MacBook Air M4 24GBOpción económica
24 GB Unified (+8)
C
Este setup está bastante equilibrado para este modelo.42 tok/s decodificación
~$1,099 MSRP
Can StarCoder2 3B run on Tesla P100 16GB?
YES — Runs Great
C46Usable
Estimated from fit model
StarCoder2 3B needs ~5.0 GB VRAM. Tesla P100 16GB has 16.0 GB. With Q4_K_M quantization, expect ~42 tok/s.
Runtime: OllamaCapacity: RoomyBandwidth: MediumStack: 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) — 5.0 GB, 42.0 tok/s, Runs well
5.0 GB required16.0 GB available
31% VRAM used
Fit status
Runs well
Decode
42.0 tok/s
TTFT
4610 ms
Safe context
517K
Memory
5.0 GB / 16.0 GB
Memory breakdown
Weights1.8 GB
KV Cache0.4 GB
Runtime1.2 GB
Headroom1.6 GB
See how fast it feels
See how fast it feelsStarCoder2 3B on Tesla P100 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: 42.0 tok/s decode · 4.6s TTFT (warm) · 105 tok/s prefill
What limits this setup
This setup is broadly balanced for this model.
Older PCIe generation
PCIe 3.0 is workable, but it compounds the penalty when you offload heavily or try to scale across multiple cards.
Best improvement path
Performance by workload
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | C | Runs well | 42.0 tok/s | 2514 ms | 517K |
| Coding | C | Runs well | 42.0 tok/s | 4610 ms | 517K |
| Agentic Coding | C | Runs well | 42.0 tok/s | 6705 ms | 517K |
| Reasoning | C | Runs well | 42.0 tok/s | 5448 ms | 517K |
| RAG | C | Runs well | 42.0 tok/s | 8381 ms | 517K |
Quantization options
How StarCoder2 3B (3B params) fits at each quantization level on Tesla P100 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 startOpciones de mejora
Hardware que ejecuta bien StarCoder2 3B
Frequently asked questions
Can Tesla P100 16GB run StarCoder2 3B?
Yes, Tesla P100 16GB 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 5.0 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 Tesla P100 16GB?
On Tesla P100 16GB, 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 Tesla P100 16GB run StarCoder2 3B for coding?
For coding workloads, StarCoder2 3B on Tesla P100 16GB receives a C grade with 42.0 tok/s and 517K context.
What context window can StarCoder2 3B use on Tesla P100 16GB?
On Tesla P100 16GB, StarCoder2 3B can safely use up to 517K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
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