12 GB VRAM (+4)672 GB/s (+448)
C
Raises estimated decode speed by about 241%.108.2 tok/s decode
Raises estimated decode speed by about 241%.
Adds memory headroom for longer context windows and future model growth.
~$549 MSRP
Can StarCoder2 7B run on RTX 3050 8GB?
YES — Runs Great
C53Usable
○Estimated from fit model
StarCoder2 7B needs ~6.5 GB VRAM. RTX 3050 8GB has 8.0 GB. With Q4_K_M quantization, expect ~35 tok/s.
Runtime: llama.cppCapacity: RoomyBandwidth: Very lowStack: StandardBottleneck: Memory bandwidth
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) — 6.5 GB, 31.7 tok/s, Runs well
6.5 GB required8.0 GB available
81% VRAM used
Fit status
Runs well
Decode
31.7 tok/s
TTFT
6098 ms
Safe context
16K
Memory
6.5 GB / 8.0 GB
Memory breakdown
Weights4.3 GB
KV Cache0.5 GB
Runtime0.9 GB
Headroom0.8 GB
See how fast it feels
See how fast it feelsStarCoder2 7B on RTX 3050 8GB
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: 31.7 tok/s decode · 6.1s TTFT (warm) · 79 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 | C | Runs well | 34.6 tok/s | 3050 ms | 16K |
| Coding | C | Runs well | 34.6 tok/s | 5592 ms | 16K |
| Agentic Coding | C | Tight fit | 34.6 tok/s | 8133 ms | 16K |
| Reasoning | C | Runs well | 34.6 tok/s | 6608 ms | 16K |
| RAG | C | Tight fit | 34.6 tok/s | 10167 ms | 16K |
Quantization options
How StarCoder2 7B (7B params) fits at each quantization level on RTX 3050 8GB (8.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 2.7 GB | Low | C52 |
Q3_K_S | 3 | 3.4 GB | Low | C53 |
NVFP4 | 4 |
Get started
Copy-paste commands to run StarCoder2 7B on your machine.
Run
lms load starcoder2-7b && lms server startUpgrade options
Hardware that runs StarCoder2 7B well
12 GB VRAM (+4)504 GB/s (+280)
C
Raises estimated decode speed by about 188%.91.3 tok/s decode
Raises estimated decode speed by about 188%.
Adds memory headroom for longer context windows and future model growth.
~$599 MSRP
12 GB VRAM (+4)504 GB/s (+280)
C
Raises estimated decode speed by about 180%.88.9 tok/s decode
Raises estimated decode speed by about 180%.
Adds memory headroom for longer context windows and future model growth.
~$599 MSRP