Is unified memory on MacBook Pro M4 Pro 48GB as fast as VRAM for speechless zephyr code functionary 7b?

Not always. MacBook Pro M4 Pro 48GB 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.

Can speechless zephyr code functionary 7b run on MacBook Pro M4 Pro 48GB?

YES — Runs Great

C46Usable

Estimated — low-sample bucket· few comparable runs

speechless zephyr code functionary 7b needs ~11.2 GB VRAM. MacBook Pro M4 Pro 48GB has 34.6 GB. With Q4_K_M quantization, expect ~45 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: LowStack: 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) — 11.2 GB, 45.3 tok/s, Runs well

11.2 GB required34.6 GB available

32% VRAM used

Fit status

Runs well

Decode

45.3 tok/s

TTFT

4275 ms

Safe context

472K

Memory

11.2 GB / 34.6 GB

Memory breakdown

Weights4.3 GB

KV Cache0.8 GB

Runtime0.9 GB

Headroom5.2 GB

See how fast it feels

See how fast it feelsspeechless zephyr code functionary 7b on MacBook Pro M4 Pro 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: 45.3 tok/s decode · 4.3s TTFT (warm) · 113 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	C	Runs well	45.3 tok/s	2332 ms	472K
Coding	C	Runs well	45.3 tok/s	4275 ms	472K
Agentic Coding	C	Runs well	45.3 tok/s	6218 ms	472K
Reasoning	C	Runs well	45.3 tok/s	5052 ms	472K
RAG	C	Runs well	45.3 tok/s	7772 ms	472K

Inference speed

speechless zephyr code functionary 7b inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for speechless zephyr code functionary 7b at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~98 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	98.0	Fits
RTX 4090 24GB	24 GB	Q4_K_M	98.0	Fits
RTX 4080 Super 16GB	16 GB	Q4_K_M	98.0

Quantization options

How speechless zephyr code functionary 7b (7B params) fits at each quantization level on MacBook Pro M4 Pro 48GB (34.6 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	2.7 GB	Low	C42
Q3_K_S	3	3.4 GB	Low	C43
NVFP4	4

Get started

Copy-paste commands to run speechless zephyr code functionary 7b on your machine.

Run

lms load hf-uukuguy--speechless-zephyr-code-functionary-7b && lms server start

Upgrade options

Hardware that runs speechless zephyr code functionary 7b well

MacBook Pro M4 Max 96GBBudget pick

96 GB Unified (+48)546 GB/s (+273)

Raises estimated decode speed by about 94%.87.8 tok/s decode

Raises estimated decode speed by about 94%.

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

~$2,499 MSRP

Mac Studio M2 Ultra 64GBBest value

64 GB Unified (+16)800 GB/s (+527)

Raises estimated decode speed by about 116%.98 tok/s decode

Raises estimated decode speed by about 116%.

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

~$3,999 MSRP

Mac Studio M1 Ultra 64GBApple upgrade

64 GB Unified (+16)800 GB/s (+527)

Raises estimated decode speed by about 116%.98 tok/s decode

Raises estimated decode speed by about 116%.

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

~$3,999 MSRP

Frequently asked questions

Can MacBook Pro M4 Pro 48GB run speechless zephyr code functionary 7b?

Yes, MacBook Pro M4 Pro 48GB can run speechless zephyr code functionary 7b with a C grade (Runs well). Expected decode speed: 45.3 tok/s.

How much VRAM does speechless zephyr code functionary 7b need?

speechless zephyr code functionary 7b (7B parameters) requires approximately 11.2 GB of memory with Q4_K_M quantization.

What is the best quantization for speechless zephyr code functionary 7b?

The recommended quantization for speechless zephyr code functionary 7b is Q4_K_M, which balances quality and memory efficiency.

What speed will speechless zephyr code functionary 7b run at on MacBook Pro M4 Pro 48GB?

On MacBook Pro M4 Pro 48GB, speechless zephyr code functionary 7b achieves approximately 45.3 tokens per second decode speed with a time-to-first-token of 4275ms using Q4_K_M quantization.

Can MacBook Pro M4 Pro 48GB run speechless zephyr code functionary 7b for coding?

For coding workloads, speechless zephyr code functionary 7b on MacBook Pro M4 Pro 48GB receives a C grade with 45.3 tok/s and 472K context.

What context window can speechless zephyr code functionary 7b use on MacBook Pro M4 Pro 48GB?

On MacBook Pro M4 Pro 48GB, speechless zephyr code functionary 7b can safely use up to 472K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for MacBook Pro M4 Pro 48GB See all hardware for speechless zephyr code functionary 7b

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