MacBook Pro M4 32GBOpção econômica
32 GB Unified (+16)
C
Adds memory headroom for longer context windows and future model growth.7 tok/s decodificação
Adds memory headroom for longer context windows and future model growth.
~$799 MSRP
Can Qwen 2.5 Coder 0.5B run on RTX 6000 Ada Laptop 16GB?
YES — Runs Great
C47Usable
Estimated from fit model
Qwen 2.5 Coder 0.5B needs ~3.3 GB VRAM. RTX 6000 Ada Laptop 16GB has 16.0 GB. With Q4_K_M quantization, expect ~7 tok/s.
Runtime: OllamaCapacity: RoomyBandwidth: MediumStack: BasicBottleneck: Memory bandwidth
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) — 3.3 GB, 7.0 tok/s, Runs well
3.3 GB required16.0 GB available
21% VRAM used
Fit status
Runs well
Decode
7.0 tok/s
TTFT
27657 ms
Safe context
131K
Memory
3.3 GB / 16.0 GB
Memory breakdown
Weights0.3 GB
KV Cache0.2 GB
Runtime1.2 GB
Headroom1.6 GB
See how fast it feels
See how fast it feelsQwen 2.5 Coder 0.5B on RTX 6000 Ada Laptop 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: 7.0 tok/s decode · 27.7s TTFT (warm) · 18 tok/s prefill
What limits this setup
This model fits, but memory bandwidth is the part holding decode speed back.
Throughput will feel slow
Estimated decode speed is only 7.0 tok/s, so this is more of a technical fit than a comfortable daily-driver setup.
Best improvement path
Prioritize bandwidth, not only capacity
If this workload feels slow, the next useful step is often a GPU tier with materially faster memory bandwidth rather than only a small bump in capacity.
Performance by workload
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | C | Runs well | 7.0 tok/s | 15086 ms | 131K |
| Coding | C | Runs well | 7.0 tok/s | 27657 ms | 131K |
| Agentic Coding | C | Runs well | 7.0 tok/s | 40229 ms | 131K |
| Reasoning | C | Runs well | 7.0 tok/s | 32686 ms | 131K |
| RAG | C | Runs well | 7.0 tok/s | 50286 ms | 131K |
Quantization options
How Qwen 2.5 Coder 0.5B (0.5B params) fits at each quantization level on RTX 6000 Ada Laptop 16GB (16.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 0.2 GB | Low | C54 |
Q3_K_S | 3 | 0.2 GB | Low | C54 |
NVFP4 | 4 | 0.3 GB | Medium | C54 |
Q4_K_M | 4 | 0.3 GB | Medium | C54 |
Q5_K_M | 5 | 0.4 GB | High | C54 |
Q6_K | 6 | 0.4 GB | High | C54 |
Q8_0 | 8 | 0.5 GB | Very High | C54 |
F16Best for your GPU | 16 | 1.0 GB | Maximum | C54 |
Get started
Copy-paste commands to run Qwen 2.5 Coder 0.5B on your machine.
Run
ollama run qwen2.5-coder:0.5bOpções de upgrade
Hardware que roda bem Qwen 2.5 Coder 0.5B
Frequently asked questions
Can RTX 6000 Ada Laptop 16GB run Qwen 2.5 Coder 0.5B?
Yes, RTX 6000 Ada Laptop 16GB can run Qwen 2.5 Coder 0.5B with a C grade (Runs well). Expected decode speed: 7.0 tok/s.
How much VRAM does Qwen 2.5 Coder 0.5B need?
Qwen 2.5 Coder 0.5B (0.5B parameters) requires approximately 3.3 GB of memory with Q4_K_M quantization.
What is the best quantization for Qwen 2.5 Coder 0.5B?
The recommended quantization for Qwen 2.5 Coder 0.5B is Q4_K_M, which balances quality and memory efficiency.
What speed will Qwen 2.5 Coder 0.5B run at on RTX 6000 Ada Laptop 16GB?
On RTX 6000 Ada Laptop 16GB, Qwen 2.5 Coder 0.5B achieves approximately 7.0 tokens per second decode speed with a time-to-first-token of 27657ms using Q4_K_M quantization.
Can RTX 6000 Ada Laptop 16GB run Qwen 2.5 Coder 0.5B for coding?
For coding workloads, Qwen 2.5 Coder 0.5B on RTX 6000 Ada Laptop 16GB receives a C grade with 7.0 tok/s and 131K context.
What context window can Qwen 2.5 Coder 0.5B use on RTX 6000 Ada Laptop 16GB?
On RTX 6000 Ada Laptop 16GB, Qwen 2.5 Coder 0.5B can safely use up to 131K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.
What should I upgrade first if Qwen 2.5 Coder 0.5B feels slow on RTX 6000 Ada Laptop 16GB?
Prioritize bandwidth, not only capacity. If this workload feels slow, the next useful step is often a GPU tier with materially faster memory bandwidth rather than only a small bump in capacity.
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