MacBook Air M4 24GBGünstige Wahl
24 GB Unified (+8)
C
This setup is broadly balanced for this model.42 tok/s Dekodierung
ca. $1,099 MSRP
Can StarCoder2 3B run on RTX 4070 Ti Super 16GB?
YES — Runs Great
C46Usable
Estimated from fit model
StarCoder2 3B needs ~4.7 GB VRAM. RTX 4070 Ti Super 16GB has 16.0 GB. With Q4_K_M quantization, expect ~48 tok/s.
Runtime: llama.cppCapacity: RoomyBandwidth: MediumStack: StandardBottleneck: 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) — 4.7 GB, 48.0 tok/s, Runs well
4.7 GB required16.0 GB available
29% VRAM used
Fit status
Runs well
Decode
48.0 tok/s
TTFT
4033 ms
Safe context
531K
Memory
4.7 GB / 16.0 GB
Memory breakdown
Weights1.8 GB
KV Cache0.4 GB
Runtime0.9 GB
Headroom1.6 GB
See how fast it feels
See how fast it feelsStarCoder2 3B on RTX 4070 Ti Super 16GB
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: 48.0 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 | 48.0 tok/s | 2200 ms | 531K |
| Coding | C | Runs well | 48.0 tok/s | 4033 ms | 531K |
| Agentic Coding | C | Runs well | 48.0 tok/s | 5867 ms | 531K |
| Reasoning | C | Runs well | 48.0 tok/s | 4767 ms | 531K |
| RAG | C | Runs well | 48.0 tok/s | 7333 ms | 531K |
Quantization options
How StarCoder2 3B (3B params) fits at each quantization level on RTX 4070 Ti Super 16GB (16.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 1.2 GB | Low | C45 |
Q3_K_S | 3 | 1.5 GB | Low | C46 |
NVFP4 | 4 | 1.7 GB | Medium | C46 |
Q4_K_M | 4 | 1.8 GB | Medium | C46 |
Q5_K_M | 5 | 2.2 GB | High | C46 |
Q6_K | 6 | 2.5 GB | High | C46 |
Q8_0 | 8 | 3.2 GB | Very High | C47 |
F16Best for your GPU | 16 | 6.1 GB | Maximum | C50 |
Get started
Copy-paste commands to run StarCoder2 3B on your machine.
Run
lms load hf-second-state--starcoder2-3b-gguf && lms server startUpgrade-Optionen
Hardware, die StarCoder2 3B gut ausführt
Frequently asked questions
Can RTX 4070 Ti Super 16GB run StarCoder2 3B?
Yes, RTX 4070 Ti Super 16GB can run StarCoder2 3B with a C grade (Runs well). Expected decode speed: 48.0 tok/s.
How much VRAM does StarCoder2 3B need?
StarCoder2 3B (3B parameters) requires approximately 4.7 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 4070 Ti Super 16GB?
On RTX 4070 Ti Super 16GB, StarCoder2 3B achieves approximately 48.0 tokens per second decode speed with a time-to-first-token of 4033ms using Q4_K_M quantization.
Can RTX 4070 Ti Super 16GB run StarCoder2 3B for coding?
For coding workloads, StarCoder2 3B on RTX 4070 Ti Super 16GB receives a C grade with 48.0 tok/s and 531K context.
What context window can StarCoder2 3B use on RTX 4070 Ti Super 16GB?
On RTX 4070 Ti Super 16GB, StarCoder2 3B can safely use up to 531K 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--starcoder2-3b-gguf-on-rtx-4070-ti-super-16gb" 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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