RTX 4000 Ada 20GB经济之选20 GB VRAM (+4)360 GB/s (+160)
C
Raises estimated decode speed by about 80%.71.8 tok/s 解码
Raises estimated decode speed by about 80%.
Adds memory headroom for longer context windows and future model growth.
~$1,250 MSRP
Can StarCoder2 7B run on NVIDIA A2 16GB?
YES — Runs Great
C49Usable
Estimated from fit model
StarCoder2 7B needs ~7.6 GB VRAM. NVIDIA A2 16GB has 16.0 GB. With Q4_K_M quantization, expect ~40 tok/s.
Runtime: OllamaCapacity: RoomyBandwidth: 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) — 7.6 GB, 39.9 tok/s, Runs well
7.6 GB required16.0 GB available
48% VRAM used
Fit status
Runs well
Decode
39.9 tok/s
TTFT
4854 ms
Safe context
16K
Memory
7.6 GB / 16.0 GB
Memory breakdown
Weights4.3 GB
KV Cache0.5 GB
Runtime1.2 GB
Headroom1.6 GB
See how fast it feels
See how fast it feelsStarCoder2 7B on NVIDIA A2 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: 39.9 tok/s decode · 4.9s TTFT (warm) · 100 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 | 39.9 tok/s | 2648 ms | 16K |
| Coding | C | Runs well | 39.9 tok/s | 4854 ms | 16K |
| Agentic Coding | C | Runs well | 39.9 tok/s | 7061 ms | 16K |
| Reasoning | C | Runs well | 39.9 tok/s | 5737 ms | 16K |
| RAG | C | Runs well | 39.9 tok/s | 8826 ms | 16K |
Quantization options
How StarCoder2 7B (7B params) fits at each quantization level on NVIDIA A2 16GB (16.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 2.7 GB | Low | C46 |
Q3_K_S | 3 | 3.4 GB | Low | C47 |
NVFP4 | 4 | 3.9 GB | Medium | C47 |
Q4_K_M | 4 | 4.3 GB | Medium | C47 |
Q5_K_M | 5 | 5.0 GB | High | C48 |
Q6_K | 6 | 5.7 GB | High | C49 |
Q8_0Best for your GPU | 8 | 7.5 GB | Very High | C51 |
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升级选项
能流畅运行 StarCoder2 7B 的硬件
RTX 4000 Ada 20GB经济之选RTX 3090 24GB最佳性价比24 GB VRAM (+8)936 GB/s (+736)
C
Raises estimated decode speed by about 146%.98 tok/s 解码
Raises estimated decode speed by about 146%.
Adds memory headroom for longer context windows and future model growth.
~$1,499 MSRP
RTX PRO 4000 Blackwell 24GBNVIDIA 升级24 GB VRAM (+8)672 GB/s (+472)
C
Raises estimated decode speed by about 146%.98 tok/s 解码
Raises estimated decode speed by about 146%.
Adds memory headroom for longer context windows and future model growth.
~$1,599 MSRP
Frequently asked questions
Can NVIDIA A2 16GB run StarCoder2 7B?
Yes, NVIDIA A2 16GB can run StarCoder2 7B with a C grade (Runs well). Expected decode speed: 39.9 tok/s.
How much VRAM does StarCoder2 7B need?
StarCoder2 7B (7B parameters) requires approximately 7.6 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 NVIDIA A2 16GB?
On NVIDIA A2 16GB, StarCoder2 7B achieves approximately 39.9 tokens per second decode speed with a time-to-first-token of 4854ms using Q4_K_M quantization.
Can NVIDIA A2 16GB run StarCoder2 7B for coding?
For coding workloads, StarCoder2 7B on NVIDIA A2 16GB receives a C grade with 39.9 tok/s and 16K context.
What context window can StarCoder2 7B use on NVIDIA A2 16GB?
On NVIDIA A2 16GB, 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.
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