Can Qwen 2.5 Coder 14B run on MacBook Pro M4 Max 64GB?

YES — Runs Great

B62Good

Estimated from fit model

Qwen 2.5 Coder 14B needs ~19.3 GB VRAM. MacBook Pro M4 Max 64GB has 46.1 GB. With Q4_K_M quantization, expect ~38 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: MediumStack: 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.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 19.3 GB, 38.3 tok/s, Runs well

19.3 GB required46.1 GB available

42% VRAM used

Fit status

Runs well

Decode

38.3 tok/s

TTFT

5057 ms

Safe context

131K

Memory

19.3 GB / 46.1 GB

Memory breakdown

Weights8.5 GB

KV Cache2.9 GB

Runtime0.9 GB

Headroom6.9 GB

See how fast it feels

See how fast it feelsQwen 2.5 Coder 14B on MacBook Pro M4 Max 64GB
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: 38.3 tok/s decode · 5.1s TTFT (warm) · 96 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	38.3 tok/s	2759 ms	131K
Coding	B	Runs well	38.3 tok/s	5057 ms	131K
Agentic Coding	B	Runs well	38.3 tok/s	7356 ms	131K
Reasoning	B	Runs well	38.3 tok/s	5977 ms	131K
RAG	B	Runs well	38.3 tok/s	9195 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 MacBook Pro M4 Max 64GB (46.1 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	5.5 GB	Low	B56
Q3_K_S	3	6.9 GB	Low	B57
NVFP4	4	7.8 GB	Medium	B57
Q4_K_M	4	8.5 GB	Medium	B57
Q5_K_M	5	10.1 GB	High	B57
Q6_K	6	11.5 GB	High	B58
Q8_0	8	15.0 GB	Very High	B59
F16Best for your GPU	16	28.7 GB	Maximum	B62

Get started

Copy-paste commands to run Qwen 2.5 Coder 14B on your machine.

Run

ollama run qwen2.5-coder:14b

アップグレードオプション

Qwen 2.5 Coder 14Bを快適に動かすハードウェア

Mac Studio M3 Ultra 96GBコスパ重視

96 GB Unified (+32)819 GB/s (+273)

Raises estimated decode speed by about 84%.70.4 tok/sデコード

Raises estimated decode speed by about 84%.

Adds memory headroom for longer context windows and future model growth.

〜$3,999 MSRP

Radeon Pro W7900 48GBコスパ最良

864 GB/s (+318)

Raises estimated decode speed by about 68%.64.5 tok/sデコード

Raises estimated decode speed by about 68%.

〜$3,999 MSRP

Frequently asked questions

Can MacBook Pro M4 Max 64GB run Qwen 2.5 Coder 14B?

Yes, MacBook Pro M4 Max 64GB can run Qwen 2.5 Coder 14B with a B grade (Runs well). Expected decode speed: 38.3 tok/s.

How much VRAM does Qwen 2.5 Coder 14B need?

Qwen 2.5 Coder 14B (14B parameters) requires approximately 19.3 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 MacBook Pro M4 Max 64GB?

On MacBook Pro M4 Max 64GB, Qwen 2.5 Coder 14B achieves approximately 38.3 tokens per second decode speed with a time-to-first-token of 5057ms using Q4_K_M quantization.

Can MacBook Pro M4 Max 64GB run Qwen 2.5 Coder 14B for coding?

For coding workloads, Qwen 2.5 Coder 14B on MacBook Pro M4 Max 64GB receives a B grade with 38.3 tok/s and 131K context.

What context window can Qwen 2.5 Coder 14B use on MacBook Pro M4 Max 64GB?

On MacBook Pro M4 Max 64GB, 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 MacBook Pro M4 Max 64GB as fast as VRAM for Qwen 2.5 Coder 14B?

Not always. MacBook Pro M4 Max 64GB 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.

See all results for MacBook Pro M4 Max 64GB See all hardware for Qwen 2.5 Coder 14B

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