MacBook Pro M4 Max 96GBコスパ重視
96 GB Unified (+48)
B
Adds memory headroom for longer context windows and future model growth.21 tok/sデコード
Adds memory headroom for longer context windows and future model growth.
〜$2,499 MSRP
Can Qwen 2.5 Coder 1.5B run on NVIDIA L20 48GB?
YES — Runs Great
B60Good
Estimated from fit model
Qwen 2.5 Coder 1.5B needs ~7.0 GB VRAM. NVIDIA L20 48GB has 48.0 GB. With Q4_K_M quantization, expect ~24 tok/s.
Runtime: llama.cppCapacity: RoomyBandwidth: HighStack: StandardBottleneck: 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) — 7.0 GB, 24.0 tok/s, Runs well
7.0 GB required48.0 GB available
15% VRAM used
Fit status
Runs well
Decode
24.0 tok/s
TTFT
8067 ms
Safe context
33K
Memory
7.0 GB / 48.0 GB
Memory breakdown
Weights0.9 GB
KV Cache0.4 GB
Runtime0.9 GB
Headroom4.8 GB
See how fast it feels
See how fast it feelsQwen 2.5 Coder 1.5B on NVIDIA L20 48GB
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: 24.0 tok/s decode · 8.1s TTFT (warm) · 60 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 | 24.0 tok/s | 4400 ms | 33K |
| Coding | B | Runs well | 24.0 tok/s | 8067 ms | 33K |
| Agentic Coding | B | Runs well | 24.0 tok/s | 11733 ms | 33K |
| Reasoning | B | Runs well | 24.0 tok/s | 9533 ms | 33K |
| RAG | B | Runs well | 24.0 tok/s | 14667 ms | 33K |
Quantization options
How Qwen 2.5 Coder 1.5B (1.5B params) fits at each quantization level on NVIDIA L20 48GB (48.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 0.6 GB | Low | B60 |
Q3_K_S | 3 | 0.7 GB | Low | B60 |
NVFP4 | 4 | 0.8 GB | Medium | B60 |
Q4_K_M | 4 | 0.9 GB | Medium | B60 |
Q5_K_M | 5 | 1.1 GB | High | B60 |
Q6_K | 6 | 1.2 GB | High | B60 |
Q8_0 | 8 | 1.6 GB | Very High | B60 |
F16Best for your GPU | 16 | 3.1 GB | Maximum | B60 |
Get started
Copy-paste commands to run Qwen 2.5 Coder 1.5B on your machine.
Run
ollama run qwen2.5-coder:1.5bアップグレードオプション
Qwen 2.5 Coder 1.5Bを快適に動かすハードウェア
Frequently asked questions
Can NVIDIA L20 48GB run Qwen 2.5 Coder 1.5B?
Yes, NVIDIA L20 48GB can run Qwen 2.5 Coder 1.5B with a B grade (Runs well). Expected decode speed: 24.0 tok/s.
How much VRAM does Qwen 2.5 Coder 1.5B need?
Qwen 2.5 Coder 1.5B (1.5B parameters) requires approximately 7.0 GB of memory with Q4_K_M quantization.
What is the best quantization for Qwen 2.5 Coder 1.5B?
The recommended quantization for Qwen 2.5 Coder 1.5B is Q4_K_M, which balances quality and memory efficiency.
What speed will Qwen 2.5 Coder 1.5B run at on NVIDIA L20 48GB?
On NVIDIA L20 48GB, Qwen 2.5 Coder 1.5B achieves approximately 24.0 tokens per second decode speed with a time-to-first-token of 8067ms using Q4_K_M quantization.
Can NVIDIA L20 48GB run Qwen 2.5 Coder 1.5B for coding?
For coding workloads, Qwen 2.5 Coder 1.5B on NVIDIA L20 48GB receives a B grade with 24.0 tok/s and 33K context.
What context window can Qwen 2.5 Coder 1.5B use on NVIDIA L20 48GB?
On NVIDIA L20 48GB, Qwen 2.5 Coder 1.5B can safely use up to 33K tokens of context. The model's official context limit is 33K, but available memory constrains the safe maximum.
Embed this result▼
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<iframe src="https://willitrunai.com/embed/qwen-2.5-coder-1.5b-on-l20-48gb" 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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