Is unified memory on MacBook Pro M3 Max 64GB as fast as VRAM for Ornith 1.0 35B A3B?

Not always. MacBook Pro M3 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.

Can Ornith 1.0 35B A3B run on MacBook Pro M3 Max 64GB?

YES — Runs Great

A83Great

Estimated from fit model

Ornith 1.0 35B A3B needs ~29.5 GB VRAM. MacBook Pro M3 Max 64GB has 46.1 GB. With Q4_K_M quantization, expect ~36 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) — 29.5 GB, 36.4 tok/s, Runs well

29.5 GB required46.1 GB available

64% VRAM used

Fit status

Runs well

Decode

36.4 tok/s

TTFT

5319 ms

Safe context

262K

Memory

29.5 GB / 46.1 GB

Memory breakdown

Weights21.4 GB

KV Cache0.3 GB

Runtime0.9 GB

Headroom6.9 GB

See how fast it feels

See how fast it feelsOrnith 1.0 35B A3B on MacBook Pro M3 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: 36.4 tok/s decode · 5.3s TTFT (warm) · 91 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	A	Runs well	36.4 tok/s	2901 ms	262K
Coding	A	Runs well	36.4 tok/s	5319 ms	262K
Agentic Coding	A	Runs well	36.4 tok/s	7736 ms	262K
Reasoning	A	Runs well	36.4 tok/s	6286 ms	262K
RAG	A	Runs well	36.4 tok/s	9670 ms	262K

Inference speed

Ornith 1.0 35B A3B inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for Ornith 1.0 35B A3B at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~139 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	139.1	Fits
RTX 4090 24GB	24 GB	Q4_K_M	77.7	Offloads

Quantization options

How Ornith 1.0 35B A3B (35.099998474121094B params) fits at each quantization level on MacBook Pro M3 Max 64GB (46.1 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	13.7 GB	Low	A75
Q3_K_S	3	17.2 GB	Low	A76
NVFP4	4

Get started

Copy-paste commands to run Ornith 1.0 35B A3B on your machine.

Run

docker run --rm -it ghcr.io/ggerganov/llama.cpp:full \
  --hf-repo "deepreinforce-ai/Ornith-1.0-35B" \
  --hf-file "Ornith-1.0-35B-Q4_K_M.gguf" \
  -c 4096 -ngl 99

Your hardware

More models your MacBook Pro M3 Max 64GB can run

Model	Params	Grade	Decode	Capabilities
Kimi Linear 48B A3B	48B	A	8.2 tok/s

Frequently asked questions

Can MacBook Pro M3 Max 64GB run Ornith 1.0 35B A3B?

Yes, MacBook Pro M3 Max 64GB can run Ornith 1.0 35B A3B with a A grade (Runs well). Expected decode speed: 36.4 tok/s.

How much VRAM does Ornith 1.0 35B A3B need?

Ornith 1.0 35B A3B (35.099998474121094B parameters) requires approximately 29.5 GB of memory with Q4_K_M quantization.

What is the best quantization for Ornith 1.0 35B A3B?

The recommended quantization for Ornith 1.0 35B A3B is Q4_K_M, which balances quality and memory efficiency.

What speed will Ornith 1.0 35B A3B run at on MacBook Pro M3 Max 64GB?

On MacBook Pro M3 Max 64GB, Ornith 1.0 35B A3B achieves approximately 36.4 tokens per second decode speed with a time-to-first-token of 5319ms using Q4_K_M quantization.

Can MacBook Pro M3 Max 64GB run Ornith 1.0 35B A3B for coding?

For coding workloads, Ornith 1.0 35B A3B on MacBook Pro M3 Max 64GB receives a A grade with 36.4 tok/s and 262K context.

What context window can Ornith 1.0 35B A3B use on MacBook Pro M3 Max 64GB?

On MacBook Pro M3 Max 64GB, Ornith 1.0 35B A3B can safely use up to 262K tokens of context. The model's official context limit is 262K, but available memory constrains the safe maximum.

See all results for MacBook Pro M3 Max 64GB See all hardware for Ornith 1.0 35B A3B

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