Can 1-bit Bonsai 27B run on Intel Arc A370M 4GB?

NO — Won't Fit

F0Won't run

Estimated from fit model

1-bit Bonsai 27B needs ~6.2 GB but Intel Arc A370M 4GB only has 4.0 GB. Try a smaller quantization or lighter model.

Runtime: llama.cppCapacity: No fitBandwidth: Very lowStack: StandardBottleneck: Memory capacity

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) — 6.2 GB, exceeds 4.0 GB available

6.2 GB required4.0 GB available

155% VRAM needed

2.2 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

3.7 tok/s

TTFT

52726 ms

Safe context

Memory

6.2 GB / 4.0 GB

Offload

40%

Memory breakdown

Weights3.9 GB

KV Cache1.0 GB

Runtime0.9 GB

Headroom0.4 GB

See how fast it feels

With memory offload — actual speed may be lower

See how fast it feels1-bit Bonsai 27B on Intel Arc A370M 4GB
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: 3.7 tok/s decode · 52.7s TTFT (warm) · 9 tok/s prefill

What limits this setup

Usable VRAM is the main blocker for this model.

Not enough usable memory

The model needs 6.2 GB, but this setup only exposes 4.0 GB of usable VRAM.

Runtime ecosystem is narrower than CUDA

Intel GPUs can look attractive on memory per dollar, but local AI tooling, kernels, and model coverage are still broader and easier on CUDA today.

Best improvement path

Add more VRAM headroom

The first useful upgrade is more dedicated VRAM so you can fit the model without shrinking context or dropping to a much lower quant.

Prefer CUDA if you want the path of least resistance

If your goal is maximum runtime coverage, easier troubleshooting, and better support for new local AI releases, CUDA is usually still the safer upgrade path.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	F	Too heavy	4.4 tok/s	24174 ms	4K
Coding	F	Too heavy	3.7 tok/s	52726 ms	4K
Agentic Coding	F	Too heavy	2.7 tok/s	104517 ms	4K
Reasoning	F	Too heavy	3.7 tok/s	62313 ms	4K
RAG	F	Too heavy	2.7 tok/s	130646 ms	4K

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 Intel Arc A370M 4GB (4.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q1_0_G128	1.125	3.9 GB	Very Low	F0
Q2_0_G128	1.71	7.2 GB	Low	F0
Q2_K	2	10.5 GB	Low	F0
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

升级选项

能流畅运行 1-bit Bonsai 27B 的硬件

Intel Arc A380 6GB经济之选

6 GB VRAM (+2)186 GB/s (+74)

Makes the model fit on the accelerator instead of staying completely out of reach.13.4 tok/s 解码

Makes the model fit on the accelerator instead of staying completely out of reach.

Raises estimated decode speed by about 262%.

~$139 MSRP

Intel Arc A580 8GB最佳性价比

8 GB VRAM (+4)512 GB/s (+400)

Makes the model fit on the accelerator instead of staying completely out of reach.55.6 tok/s 解码

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

~$179 MSRP

Intel Arc B570 10GBIntel 升级

10 GB VRAM (+6)380 GB/s (+268)

Makes the model fit on the accelerator instead of staying completely out of reach.45.5 tok/s 解码

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

~$219 MSRP

Frequently asked questions

Can Intel Arc A370M 4GB run 1-bit Bonsai 27B?

No, 1-bit Bonsai 27B requires more memory than Intel Arc A370M 4GB provides.

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

1-bit Bonsai 27B (27B parameters) requires approximately 6.2 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 Intel Arc A370M 4GB?

On Intel Arc A370M 4GB, 1-bit Bonsai 27B achieves approximately 3.7 tokens per second decode speed with a time-to-first-token of 52726ms using Q1_0_G128 quantization.

Can Intel Arc A370M 4GB run 1-bit Bonsai 27B for coding?

For coding workloads, 1-bit Bonsai 27B on Intel Arc A370M 4GB receives a F grade with 3.7 tok/s and 4K context.

What context window can 1-bit Bonsai 27B use on Intel Arc A370M 4GB?

On Intel Arc A370M 4GB, 1-bit Bonsai 27B can safely use up to 4K tokens of context. The model's official context limit is 262K, but available memory constrains the safe maximum.

What should I upgrade first if 1-bit Bonsai 27B feels slow on Intel Arc A370M 4GB?

Add more VRAM headroom. The first useful upgrade is more dedicated VRAM so you can fit the model without shrinking context or dropping to a much lower quant.

Would CUDA be a better path than Intel Arc A370M 4GB for 1-bit Bonsai 27B?

Often yes, if your goal is the easiest setup and the widest runtime support. Intel can offer attractive memory capacity, but CUDA still tends to win on tooling maturity, guides, kernels, and model coverage for local AI.

See all results for Intel Arc A370M 4GB See all hardware for 1-bit Bonsai 27B

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