_0 | 8 | 16.1 GB | Very High | F0 |
F16 | 16 | 30.7 GB | Maximum | F0 |
Get started
Copy-paste commands to run starcoder2 15b instruct v0.1 on your machine.
Run
lms load hf-lmstudio-community--starcoder2-15b-instruct-v0-1-gguf && lms server startUpgrade-Optionen
Hardware, die starcoder2 15b instruct v0.1 gut ausführt
RTX 4000 Ada 20GBGünstige WahlRTX 3090 24GBBestes Preis-Leistungs-Verhältnis24 GB VRAM (+8)936 GB/s (+168)
C
Adds memory headroom for longer context windows and future model growth.71.6 tok/s Dekodierung
Adds memory headroom for longer context windows and future model growth.
ca. $1,499 MSRP
RTX 4090 24GBNVIDIA-Upgrade24 GB VRAM (+8)1008 GB/s (+240)
C
Adds memory headroom for longer context windows and future model growth.83.7 tok/s Dekodierung
Adds memory headroom for longer context windows and future model growth.
ca. $1,599 MSRP
Frequently asked questions
Can RTX 5080 Laptop 16GB run starcoder2 15b instruct v0.1?
Yes, RTX 5080 Laptop 16GB can run starcoder2 15b instruct v0.1 with a C grade (Tight fit). Expected decode speed: 70.5 tok/s.
How much VRAM does starcoder2 15b instruct v0.1 need?
starcoder2 15b instruct v0.1 (15B parameters) requires approximately 13.7 GB of memory with Q4_K_M quantization.
What is the best quantization for starcoder2 15b instruct v0.1?
The recommended quantization for starcoder2 15b instruct v0.1 is Q4_K_M, which balances quality and memory efficiency.
What speed will starcoder2 15b instruct v0.1 run at on RTX 5080 Laptop 16GB?
On RTX 5080 Laptop 16GB, starcoder2 15b instruct v0.1 achieves approximately 70.5 tokens per second decode speed with a time-to-first-token of 2746ms using Q4_K_M quantization.
Can RTX 5080 Laptop 16GB run starcoder2 15b instruct v0.1 for coding?
For coding workloads, starcoder2 15b instruct v0.1 on RTX 5080 Laptop 16GB receives a C grade with 70.5 tok/s and 37K context.
What context window can starcoder2 15b instruct v0.1 use on RTX 5080 Laptop 16GB?
On RTX 5080 Laptop 16GB, starcoder2 15b instruct v0.1 can safely use up to 37K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
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