8 GB VRAM (+2)224 GB/s (+32)
C
Raises estimated decode speed by about 40%.31.7 tok/s decode
Raises estimated decode speed by about 40%.
Adds memory headroom for longer context windows and future model growth.
~$249 MSRP
Can StarCoder2 7B run on RTX 4050 Laptop 6GB?
YES — With Offload
C49Usable
○Estimated from fit model
StarCoder2 7B needs ~6.3 GB VRAM. RTX 4050 Laptop 6GB has 6.0 GB. With Q4_K_M quantization, expect ~23 tok/s.
Runtime: llama.cppCapacity: OffloadBandwidth: 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) — 6.3 GB, 22.6 tok/s, Runs with offload (needs ~0.2 GB host RAM)
6.3 GB required6.0 GB available
105% VRAM needed
0.3 GB over capacity — needs offload or smaller quantization
Fit status
Runs with offload (needs ~0.2 GB host RAM)
Decode
22.6 tok/s
TTFT
8557 ms
Safe context
8K
Memory
6.3 GB / 6.0 GB
Memory breakdown
Weights4.3 GB
KV Cache0.5 GB
Runtime0.9 GB
Headroom0.6 GB
See how fast it feels
See how fast it feelsStarCoder2 7B on RTX 4050 Laptop 6GB
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: 22.6 tok/s decode · 8.6s TTFT (warm) · 57 tok/s prefill
What limits this setup
This setup is broadly balanced for this model.
Very little memory headroom
You can run the model, but there is not much room left for longer context, bigger batches, extra apps, or future model updates.
Best improvement path
Buy headroom, not only minimum fit
A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
Performance by workload
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | C | Runs with offload (needs ~0 GB host RAM) | 24.6 tok/s | 4292 ms | 8K |
| Coding | C | Runs with offload (needs ~0.2 GB host RAM) | 22.6 tok/s | 8557 ms | 8K |
| Agentic Coding | D | Very compromised (needs ~0.5 GB host RAM) | 19.3 tok/s | 14578 ms | 8K |
| Reasoning | C | Runs with offload (needs ~0.2 GB host RAM) | 22.6 tok/s | 10112 ms | 8K |
| RAG | D | Very compromised (needs ~0.5 GB host RAM) | 19.3 tok/s | 18223 ms | 8K |
Quantization options
How StarCoder2 7B (7B params) fits at each quantization level on RTX 4050 Laptop 6GB (6.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 2.7 GB | Low | C53 |
Q3_K_SBest for your GPU | 3 | 3.4 GB | Low | C53 |
NVFP4 | 4 | 3.9 GB | Medium | F0 |
Q4_K_M | 4 | 4.3 GB | Medium | F0 |
Q5_K_M | 5 | 5.0 GB | High | F0 |
Q6_K | 6 | 5.7 GB | High | F0 |
Q8_0 | 8 | 7.5 GB | Very High | F0 |
F16 | 16 | 14.3 GB | Maximum | F0 |
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
8 GB VRAM (+2)448 GB/s (+256)
B
Raises estimated decode speed by about 209%.69.9 tok/s decode
Raises estimated decode speed by about 209%.
Adds memory headroom for longer context windows and future model growth.
~$299 MSRP
8 GB VRAM (+2)224 GB/s (+32)
C
Raises estimated decode speed by about 113%.48.1 tok/s decode
Raises estimated decode speed by about 113%.
Adds memory headroom for longer context windows and future model growth.
~$299 MSRP