Can 1-bit Bonsai 27B run on Intel Arc B570 10GB?

YES — With Offload

S87Excellent

Estimated from fit model

1-bit Bonsai 27B needs ~9.7 GB VRAM. Intel Arc B570 10GB has 10.0 GB. With Q1_0_G128 quantization, expect ~42 tok/s.

Runtime: llama.cppCapacity: OffloadBandwidth: 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

Q1_0_G128 (Very Low quality) — 6.8 GB, 45.5 tok/s, Runs well

6.8 GB required10.0 GB available

68% VRAM used

Fit status

Runs well

Decode

45.5 tok/s

TTFT

4255 ms

Safe context

69K

Memory

6.8 GB / 10.0 GB

Memory breakdown

Weights3.9 GB

KV Cache1.0 GB

Runtime0.9 GB

Headroom1.0 GB

See how fast it feels

See how fast it feels1-bit Bonsai 27B on Intel Arc B570 10GB
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: 45.5 tok/s decode · 4.3s TTFT (warm) · 114 tok/s prefill

What limits this setup

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

Very little memory headroom

You can run the model, but there is not much room left for longer context, bigger batches, extra apps, or future model updates.

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.

Buy headroom, not only minimum fit

A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	S	Runs well	42.0 tok/s	2514 ms	17K
Coding	S	Runs with offload	42.0 tok/s	4610 ms	17K
Agentic Coding	F	Too heavy	17.2 tok/s	16420 ms	17K
Reasoning	S	Runs with offload	42.0 tok/s	5448 ms	17K
RAG	F	Too heavy	17.2 tok/s	20524 ms	17K

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 B570 10GB (10.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q1_0_G128Best for your GPU	1.125	3.9 GB	Very Low	S88
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

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 B570 10GB run 1-bit Bonsai 27B?

Yes, Intel Arc B570 10GB can run 1-bit Bonsai 27B with a S grade (Runs with offload). Expected decode speed: 42.0 tok/s.

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

1-bit Bonsai 27B (27B parameters) requires approximately 9.7 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 B570 10GB?

On Intel Arc B570 10GB, 1-bit Bonsai 27B achieves approximately 42.0 tokens per second decode speed with a time-to-first-token of 4610ms using Q1_0_G128 quantization.

Can Intel Arc B570 10GB run 1-bit Bonsai 27B for coding?

For coding workloads, 1-bit Bonsai 27B on Intel Arc B570 10GB receives a S grade with 42.0 tok/s and 17K context.

What context window can 1-bit Bonsai 27B use on Intel Arc B570 10GB?

On Intel Arc B570 10GB, 1-bit Bonsai 27B can safely use up to 17K 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 B570 10GB?

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.

Would CUDA be a better path than Intel Arc B570 10GB 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 B570 10GB See all hardware for 1-bit Bonsai 27B

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Preview:

What limits this setup

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

Very little memory headroom

You can run the model, but there is not much room left for longer context, bigger batches, extra apps, or future model updates.

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.

Buy headroom, not only minimum fit

A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.

Workload

Grade

Fit

Decode

TTFT

Context

Chat

Runs well

42.0 tok/s

2514 ms

17K

Coding

Runs with offload

42.0 tok/s

4610 ms

17K

Agentic Coding

Too heavy

17.2 tok/s

16420 ms

17K

Reasoning

Runs with offload

42.0 tok/s

5448 ms

17K

RAG

Too heavy

17.2 tok/s

20524 ms

17K

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

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

Quantization options

How 1-bit Bonsai 27B (27B params) fits at each quantization level on Intel Arc B570 10GB (10.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q1_0_G128Best for your GPU	1.125	3.9 GB	Very Low	S88
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

Frequently asked questions

Can Intel Arc B570 10GB run 1-bit Bonsai 27B?

Yes, Intel Arc B570 10GB can run 1-bit Bonsai 27B with a S grade (Runs with offload). Expected decode speed: 42.0 tok/s.

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

1-bit Bonsai 27B (27B parameters) requires approximately 9.7 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 B570 10GB?

On Intel Arc B570 10GB, 1-bit Bonsai 27B achieves approximately 42.0 tokens per second decode speed with a time-to-first-token of 4610ms using Q1_0_G128 quantization.

Can Intel Arc B570 10GB run 1-bit Bonsai 27B for coding?

For coding workloads, 1-bit Bonsai 27B on Intel Arc B570 10GB receives a S grade with 42.0 tok/s and 17K context.

What context window can 1-bit Bonsai 27B use on Intel Arc B570 10GB?

On Intel Arc B570 10GB, 1-bit Bonsai 27B can safely use up to 17K 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 B570 10GB?

Would CUDA be a better path than Intel Arc B570 10GB for 1-bit Bonsai 27B?