12 GB VRAM (+2)
B
Makes the model fit on the accelerator instead of staying completely out of reach.30.2 tok/s decode
Makes the model fit on the accelerator instead of staying completely out of reach.
~$329 MSRP
Can StarCoder 7B run on RTX 3080 10GB?
NO — Won't Fit
F0Won't run
○Estimated from fit model
StarCoder 7B needs ~13.8 GB but RTX 3080 10GB only has 10.0 GB. Try a smaller quantization or lighter model.
Runtime: OllamaCapacity: No fitBandwidth: MediumStack: BasicBottleneck: Memory capacity
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) — 13.8 GB, exceeds 10.0 GB available
13.8 GB required10.0 GB available
138% VRAM needed
3.8 GB over capacity — needs offload or smaller quantization
Fit status
Too heavy
Decode
51.5 tok/s
TTFT
3756 ms
Safe context
8K
Memory
13.8 GB / 10.0 GB
Offload
30%
Memory breakdown
Weights4.3 GB
KV Cache7.3 GB
Runtime1.2 GB
Headroom1.0 GB
See how fast it feels
With memory offload — actual speed may be lowerSee how fast it feelsStarCoder 7B on RTX 3080 10GB
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: 51.5 tok/s decode · 3.8s TTFT (warm) · 129 tok/s prefill
What limits this setup
Usable VRAM is the main blocker for this model.
Not enough usable memory
The model needs 13.8 GB, but this setup only exposes 10.0 GB of usable VRAM.
Best improvement path
Add more VRAM headroom
The first useful upgrade is more dedicated VRAM so you can fit the model without shrinking context or dropping to a much lower quant.
Performance by workload
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | A | Runs with offload (needs ~0.1 GB host RAM) | 98.0 tok/s | 1078 ms | 8K |
| Coding | F | Too heavy | 51.5 tok/s | 3756 ms | 8K |
| Agentic Coding | F | Too heavy | 21.0 tok/s | 13390 ms | 8K |
| Reasoning | F | Too heavy | 51.5 tok/s | 4439 ms | 8K |
| RAG | F | Too heavy | 21.0 tok/s | 16738 ms | 8K |
Quantization options
How StarCoder 7B (7B params) fits at each quantization level on RTX 3080 10GB (10.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 2.7 GB | Low | A74 |
Q3_K_S | 3 | 3.4 GB | Low | A75 |
NVFP4 | 4 | 3.9 GB | Medium | A76 |
Q4_K_M | 4 | 4.3 GB | Medium | A76 |
Q5_K_M | 5 | 5.0 GB | High | A76 |
Q6_KBest for your GPU | 6 | 5.7 GB | High | A76 |
Q8_0 | 8 | 7.5 GB | Very High | F0 |
F16 | 16 | 14.3 GB | Maximum | F0 |
Upgrade options
Hardware that runs StarCoder 7B well
16 GB VRAM (+6)
A
Makes the model fit on the accelerator instead of staying completely out of reach.65 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.
~$449 MSRP
16 GB VRAM (+6)
A
Makes the model fit on the accelerator instead of staying completely out of reach.49.2 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.
~$499 MSRP