10 GB VRAM (+2)760 GB/s (+312)
B
Adds memory headroom for longer context windows and future model growth.84 tok/s decode
Adds memory headroom for longer context windows and future model growth.
~$699 MSRP
Can StarCoder2 7B run on RTX 3070 8GB?
YES — Runs Great
B55Good
○Estimated from fit model
StarCoder2 7B needs ~6.5 GB VRAM. RTX 3070 8GB has 8.0 GB. With Q4_K_M quantization, expect ~73 tok/s.
Runtime: llama.cppCapacity: RoomyBandwidth: LowStack: StandardBottleneck: 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) — 6.5 GB, 67.3 tok/s, Runs well
6.5 GB required8.0 GB available
81% VRAM used
Fit status
Runs well
Decode
67.3 tok/s
TTFT
2875 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 3070 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: 67.3 tok/s decode · 2.9s TTFT (warm) · 168 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 | 73.4 tok/s | 1438 ms | 16K |
| Coding | B | Runs well | 73.4 tok/s | 2636 ms | 16K |
| Agentic Coding | C | Tight fit | 73.4 tok/s | 3834 ms | 16K |
| Reasoning | B | Runs well | 73.4 tok/s | 3115 ms | 16K |
| RAG | C | Tight fit | 73.4 tok/s | 4793 ms | 16K |
Quantization options
How StarCoder2 7B (7B params) fits at each quantization level on RTX 3070 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