MacBook Pro M3 Pro 36GB经济之选
36 GB Unified (+12)
C
This setup is broadly balanced for this model.42 tok/s 解码
~$1,999 MSRP
Can StarCoder2 3B run on NVIDIA L4 24GB?
YES — Runs Great
C43Usable
Estimated from fit model
StarCoder2 3B needs ~5.6 GB VRAM. NVIDIA L4 24GB has 24.0 GB. With Q4_K_M quantization, expect ~42 tok/s.
Runtime: llama.cppCapacity: RoomyBandwidth: LowStack: 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) — 5.6 GB, 48.0 tok/s, Runs well
5.6 GB required24.0 GB available
23% VRAM used
Fit status
Runs well
Decode
48.0 tok/s
TTFT
4033 ms
Safe context
16K
Memory
5.6 GB / 24.0 GB
Memory breakdown
Weights1.8 GB
KV Cache0.5 GB
Runtime0.9 GB
Headroom2.4 GB
See how fast it feels
See how fast it feelsStarCoder2 3B on NVIDIA L4 24GB
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 | 16K |
| Coding | C | Runs well | 42.0 tok/s | 4610 ms | 16K |
| Agentic Coding | C | Runs well | 48.0 tok/s | 5867 ms | 16K |
| Reasoning | C | Runs well | 48.0 tok/s | 4767 ms | 16K |
| RAG | C | Runs well | 48.0 tok/s | 7333 ms | 16K |
Quantization options
How StarCoder2 3B (3B params) fits at each quantization level on NVIDIA L4 24GB (24.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 1.2 GB | Low | C42 |
Q3_K_S | 3 | 1.5 GB | Low | C42 |
NVFP4 | 4 | 1.7 GB | Medium | C42 |
Q4_K_M | 4 | 1.8 GB | Medium | C42 |
Q5_K_M | 5 | 2.2 GB | High | C43 |
Q6_K | 6 | 2.5 GB | High | C43 |
Q8_0 | 8 | 3.2 GB | Very High | C43 |
F16Best for your GPU | 16 | 6.1 GB | Maximum | C45 |
Get started
Copy-paste commands to run StarCoder2 3B on your machine.
Run
ollama run starcoder2:3b升级选项
能流畅运行 StarCoder2 3B 的硬件
Frequently asked questions
Can NVIDIA L4 24GB run StarCoder2 3B?
Yes, NVIDIA L4 24GB can run StarCoder2 3B with a C grade (Runs well). Expected decode speed: 42.0 tok/s.
How much VRAM does StarCoder2 3B need?
StarCoder2 3B (3B parameters) requires approximately 5.6 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 NVIDIA L4 24GB?
On NVIDIA L4 24GB, 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 NVIDIA L4 24GB run StarCoder2 3B for coding?
For coding workloads, StarCoder2 3B on NVIDIA L4 24GB receives a C grade with 42.0 tok/s and 16K context.
What context window can StarCoder2 3B use on NVIDIA L4 24GB?
On NVIDIA L4 24GB, StarCoder2 3B 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/starcoder2-3b-on-l4-24gb" 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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