Would CUDA be a better path than Intel Arc A370M 4GB for Ternary 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.

Can Ternary Bonsai 27B run on Intel Arc A370M 4GB?

Q: What should I upgrade first if Ternary 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.

NO — Won't Fit

F0Won't run

Estimated from fit model

Ternary Bonsai 27B needs ~12.4 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

Q2_0_G128 (Low quality) — 9.5 GB, exceeds 4.0 GB available

9.5 GB required4.0 GB available

238% VRAM needed

5.5 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

2.0 tok/s

TTFT

96800 ms

Safe context

Memory

9.5 GB / 4.0 GB

Offload

60%

Memory breakdown

Weights7.2 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 feelsTernary 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: 2.0 tok/s decode · 96.8s TTFT (warm) · 5 tok/s prefill

What limits this setup

Usable VRAM is the main blocker for this model.

Not enough usable memory

The model needs 12.4 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	2.0 tok/s	52800 ms	4K
Coding	F	Too heavy	2.0 tok/s	96800 ms	4K
Agentic Coding	F	Too heavy	2.0 tok/s	140800 ms	4K
Reasoning	F	Too heavy	2.0 tok/s	114400 ms	4K
RAG	F	Too heavy	2.0 tok/s	176000 ms	4K

Inference speed

Ternary Bonsai 27B inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for Ternary Bonsai 27B at Q2_0_G128 across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 4080 Super 16GB at ~87 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 4080 Super 16GB	16 GB	Q2_0_G128	87.2	Fits
RTX 5090 32GB	32 GB	Q2_0_G128	76.2	Fits

Quantization options

How Ternary 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

Upgrade options

Hardware that runs Ternary Bonsai 27B well

Intel Arc B570 10GBBudget pick

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

Makes the model fit on the accelerator instead of staying completely out of reach.16.7 tok/s decode

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

Intel Arc B580 12GBBest value

12 GB VRAM (+8)456 GB/s (+344)

Makes the model fit on the accelerator instead of staying completely out of reach.23.7 tok/s decode

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.

~$249 MSRP

Intel Arc A770 16GBIntel upgrade

16 GB VRAM (+12)560 GB/s (+448)

Makes the model fit on the accelerator instead of staying completely out of reach.27.3 tok/s decode

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.

~$349 MSRP

Frequently asked questions

Can Intel Arc A370M 4GB run Ternary Bonsai 27B?

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

How much VRAM does Ternary Bonsai 27B need?

Ternary Bonsai 27B (27B parameters) requires approximately 12.4 GB of memory with Q2_0_G128 quantization.

What is the best quantization for Ternary Bonsai 27B?

The recommended quantization for Ternary Bonsai 27B is Q2_0_G128, which balances quality and memory efficiency.

What speed will Ternary Bonsai 27B run at on Intel Arc A370M 4GB?

On Intel Arc A370M 4GB, Ternary Bonsai 27B achieves approximately 2.0 tokens per second decode speed with a time-to-first-token of 96800ms using Q2_0_G128 quantization.

Can Intel Arc A370M 4GB run Ternary Bonsai 27B for coding?

For coding workloads, Ternary Bonsai 27B on Intel Arc A370M 4GB receives a F grade with 2.0 tok/s and 4K context.

What context window can Ternary Bonsai 27B use on Intel Arc A370M 4GB?

On Intel Arc A370M 4GB, Ternary 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 Ternary Bonsai 27B feels slow on Intel Arc A370M 4GB?

See all results for Intel Arc A370M 4GB See all hardware for Ternary Bonsai 27B

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