Mac mini M4 64GBコスパ重視
64 GB Unified (+24)
A
This setup is broadly balanced for this model.42 tok/sデコード
〜$1,099 MSRP
Can Qwen 2.5 Coder 3B run on NVIDIA A100 40GB?
YES — Runs Great
B70Good
Estimated from fit model
Qwen 2.5 Coder 3B needs ~9.2 GB VRAM. NVIDIA A100 40GB has 40.0 GB. With Q4_K_M quantization, expect ~42 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) — 9.2 GB, 42.0 tok/s, Runs well
9.2 GB required40.0 GB available
23% VRAM used
Fit status
Runs well
Decode
42.0 tok/s
TTFT
4610 ms
Safe context
131K
Memory
9.2 GB / 40.0 GB
Memory breakdown
Weights1.8 GB
KV Cache2.2 GB
Runtime1.2 GB
Headroom4.0 GB
See how fast it feels
See how fast it feelsQwen 2.5 Coder 3B on NVIDIA A100 40GB
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.
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 | 42.0 tok/s | 2514 ms | 131K |
| Coding | B | Runs well | 42.0 tok/s | 4610 ms | 131K |
| Agentic Coding | A | Runs well | 42.0 tok/s | 6705 ms | 131K |
| Reasoning | B | Runs well | 42.0 tok/s | 5448 ms | 131K |
| RAG | A | Runs well | 42.0 tok/s | 8381 ms | 131K |
Quantization options
How Qwen 2.5 Coder 3B (3B params) fits at each quantization level on NVIDIA A100 40GB (40.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 1.2 GB | Low | B68 |
Q3_K_S | 3 | 1.5 GB | Low | B68 |
NVFP4 | 4 | 1.7 GB | Medium | B68 |
Q4_K_M | 4 | 1.8 GB | Medium | B68 |
Q5_K_M | 5 | 2.2 GB | High | B68 |
Q6_K | 6 | 2.5 GB | High | B68 |
Q8_0 | 8 | 3.2 GB | Very High | B68 |
F16Best for your GPU | 16 | 6.1 GB | Maximum | B68 |
Get started
Copy-paste commands to run Qwen 2.5 Coder 3B on your machine.
Run
ollama run qwen2.5-coder:3bアップグレードオプション
Qwen 2.5 Coder 3Bを快適に動かすハードウェア
Frequently asked questions
Can NVIDIA A100 40GB run Qwen 2.5 Coder 3B?
Yes, NVIDIA A100 40GB can run Qwen 2.5 Coder 3B with a B grade (Runs well). Expected decode speed: 42.0 tok/s.
How much VRAM does Qwen 2.5 Coder 3B need?
Qwen 2.5 Coder 3B (3B parameters) requires approximately 9.2 GB of memory with Q4_K_M quantization.
What is the best quantization for Qwen 2.5 Coder 3B?
The recommended quantization for Qwen 2.5 Coder 3B is Q4_K_M, which balances quality and memory efficiency.
What speed will Qwen 2.5 Coder 3B run at on NVIDIA A100 40GB?
On NVIDIA A100 40GB, Qwen 2.5 Coder 3B achieves approximately 42.0 tokens per second decode speed with a time-to-first-token of 4610ms using Q4_K_M quantization.
Can NVIDIA A100 40GB run Qwen 2.5 Coder 3B for coding?
For coding workloads, Qwen 2.5 Coder 3B on NVIDIA A100 40GB receives a B grade with 42.0 tok/s and 131K context.
What context window can Qwen 2.5 Coder 3B use on NVIDIA A100 40GB?
On NVIDIA A100 40GB, Qwen 2.5 Coder 3B 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-3b-on-a100-40gb" 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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