RTX 4000 Ada 20GB经济之选20 GB VRAM (+4)360 GB/s (+160)
C
Raises estimated decode speed by about 80%.29 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 15B run on NVIDIA A2 16GB?
YES — Tight Fit
C50Usable
Estimated from fit model
StarCoder2 15B needs ~14.8 GB VRAM. NVIDIA A2 16GB has 16.0 GB. With Q5_K_M quantization, expect ~16 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
Q5_K_M (High quality) — 14.8 GB, 16.1 tok/s, Tight fit
14.8 GB required16.0 GB available
93% VRAM used
Fit status
Tight fit
Decode
16.1 tok/s
TTFT
12037 ms
Safe context
16K
Memory
14.8 GB / 16.0 GB
Memory breakdown
Weights10.8 GB
KV Cache1.2 GB
Runtime1.2 GB
Headroom1.6 GB
See how fast it feels
See how fast it feelsStarCoder2 15B 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: 16.1 tok/s decode · 12.0s TTFT (warm) · 40 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 | 16.1 tok/s | 6566 ms | 16K |
| Coding | C | Tight fit | 16.1 tok/s | 12037 ms | 16K |
| Agentic Coding | C | Runs with offload (needs ~0 GB host RAM) | 12.0 tok/s | 23472 ms | 16K |
| Reasoning | C | Tight fit | 16.1 tok/s | 14225 ms | 16K |
| RAG | C | Runs with offload (needs ~0 GB host RAM) | 12.0 tok/s | 29340 ms | 16K |
Quantization options
How StarCoder2 15B (15B params) fits at each quantization level on NVIDIA A2 16GB (16.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 5.9 GB | Low | C51 |
Q3_K_S | 3 | 7.4 GB | Low | C53 |
NVFP4 | 4 | 8.4 GB | Medium | C53 |
Q4_K_M | 4 | 9.2 GB | Medium | C53 |
Q5_K_M | 5 | 10.8 GB | High | C52 |
Q6_KBest for your GPU | 6 | 12.3 GB | High | C52 |
Q8_0 | 8 | 16.1 GB | Very High | F0 |
F16 | 16 | 30.7 GB | Maximum | F0 |
Get started
Copy-paste commands to run StarCoder2 15B on your machine.
Run
docker run --rm -it ghcr.io/ggerganov/llama.cpp:full \
--hf-repo "bigcode/starcoder2-15b" \
--hf-file "starcoder2-15b-Q5_K_M.gguf" \
-c 4096 -ngl 99升级选项
能流畅运行 StarCoder2 15B 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)
B
Raises estimated decode speed by about 320%.67.6 tok/s 解码
Raises estimated decode speed by about 320%.
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)
B
Raises estimated decode speed by about 261%.58.2 tok/s 解码
Raises estimated decode speed by about 261%.
Adds memory headroom for longer context windows and future model growth.
~$1,599 MSRP
Frequently asked questions
Can NVIDIA A2 16GB run StarCoder2 15B?
Yes, NVIDIA A2 16GB can run StarCoder2 15B with a C grade (Tight fit). Expected decode speed: 16.1 tok/s.
How much VRAM does StarCoder2 15B need?
StarCoder2 15B (15B parameters) requires approximately 14.8 GB of memory with Q5_K_M quantization.
What is the best quantization for StarCoder2 15B?
The recommended quantization for StarCoder2 15B is Q5_K_M, which balances quality and memory efficiency.
What speed will StarCoder2 15B run at on NVIDIA A2 16GB?
On NVIDIA A2 16GB, StarCoder2 15B achieves approximately 16.1 tokens per second decode speed with a time-to-first-token of 12037ms using Q5_K_M quantization.
Can NVIDIA A2 16GB run StarCoder2 15B for coding?
For coding workloads, StarCoder2 15B on NVIDIA A2 16GB receives a C grade with 16.1 tok/s and 16K context.
What context window can StarCoder2 15B use on NVIDIA A2 16GB?
On NVIDIA A2 16GB, StarCoder2 15B can safely use up to 16K tokens of context. The model's official context limit is 16K, but available memory constrains the safe maximum.
What should I upgrade first if StarCoder2 15B feels slow on NVIDIA A2 16GB?
Buy headroom, not only minimum fit. A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
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<iframe src="https://willitrunai.com/embed/starcoder2-15b-on-a2-16gb" 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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