GTX 1660 Super 6GB经济之选6 GB VRAM (+2)336 GB/s (+144)
C
Adds memory headroom for longer context windows and future model growth.42 tok/s 解码
Adds memory headroom for longer context windows and future model growth.
~$229 MSRP
Can StarCoder2 3B run on RTX 3050 Ti Laptop 4GB?
YES — Tight Fit
C51Usable
Estimated from fit model
StarCoder2 3B needs ~3.8 GB VRAM. RTX 3050 Ti Laptop 4GB has 4.0 GB. With Q4_K_M quantization, expect ~42 tok/s.
Runtime: OllamaCapacity: TightBandwidth: Very lowStack: BasicBottleneck: 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) — 3.8 GB, 42.0 tok/s, Tight fit
3.8 GB required4.0 GB available
95% VRAM used
Fit status
Tight fit
Decode
42.0 tok/s
TTFT
4610 ms
Safe context
26K
Memory
3.8 GB / 4.0 GB
Memory breakdown
Weights1.8 GB
KV Cache0.4 GB
Runtime1.2 GB
Headroom0.4 GB
See how fast it feels
See how fast it feelsStarCoder2 3B on RTX 3050 Ti Laptop 4GB
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: 42.0 tok/s decode · 4.6s TTFT (warm) · 105 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 | Tight fit | 42.0 tok/s | 2514 ms | 26K |
| Coding | C | Tight fit | 42.0 tok/s | 4610 ms | 26K |
| Agentic Coding | C | Runs with offload (needs ~0.1 GB host RAM) | 42.0 tok/s | 6705 ms | 26K |
| Reasoning | C | Tight fit | 42.0 tok/s | 5448 ms | 26K |
| RAG | C | Runs with offload (needs ~0.1 GB host RAM) | 42.0 tok/s | 8381 ms | 26K |
Quantization options
How StarCoder2 3B (3B params) fits at each quantization level on RTX 3050 Ti Laptop 4GB (4.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 1.2 GB | Low | B55 |
Q3_K_S | 3 | 1.5 GB | Low | C55 |
NVFP4 | 4 | 1.7 GB | Medium | C55 |
Q4_K_MBest for your GPU | 4 | 1.8 GB | Medium | C55 |
Q5_K_M | 5 | 2.2 GB | High | F0 |
Q6_K | 6 | 2.5 GB | High | F0 |
Q8_0 | 8 | 3.2 GB | Very High | F0 |
F16 | 16 | 6.1 GB | Maximum | F0 |
Get started
Copy-paste commands to run StarCoder2 3B on your machine.
Run
lms load hf-second-state--starcoder2-3b-gguf && lms server start升级选项
能流畅运行 StarCoder2 3B 的硬件
GTX 1660 Super 6GB经济之选GTX 1060 6GB最佳性价比6 GB VRAM (+2)
C
Adds memory headroom for longer context windows and future model growth.42 tok/s 解码
Adds memory headroom for longer context windows and future model growth.
~$249 MSRP
GTX 1660 Ti 6GBNVIDIA 升级6 GB VRAM (+2)288 GB/s (+96)
C
Adds memory headroom for longer context windows and future model growth.42 tok/s 解码
Adds memory headroom for longer context windows and future model growth.
~$279 MSRP
Frequently asked questions
Can RTX 3050 Ti Laptop 4GB run StarCoder2 3B?
Yes, RTX 3050 Ti Laptop 4GB can run StarCoder2 3B with a C grade (Tight fit). Expected decode speed: 42.0 tok/s.
How much VRAM does StarCoder2 3B need?
StarCoder2 3B (3B parameters) requires approximately 3.8 GB of memory with Q4_K_M quantization.
What is the best quantization for StarCoder2 3B?
The recommended quantization for StarCoder2 3B is Q4_K_M, which balances quality and memory efficiency.
What speed will StarCoder2 3B run at on RTX 3050 Ti Laptop 4GB?
On RTX 3050 Ti Laptop 4GB, StarCoder2 3B achieves approximately 42.0 tokens per second decode speed with a time-to-first-token of 4610ms using Q4_K_M quantization.
Can RTX 3050 Ti Laptop 4GB run StarCoder2 3B for coding?
For coding workloads, StarCoder2 3B on RTX 3050 Ti Laptop 4GB receives a C grade with 42.0 tok/s and 26K context.
What context window can StarCoder2 3B use on RTX 3050 Ti Laptop 4GB?
On RTX 3050 Ti Laptop 4GB, StarCoder2 3B can safely use up to 26K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
What should I upgrade first if StarCoder2 3B feels slow on RTX 3050 Ti Laptop 4GB?
Buy headroom, not only minimum fit. A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
Embed this result▼
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<iframe src="https://willitrunai.com/embed/hf-second-state--starcoder2-3b-gguf-on-rtx-3050-ti-laptop-4gb" 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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