RTX 3060 12GBOpção econômica12 GB VRAM (+4)360 GB/s (+72)
C
Adds memory headroom for longer context windows and future model growth.55.6 tok/s decodificação
Adds memory headroom for longer context windows and future model growth.
~$329 MSRP
Can StarCoder2 7B run on RTX 4060 Ti 8GB?
YES — Tight Fit
C52Usable
Estimated from fit model
StarCoder2 7B needs ~7.1 GB VRAM. RTX 4060 Ti 8GB has 8.0 GB. With Q4_K_M quantization, expect ~49 tok/s.
Runtime: OllamaCapacity: TightBandwidth: LowStack: BasicBottleneck: Balanced
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) — 7.1 GB, 49.2 tok/s, Tight fit
7.1 GB required8.0 GB available
89% VRAM used
Fit status
Tight fit
Decode
49.2 tok/s
TTFT
3932 ms
Safe context
34K
Memory
7.1 GB / 8.0 GB
Memory breakdown
Weights4.3 GB
KV Cache0.8 GB
Runtime1.2 GB
Headroom0.8 GB
See how fast it feels
See how fast it feelsStarCoder2 7B on RTX 4060 Ti 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: 49.2 tok/s decode · 3.9s TTFT (warm) · 123 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 | Tight fit | 49.2 tok/s | 2145 ms | 34K |
| Coding | C | Tight fit | 49.2 tok/s | 3932 ms | 34K |
| Agentic Coding | C | Runs with offload | 49.2 tok/s | 5719 ms | 34K |
| Reasoning | C | Tight fit | 49.2 tok/s | 4647 ms | 34K |
| RAG | C | Runs with offload | 49.2 tok/s | 7149 ms | 34K |
Quantization options
How StarCoder2 7B (7B params) fits at each quantization level on RTX 4060 Ti 8GB (8.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 2.7 GB | Low | C53 |
Q3_K_S | 3 | 3.4 GB | Low | C53 |
NVFP4 | 4 | 3.9 GB | Medium | C53 |
Q4_K_M | 4 | 4.3 GB | Medium | C53 |
Q5_K_MBest for your GPU | 5 | 5.0 GB | High | C53 |
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 hf-second-state--starcoder2-7b-gguf && lms server startOpções de upgrade
Hardware que roda bem StarCoder2 7B
RTX 3060 12GBOpção econômicaRTX 5070 12GBMelhor custo-benefício12 GB VRAM (+4)672 GB/s (+384)
B
Raises estimated decode speed by about 99%.98 tok/s decodificação
Raises estimated decode speed by about 99%.
Adds memory headroom for longer context windows and future model growth.
~$549 MSRP
RTX 4070 Super 12GBUpgrade NVIDIA12 GB VRAM (+4)504 GB/s (+216)
B
Raises estimated decode speed by about 85%.90.9 tok/s decodificação
Raises estimated decode speed by about 85%.
Adds memory headroom for longer context windows and future model growth.
~$599 MSRP
Frequently asked questions
Can RTX 4060 Ti 8GB run StarCoder2 7B?
Yes, RTX 4060 Ti 8GB can run StarCoder2 7B with a C grade (Tight fit). Expected decode speed: 49.2 tok/s.
How much VRAM does StarCoder2 7B need?
StarCoder2 7B (7B parameters) requires approximately 7.1 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 4060 Ti 8GB?
On RTX 4060 Ti 8GB, StarCoder2 7B achieves approximately 49.2 tokens per second decode speed with a time-to-first-token of 3932ms using Q4_K_M quantization.
Can RTX 4060 Ti 8GB run StarCoder2 7B for coding?
For coding workloads, StarCoder2 7B on RTX 4060 Ti 8GB receives a C grade with 49.2 tok/s and 34K context.
What context window can StarCoder2 7B use on RTX 4060 Ti 8GB?
On RTX 4060 Ti 8GB, StarCoder2 7B can safely use up to 34K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
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<iframe src="https://willitrunai.com/embed/hf-second-state--starcoder2-7b-gguf-on-rtx-4060-ti-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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