Would CUDA be a better path than Intel Arc A770 16GB 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.

Can 1-bit Bonsai 27B run on Intel Arc A770 16GB?

Q: What should I upgrade first if 1-bit Bonsai 27B feels slow on Intel Arc A770 16GB?

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.

YES — Runs Great

S89Excellent

Estimated from fit model

1-bit Bonsai 27B needs ~10.3 GB VRAM. Intel Arc A770 16GB has 16.0 GB. With Q1_0_G128 quantization, expect ~52 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: MediumStack: 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

Q1_0_G128 (Very Low quality) — 7.4 GB, 55.9 tok/s, Runs well

7.4 GB required16.0 GB available

46% VRAM used

Fit status

Runs well

Decode

55.9 tok/s

TTFT

3465 ms

Safe context

157K

Memory

7.4 GB / 16.0 GB

Memory breakdown

Weights3.9 GB

KV Cache1.0 GB

Runtime0.9 GB

Headroom1.6 GB

See how fast it feels

See how fast it feels1-bit Bonsai 27B on Intel Arc A770 16GB
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: 55.9 tok/s decode · 3.5s TTFT (warm) · 140 tok/s prefill

What limits this setup

The raw memory story may look fine, but the software ecosystem is still a constraint here.

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

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	S	Runs well	51.6 tok/s	2047 ms	39K
Coding	S	Runs well	51.6 tok/s	3754 ms	39K
Agentic Coding	S	Tight fit	51.6 tok/s	5460 ms	39K
Reasoning	S	Runs well	51.6 tok/s	4436 ms	39K
RAG	S	Tight fit	51.6 tok/s	6825 ms	39K

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

Quantization options

How 1-bit Bonsai 27B (27B params) fits at each quantization level on Intel Arc A770 16GB (16.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q1_0_G128	1.125	3.9 GB	Very Low	A84
Q2_0_G128	1.71	7.2 GB	Low	S87
Q2_KBest for your GPU

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 Intel Arc A770 16GB run 1-bit Bonsai 27B?

Yes, Intel Arc A770 16GB can run 1-bit Bonsai 27B with a S grade (Runs well). Expected decode speed: 51.6 tok/s.

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

1-bit Bonsai 27B (27B parameters) requires approximately 10.3 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 Intel Arc A770 16GB?

On Intel Arc A770 16GB, 1-bit Bonsai 27B achieves approximately 51.6 tokens per second decode speed with a time-to-first-token of 3754ms using Q1_0_G128 quantization.

Can Intel Arc A770 16GB run 1-bit Bonsai 27B for coding?

For coding workloads, 1-bit Bonsai 27B on Intel Arc A770 16GB receives a S grade with 51.6 tok/s and 39K context.

What context window can 1-bit Bonsai 27B use on Intel Arc A770 16GB?

On Intel Arc A770 16GB, 1-bit Bonsai 27B can safely use up to 39K 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 A770 16GB?

See all results for Intel Arc A770 16GB See all hardware for 1-bit Bonsai 27B

Embed this result▼

Paste this snippet into any page to show a live fit card.

<iframe src="https://willitrunai.com/embed/bonsai-27b-on-arc-a770-16gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>

Preview: