Can 1-bit Bonsai 27B run on MacBook Air M4 24GB?

YES — Runs Great

S86Excellent

Estimated — low-sample bucket· few comparable runs

1-bit Bonsai 27B needs ~11.3 GB VRAM. MacBook Air M4 24GB has 17.3 GB. With Q1_0_G128 quantization, expect ~18 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: Very lowStack: StandardBottleneck: 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.

Capabilities:

Select quantization to explore

Q1_0_G128 (Very Low quality) — 8.4 GB, 31.6 tok/s, Runs well

8.4 GB required17.3 GB available

49% VRAM used

Fit status

Runs well

Decode

31.6 tok/s

TTFT

6125 ms

Safe context

162K

Memory

8.4 GB / 17.3 GB

Memory breakdown

Weights3.9 GB

KV Cache1.0 GB

Runtime0.9 GB

Headroom2.6 GB

See how fast it feels

See how fast it feels1-bit Bonsai 27B on MacBook Air M4 24GB
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: 31.6 tok/s decode · 6.1s TTFT (warm) · 79 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	17.7 tok/s	5972 ms	41K
Coding	S	Runs well	17.7 tok/s	10948 ms	41K
Agentic Coding	A	Tight fit	17.7 tok/s	15925 ms	41K
Reasoning	S	Runs well	17.7 tok/s	12939 ms	41K
RAG	A	Tight fit	17.7 tok/s	19906 ms	41K

Inference speed

1-bit Bonsai 27B inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for 1-bit Bonsai 27B at Q1_0_G128 across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~156 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	Q1_0_G128	156.1	Fits
RX 7900 XTX 24GB	24 GB	Q1_0_G128	132.5	Fits
Mac Studio M3 Ultra 256GB	256 GB	Q1_0_G128	123.5	Fits
MacBook Pro M4 Max 128GB	128 GB	Q1_0_G128	122.1	Fits
MacBook Pro M4 Max 64GB	64 GB	Q1_0_G128	122.1	Fits
Mac Studio M2 Ultra 128GB	128 GB	Q1_0_G128	102.9	Fits
Mac Studio M1 Ultra 128GB	128 GB	Q1_0_G128	97.6	Fits
RTX 4090 24GB	24 GB	Q1_0_G128	89.7	Fits
RTX 3090 24GB	24 GB	Q1_0_G128	87.4	Fits
RTX 4080 Super 16GB	16 GB	Q1_0_G128	86.1	Fits
MacBook Pro M4 Pro 48GB	48 GB	Q1_0_G128	76.9	Fits
RTX 3060 12GB	12 GB	Q1_0_G128	69.5	Fits
RTX 4070 12GB	12 GB	Q1_0_G128	53.3	Fits
MacBook Pro M3 Max 64GB	64 GB	Q1_0_G128	53.2	Fits
MacBook Pro M1 Max 64GB	64 GB	Q1_0_G128	48.8	Fits
RTX 4060 8GB	8 GB	Q1_0_G128	28.0	Tight

Estimates for single-stream decoding at Q1_0_G128; 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 1-bit Bonsai 27B (27B params) fits at each quantization level on MacBook Air M4 24GB (17.3 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q1_0_G128	1.125	3.9 GB	Very Low	A83
Q2_0_G128	1.71	7.2 GB	Low	S86
Q2_KBest for your GPU	2	10.5 GB	Low	S86
Q3_K_S	3	13.2 GB	Low	F0
NVFP4	4	15.1 GB	Medium	F0
Q4_K_M	4	16.5 GB	Medium	F0
Q5_K_M	5	19.4 GB	High	F0
Q6_K	6	22.1 GB	High	F0
Q8_0	8	28.9 GB	Very High	F0
F16	16	55.4 GB	Maximum	F0

Get started

Copy-paste commands to run 1-bit Bonsai 27B on your machine.

Run

docker run --rm -it ghcr.io/ggerganov/llama.cpp:full \
  --hf-repo "prism-ml/Bonsai-27B-gguf" \
  --hf-file "Bonsai-27B-gguf-Q1_0_G128.gguf" \
  -c 4096 -ngl 99

Frequently asked questions

Can MacBook Air M4 24GB run 1-bit Bonsai 27B?

Yes, MacBook Air M4 24GB can run 1-bit Bonsai 27B with a S grade (Runs well). Expected decode speed: 17.7 tok/s.

How much VRAM does 1-bit Bonsai 27B need?

1-bit Bonsai 27B (27B parameters) requires approximately 11.3 GB of memory with Q1_0_G128 quantization.

What is the best quantization for 1-bit Bonsai 27B?

The recommended quantization for 1-bit Bonsai 27B is Q1_0_G128, which balances quality and memory efficiency.

What speed will 1-bit Bonsai 27B run at on MacBook Air M4 24GB?

On MacBook Air M4 24GB, 1-bit Bonsai 27B achieves approximately 17.7 tokens per second decode speed with a time-to-first-token of 10948ms using Q1_0_G128 quantization.

Can MacBook Air M4 24GB run 1-bit Bonsai 27B for coding?

For coding workloads, 1-bit Bonsai 27B on MacBook Air M4 24GB receives a S grade with 17.7 tok/s and 41K context.

What context window can 1-bit Bonsai 27B use on MacBook Air M4 24GB?

On MacBook Air M4 24GB, 1-bit Bonsai 27B can safely use up to 41K tokens of context. The model's official context limit is 262K, but available memory constrains the safe maximum.

Is unified memory on MacBook Air M4 24GB as fast as VRAM for 1-bit Bonsai 27B?

Not always. MacBook Air M4 24GB 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 Air M4 24GB See all hardware for 1-bit Bonsai 27B

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