RTX 4000 Ada 20GBOpção econômica20 GB VRAM (+4)
B
Adds memory headroom for longer context windows and future model growth.35.5 tok/s decodificação
Adds memory headroom for longer context windows and future model growth.
~$1,250 MSRP
Can Qwen 2.5 Coder 14B run on Tesla P100 16GB?
YES — Tight Fit
B66Good
Estimated from fit model
Qwen 2.5 Coder 14B needs ~14.3 GB VRAM. Tesla P100 16GB has 16.0 GB. With Q4_K_M quantization, expect ~55 tok/s.
Runtime: OllamaCapacity: TightBandwidth: 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) — 14.3 GB, 54.6 tok/s, Tight fit
14.3 GB required16.0 GB available
89% VRAM used
Fit status
Tight fit
Decode
54.6 tok/s
TTFT
3545 ms
Safe context
25K
Memory
14.3 GB / 16.0 GB
Memory breakdown
Weights8.5 GB
KV Cache2.9 GB
Runtime1.2 GB
Headroom1.6 GB
See how fast it feels
See how fast it feelsQwen 2.5 Coder 14B 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: 54.6 tok/s decode · 3.5s TTFT (warm) · 137 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 | B | Runs well | 54.6 tok/s | 1933 ms | 25K |
| Coding | B | Tight fit | 54.6 tok/s | 3545 ms | 25K |
| Agentic Coding | C | Runs with offload (needs ~0.6 GB host RAM) | 34.0 tok/s | 8285 ms | 25K |
| Reasoning | B | Tight fit | 54.6 tok/s | 4189 ms | 25K |
| RAG | C | Runs with offload (needs ~0.6 GB host RAM) | 34.0 tok/s | 10356 ms | 25K |
Quantization options
How Qwen 2.5 Coder 14B (14B params) fits at each quantization level on Tesla P100 16GB (16.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 5.5 GB | Low | B64 |
Q3_K_S | 3 | 6.9 GB | Low | B65 |
NVFP4 | 4 | 7.8 GB | Medium | B66 |
Q4_K_M | 4 | 8.5 GB | Medium | B66 |
Q5_K_M | 5 | 10.1 GB | High | B65 |
Q6_KBest for your GPU | 6 | 11.5 GB | High | B65 |
Q8_0 | 8 | 15.0 GB | Very High | F0 |
F16 | 16 | 28.7 GB | Maximum | F0 |
Get started
Copy-paste commands to run Qwen 2.5 Coder 14B on your machine.
Run
ollama run qwen2.5-coder:14bOpções de upgrade
Hardware que roda bem Qwen 2.5 Coder 14B
RTX 4000 Ada 20GBOpção econômicaRTX 3090 24GBMelhor custo-benefício24 GB VRAM (+8)936 GB/s (+204)
B
Raises estimated decode speed by about 52%.82.9 tok/s decodificação
Raises estimated decode speed by about 52%.
Adds memory headroom for longer context windows and future model growth.
~$1,499 MSRP
RTX PRO 4000 Blackwell 24GBUpgrade NVIDIA24 GB VRAM (+8)
B
Raises estimated decode speed by about 31%.71.4 tok/s decodificação
Raises estimated decode speed by about 31%.
Adds memory headroom for longer context windows and future model growth.
~$1,599 MSRP
Frequently asked questions
Can Tesla P100 16GB run Qwen 2.5 Coder 14B?
Yes, Tesla P100 16GB can run Qwen 2.5 Coder 14B with a B grade (Tight fit). Expected decode speed: 54.6 tok/s.
How much VRAM does Qwen 2.5 Coder 14B need?
Qwen 2.5 Coder 14B (14B parameters) requires approximately 14.3 GB of memory with Q4_K_M quantization.
What is the best quantization for Qwen 2.5 Coder 14B?
The recommended quantization for Qwen 2.5 Coder 14B is Q4_K_M, which balances quality and memory efficiency.
What speed will Qwen 2.5 Coder 14B run at on Tesla P100 16GB?
On Tesla P100 16GB, Qwen 2.5 Coder 14B achieves approximately 54.6 tokens per second decode speed with a time-to-first-token of 3545ms using Q4_K_M quantization.
Can Tesla P100 16GB run Qwen 2.5 Coder 14B for coding?
For coding workloads, Qwen 2.5 Coder 14B on Tesla P100 16GB receives a B grade with 54.6 tok/s and 25K context.
What context window can Qwen 2.5 Coder 14B use on Tesla P100 16GB?
On Tesla P100 16GB, Qwen 2.5 Coder 14B can safely use up to 25K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.
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<iframe src="https://willitrunai.com/embed/qwen-2.5-coder-14b-on-tesla-p100-16gb" 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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