Mac Studio M3 Ultra 256GB经济之选
256 GB Unified (+76)
B
This setup is broadly balanced for this model.70.4 tok/s 解码
~$6,999 MSRP
Can Qwen 2.5 Coder 14B run on NVIDIA B200 180GB?
YES — Runs Great
B60Good
Estimated from fit model
Qwen 2.5 Coder 14B needs ~30.7 GB VRAM. NVIDIA B200 180GB has 180.0 GB. With Q4_K_M quantization, expect ~196 tok/s.
Runtime: OllamaCapacity: RoomyBandwidth: HighStack: 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) — 30.7 GB, 196.0 tok/s, Runs well
30.7 GB required180.0 GB available
17% VRAM used
Fit status
Runs well
Decode
196.0 tok/s
TTFT
988 ms
Safe context
131K
Memory
30.7 GB / 180.0 GB
Memory breakdown
Weights8.5 GB
KV Cache2.9 GB
Runtime1.2 GB
Headroom18.0 GB
See how fast it feels
See how fast it feelsQwen 2.5 Coder 14B on NVIDIA B200 180GB
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: 196.0 tok/s decode · 988ms TTFT (warm) · 490 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 | B | Runs well | 196.0 tok/s | 539 ms | 131K |
| Coding | B | Runs well | 196.0 tok/s | 988 ms | 131K |
| Agentic Coding | B | Runs well | 196.0 tok/s | 1437 ms | 131K |
| Reasoning | B | Runs well | 196.0 tok/s | 1167 ms | 131K |
| RAG | B | Runs well | 196.0 tok/s | 1796 ms | 131K |
Quantization options
How Qwen 2.5 Coder 14B (14B params) fits at each quantization level on NVIDIA B200 180GB (180.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 5.5 GB | Low | C51 |
Q3_K_S | 3 | 6.9 GB | Low | C51 |
NVFP4 | 4 | 7.8 GB | Medium | C51 |
Q4_K_M | 4 | 8.5 GB | Medium | C51 |
Q5_K_M | 5 | 10.1 GB | High | C51 |
Q6_K | 6 | 11.5 GB | High | C52 |
Q8_0 | 8 | 15.0 GB | Very High | C52 |
F16Best for your GPU | 16 | 28.7 GB | Maximum | C53 |
Get started
Copy-paste commands to run Qwen 2.5 Coder 14B on your machine.
Run
ollama run qwen2.5-coder:14b升级选项
能流畅运行 Qwen 2.5 Coder 14B 的硬件
Frequently asked questions
Can NVIDIA B200 180GB run Qwen 2.5 Coder 14B?
Yes, NVIDIA B200 180GB can run Qwen 2.5 Coder 14B with a B grade (Runs well). Expected decode speed: 196.0 tok/s.
How much VRAM does Qwen 2.5 Coder 14B need?
Qwen 2.5 Coder 14B (14B parameters) requires approximately 30.7 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 NVIDIA B200 180GB?
On NVIDIA B200 180GB, Qwen 2.5 Coder 14B achieves approximately 196.0 tokens per second decode speed with a time-to-first-token of 988ms using Q4_K_M quantization.
Can NVIDIA B200 180GB run Qwen 2.5 Coder 14B for coding?
For coding workloads, Qwen 2.5 Coder 14B on NVIDIA B200 180GB receives a B grade with 196.0 tok/s and 131K context.
What context window can Qwen 2.5 Coder 14B use on NVIDIA B200 180GB?
On NVIDIA B200 180GB, Qwen 2.5 Coder 14B can safely use up to 131K 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-b200-180gb" 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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