RTX PRO 6000 Blackwell Workstation Edition 96GBOpção econômica1792 GB/s (+992)
B
Raises estimated decode speed by about 242%.190.4 tok/s decodificação
Raises estimated decode speed by about 242%.
~$9,999 MSRP
Can Qwen 2.5 Coder 14B run on Mac Studio M1 Ultra 128GB?
YES — Runs Great
B61Good
Estimated from fit model
Qwen 2.5 Coder 14B needs ~26.2 GB VRAM. Mac Studio M1 Ultra 128GB has 92.2 GB. With Q4_K_M quantization, expect ~56 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) — 26.2 GB, 55.6 tok/s, Runs well
26.2 GB required92.2 GB available
28% VRAM used
Fit status
Runs well
Decode
55.6 tok/s
TTFT
3479 ms
Safe context
131K
Memory
26.2 GB / 92.2 GB
Memory breakdown
Weights8.5 GB
KV Cache2.9 GB
Runtime0.9 GB
Headroom13.8 GB
See how fast it feels
See how fast it feelsQwen 2.5 Coder 14B on Mac Studio M1 Ultra 128GB
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: 55.6 tok/s decode · 3.5s TTFT (warm) · 139 tok/s prefill
What limits this setup
This setup is broadly balanced for this model.
Shared-memory contention still exists
The OS, browser, and inference runtime all compete for the same physical memory pool, so real-world headroom is less forgiving than raw capacity suggests.
Best improvement path
Performance by workload
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | B | Runs well | 55.6 tok/s | 1898 ms | 131K |
| Coding | B | Runs well | 55.6 tok/s | 3479 ms | 131K |
| Agentic Coding | B | Runs well | 55.6 tok/s | 5061 ms | 131K |
| Reasoning | B | Runs well | 55.6 tok/s | 4112 ms | 131K |
| RAG | B | Runs well | 55.6 tok/s | 6326 ms | 131K |
Inference speed
Qwen 2.5 Coder 14B inference speed — tokens per second by GPU & Mac
Estimated decode speed (tokens/sec) for Qwen 2.5 Coder 14B at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~152 tok/s. Speed is memory-bandwidth bound, so cards that fit the whole model in VRAM run far faster than ones that offload to system RAM.
| GPU / Mac | Memory | Quant | Speed (tok/s) | Fits? |
|---|---|---|---|---|
| RTX 5090 32GB | 32 GB | Q4_K_M | 151.8 | Fits |
| RTX 4090 24GB | 24 GB | Q4_K_M | 96.9 | Fits |
RX 7900 XTX 24GB | 24 GB | Q4_K_M | 87.4 | Fits |
| RTX 3090 24GB | 24 GB | Q4_K_M | 82.9 | Fits |
| RTX 4080 Super 16GB | 16 GB | Q4_K_M | 81.1 | Tight |
Mac Studio M3 Ultra 256GB | 256 GB | Q4_K_M | 70.4 | Fits |
Mac Studio M2 Ultra 128GB | 128 GB | Q4_K_M | 58.7 | Fits |
Mac Studio M1 Ultra 128GB | 128 GB | Q4_K_M | 55.6 | Fits |
MacBook Pro M4 Max 128GB | 128 GB | Q4_K_M | 38.3 | Fits |
MacBook Pro M4 Max 64GB | 64 GB | Q4_K_M | 38.3 | Fits |
MacBook Pro M3 Max 64GB | 64 GB | Q4_K_M | 30.4 | Fits |
| RTX 4070 12GB | 12 GB | Q4_K_M | 29.1 | Heavy offload |
MacBook Pro M1 Max 64GB | 64 GB | Q4_K_M | 27.8 | Fits |
MacBook Pro M4 Pro 48GB | 48 GB | Q4_K_M | 23.4 | Fits |
| RTX 3060 12GB | 12 GB | Q4_K_M | 17.0 | Heavy offload |
| RTX 4060 8GB | 8 GB | Q4_K_M | 6.3 | Too big |
Estimates for single-stream decoding at Q4_K_M; real tokens/sec varies with prompt length, context, batch size, and runtime build. Prompt processing (prefill) is faster than the decode figures shown here.
Quantization options
How Qwen 2.5 Coder 14B (14B params) fits at each quantization level on Mac Studio M1 Ultra 128GB (92.2 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 5.5 GB | Low | C54 |
Q3_K_S | 3 | 6.9 GB | Low | C54 |
NVFP4 | 4 | 7.8 GB | Medium | C54 |
Q4_K_M | 4 | 8.5 GB | Medium | C54 |
Q5_K_M | 5 | 10.1 GB | High | C54 |
Q6_K | 6 | 11.5 GB | High | C54 |
Q8_0 | 8 | 15.0 GB | Very High | C54 |
F16Best for your GPU | 16 | 28.7 GB | Maximum | B56 |
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
Frequently asked questions
Can Mac Studio M1 Ultra 128GB run Qwen 2.5 Coder 14B?
Yes, Mac Studio M1 Ultra 128GB can run Qwen 2.5 Coder 14B with a B grade (Runs well). Expected decode speed: 55.6 tok/s.
How much VRAM does Qwen 2.5 Coder 14B need?
Qwen 2.5 Coder 14B (14B parameters) requires approximately 26.2 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 Mac Studio M1 Ultra 128GB?
On Mac Studio M1 Ultra 128GB, Qwen 2.5 Coder 14B achieves approximately 55.6 tokens per second decode speed with a time-to-first-token of 3479ms using Q4_K_M quantization.
Can Mac Studio M1 Ultra 128GB run Qwen 2.5 Coder 14B for coding?
For coding workloads, Qwen 2.5 Coder 14B on Mac Studio M1 Ultra 128GB receives a B grade with 55.6 tok/s and 131K context.
What context window can Qwen 2.5 Coder 14B use on Mac Studio M1 Ultra 128GB?
On Mac Studio M1 Ultra 128GB, 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.
Is unified memory on Mac Studio M1 Ultra 128GB as fast as VRAM for Qwen 2.5 Coder 14B?
Not always. Mac Studio M1 Ultra 128GB can often fit larger models thanks to unified memory, but a discrete GPU with dedicated high-bandwidth VRAM may still decode faster once the model fits. For this combination, the important distinction is capacity versus sustained throughput.
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