Can Antares 1B run on Intel Arc A370M 4GB?

YES — Tight Fit

B67Good

Estimated from fit model

Antares 1B needs ~3.6 GB VRAM. Intel Arc A370M 4GB has 4.0 GB. With Q4_K_M quantization, expect ~26 tok/s.

Runtime: llama.cppCapacity: TightBandwidth: 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

Q4_K_M (Medium quality) — 3.6 GB, 25.8 tok/s, Tight fit

3.6 GB required4.0 GB available

90% VRAM used

Fit status

Tight fit

Decode

25.8 tok/s

TTFT

7516 ms

Safe context

21K

Memory

3.6 GB / 4.0 GB

Memory breakdown

Weights1.1 GB

KV Cache1.2 GB

Runtime0.9 GB

Headroom0.4 GB

See how fast it feels

See how fast it feelsAntares 1B 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: 25.8 tok/s decode · 7.5s TTFT (warm) · 64 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	B	Runs well	25.8 tok/s	4099 ms	21K
Coding	B	Tight fit	25.8 tok/s	7516 ms	21K
Agentic Coding	F	Too heavy	25.8 tok/s	10932 ms	21K
Reasoning	B	Tight fit	25.8 tok/s	8882 ms	21K
RAG	F	Too heavy	25.8 tok/s	13665 ms	21K

Inference speed

Antares 1B inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for Antares 1B at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~35 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	Q4_K_M	35.0	Fits
RTX 4090 24GB	24 GB	Q4_K_M	29.4	Fits
RTX 4080 Super 16GB	16 GB	Q4_K_M	29.4	Fits
RTX 3090 24GB	24 GB	Q4_K_M	25.8	Fits
RTX 4070 12GB	12 GB	Q4_K_M	25.8	Fits
RTX 3060 12GB	12 GB	Q4_K_M	25.8	Fits
RTX 4060 8GB	8 GB	Q4_K_M	25.8	Fits
RX 7900 XTX 24GB	24 GB	Q4_K_M	25.8	Fits
MacBook Pro M4 Max 128GB	128 GB	Q4_K_M	25.8	Fits
Mac Studio M3 Ultra 256GB	256 GB	Q4_K_M	25.8	Fits
Mac Studio M2 Ultra 128GB	128 GB	Q4_K_M	25.8	Fits
Mac Studio M1 Ultra 128GB	128 GB	Q4_K_M	25.8	Fits
MacBook Pro M4 Max 64GB	64 GB	Q4_K_M	25.8	Fits
MacBook Pro M3 Max 64GB	64 GB	Q4_K_M	25.8	Fits
MacBook Pro M1 Max 64GB	64 GB	Q4_K_M	25.8	Fits
MacBook Pro M4 Pro 48GB	48 GB	Q4_K_M	25.8	Fits

Estimates for single-stream decoding at Q4_K_M; 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 Antares 1B (1.840000033378601B 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	0.3 GB	Very Low	A72
Q2_0_G128	1.71	0.5 GB	Low	A73
Q2_K	2	0.7 GB	Low	A73
Q3_K_S	3	0.9 GB	Low	A73
NVFP4	4	1.0 GB	Medium	A73
Q4_K_M	4	1.1 GB	Medium	A73
Q5_K_M	5	1.3 GB	High	A72
Q6_KBest for your GPU	6	1.5 GB	High	A72
Q8_0	8	2.0 GB	Very High	F0
F16	16	3.8 GB	Maximum	F0

Get started

Copy-paste commands to run Antares 1B on your machine.

Run

docker run --rm -it ghcr.io/ggerganov/llama.cpp:full \
  --hf-repo "fdtn-ai/antares-1b" \
  --hf-file "antares-1b-Q4_K_M.gguf" \
  -c 4096 -ngl 99

升级选项

能流畅运行 Antares 1B 的硬件

Intel Arc A380 6GB经济之选

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

Adds memory headroom for longer context windows and future model growth.25.8 tok/s 解码

Adds memory headroom for longer context windows and future model growth.

~$139 MSRP

Intel Arc Pro A40 6GB最佳性价比

6 GB VRAM (+2)192 GB/s (+80)

Adds memory headroom for longer context windows and future model growth.25.8 tok/s 解码

Adds memory headroom for longer context windows and future model growth.

~$249 MSRP

Frequently asked questions

Can Intel Arc A370M 4GB run Antares 1B?

Yes, Intel Arc A370M 4GB can run Antares 1B with a B grade (Tight fit). Expected decode speed: 25.8 tok/s.

How much VRAM does Antares 1B need?

Antares 1B (1.840000033378601B parameters) requires approximately 3.6 GB of memory with Q4_K_M quantization.

What is the best quantization for Antares 1B?

The recommended quantization for Antares 1B is Q4_K_M, which balances quality and memory efficiency.

What speed will Antares 1B run at on Intel Arc A370M 4GB?

On Intel Arc A370M 4GB, Antares 1B achieves approximately 25.8 tokens per second decode speed with a time-to-first-token of 7516ms using Q4_K_M quantization.

Can Intel Arc A370M 4GB run Antares 1B for coding?

For coding workloads, Antares 1B on Intel Arc A370M 4GB receives a B grade with 25.8 tok/s and 21K context.

What context window can Antares 1B use on Intel Arc A370M 4GB?

On Intel Arc A370M 4GB, Antares 1B can safely use up to 21K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.

What should I upgrade first if Antares 1B feels slow on Intel Arc A370M 4GB?

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 A370M 4GB for Antares 1B?

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 Antares 1B

Embed this result▼

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

<iframe src="https://willitrunai.com/embed/antares-1b-on-arc-a370m-4gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>

Preview: