RTX 3080 10GBコスパ重視10 GB VRAM (+2)760 GB/s (+536)
B
Raises estimated decode speed by about 75%.84 tok/sデコード
Raises estimated decode speed by about 75%.
Adds memory headroom for longer context windows and future model growth.
〜$699 MSRP
Can StarCoder2 7B run on RTX 5050 8GB?
YES — Runs Great
C54Usable
Estimated from fit model
StarCoder2 7B needs ~6.5 GB VRAM. RTX 5050 8GB has 8.0 GB. With Q4_K_M quantization, expect ~48 tok/s.
Runtime: llama.cppCapacity: RoomyBandwidth: Very lowStack: StandardBottleneck: Memory bandwidth
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, 48.1 tok/s, Runs well
6.5 GB required8.0 GB available
81% VRAM used
Fit status
Runs well
Decode
48.1 tok/s
TTFT
4025 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 5050 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: 48.1 tok/s decode · 4.0s TTFT (warm) · 120 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 | 48.1 tok/s | 2195 ms | 16K |
| Coding | C | Runs well | 48.1 tok/s | 4025 ms | 16K |
| Agentic Coding | C | Tight fit | 48.1 tok/s | 5854 ms | 16K |
| Reasoning | C | Runs well | 48.1 tok/s | 4756 ms | 16K |
| RAG | C | Tight fit | 48.1 tok/s | 7317 ms | 16K |
Quantization options
How StarCoder2 7B (7B params) fits at each quantization level on RTX 5050 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 | 3.9 GB | Medium | C53 |
Q4_K_M | 4 | 4.3 GB | Medium | C52 |
Q5_K_MBest for your GPU | 5 | 5.0 GB | High | C52 |
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 startアップグレードオプション
StarCoder2 7Bを快適に動かすハードウェア
Frequently asked questions
Can RTX 5050 8GB run StarCoder2 7B?
Yes, RTX 5050 8GB can run StarCoder2 7B with a C grade (Runs well). Expected decode speed: 48.1 tok/s.
How much VRAM does StarCoder2 7B need?
StarCoder2 7B (7B parameters) requires approximately 6.5 GB of memory with Q4_K_M quantization.
What is the best quantization for StarCoder2 7B?
The recommended quantization for StarCoder2 7B is Q4_K_M, which balances quality and memory efficiency.
What speed will StarCoder2 7B run at on RTX 5050 8GB?
On RTX 5050 8GB, StarCoder2 7B achieves approximately 48.1 tokens per second decode speed with a time-to-first-token of 4025ms using Q4_K_M quantization.
Can RTX 5050 8GB run StarCoder2 7B for coding?
For coding workloads, StarCoder2 7B on RTX 5050 8GB receives a C grade with 48.1 tok/s and 16K context.
What context window can StarCoder2 7B use on RTX 5050 8GB?
On RTX 5050 8GB, StarCoder2 7B can safely use up to 16K tokens of context. The model's official context limit is 16K, but available memory constrains the safe maximum.
Embed this result▼
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<iframe src="https://willitrunai.com/embed/starcoder2-7b-on-rtx-5050-8gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
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