sam3_openvino

kengboon/sam3_openvino

sam3_openvino is a machine learning model from kengboon. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for openvino. The card lists the license as other.

Pre-exported OpenVINO IR variants of SAM3.1 for efficient CPU and GPU inference.

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Updated Jul 23, 2026

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12.7 GB

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At a glance

Library: openvino
License: other
Access: Public
Created: Jul 23, 2026
Updated: Jul 23, 2026
SHA: 2ac87a4f

Base models

facebook/sam3.1

Model card

From the Hugging Face model README

SAM3 OpenVINO Models

Pre-exported OpenVINO IR variants of SAM3.1 for efficient CPU and GPU inference.

Origin & License

These models are derived from Meta's SAM 3.1 (facebook/sam3), and therefore follow the same license under the SAM License The OpenVINO IR exports and quantized variants in this repository are derivative works of the original SAM 3.1 model weights and are subject to the same SAM License terms. No model architecture was modified — only the format (PyTorch → ONNX → OpenVINO IR) and optional weight compression were applied.

Available Variants

Variant	Description	Size
`openvino-fp16`	FP16 — recommended for GPU	1.63 GB
`openvino-fp32`	FP32 — reference precision for CPU	3.25 GB
`openvino-int8_sym`	INT8 symmetric — CPU-optimized	~0.83 GB
`openvino-int8_asym`	INT8 asymmetric — CPU-optimized	~0.83 GB
`openvino-int4_sym`	INT4 symmetric — ultra-low memory CPU	~0.45 GB
`openvino-int4_asym`	INT4 asymmetric — ultra-low memory CPU	~0.45 GB
`openvino-int8_w8a16`	W8A16 weight-only — recommended for both CPU and GPU	0.84 GB
`openvino-int8_ptq_gpu`	W8A8 post-training quantization — best CPU throughput	~0.83 GB

Benchmark Results

GPU (XPU) — text mode, ms/image ↓

Variant	Potatoes	Candies	Nuts	LED	Mean	vs FP16
FP16	473	450	488	398	452	1.00×
W8A16	495	474	512	419	475	0.96×
PTQ W8A8	~645	~620	~670	~550	~621	0.73×

GPU (XPU) — canvas mode, ms/image ↓

Variant	Potatoes	Candies	Nuts	LED	Mean	vs FP16
FP16	638	752	449	535	593	1.00×
W8A16	657	773	472	556	614	0.96×

CPU — text mode, ms/image ↓

Variant	Potatoes	Candies	Mean	vs FP16	F1 Potatoes	F1 Candies
FP32	8,525	8,407	8,466	1.03×	1.000	0.992
FP16	8,862	8,648	8,755	1.00×	1.000	0.992
INT8 sym	5,270	5,170	5,220	1.67×	1.000	0.992 ✓
INT8 asym	5,360	5,213	5,286	1.66×	1.000	0.992 ✓
W8A16	5,228	5,183	5,205	1.68×	1.000	0.992 ✓
INT4 sym	5,526	5,450	5,488	1.59×	0.963	0.792 ⚠️
INT4 asym	5,599	5,485	5,542	1.58×	1.000	0.939 ⚠️

Quality ([email protected]) — GPU

Dataset	FP16 (text)	W8A16 (text)	FP16 (canvas)	W8A16 (canvas)
Potatoes	1.000	1.000	1.000	1.000
Candies	0.992	0.992	0.991	0.991
Nuts	0.698	0.703	0.874	0.881
LED	0.000*	0.000*	0.720	0.724

* LED dataset uses visual-only prompts; F1=0.000 in text mode is expected (no text labels provided).

Quantization Summary — GPU

Approach	Avg text (ms)	Avg canvas (ms)	vs FP16	Notes
FP16 (baseline)	452	593	1.00×	Native GPU precision
PTQ W8A8 (all layers)	~640	~780	0.73×	Q/DQ overhead on FP16-optimized GPU
PTQ W8A8 (VE only)	~650	~760	0.72×	Mixed-precision boundary overhead
W8A16 (weight-only)	475	614	0.96×	No activation Q/DQ; near-lossless

Why PTQ is slower on GPU: Modern GPU compute units (e.g., FP16 DPAS) are highly optimized for FP16 math. NNCF PTQ inserts explicit Q/DQ activation nodes at every layer boundary; those extra kernel launches exceed the memory savings from 2× smaller weights. W8A16 (weight-only) has no such nodes — weights are dequantized on-the-fly in a fused kernel — giving only ~4% overhead.

Why INT8 is faster on CPU: VNNI instructions natively accelerate INT8 dot products with no separate Q/DQ overhead. The OV CPU plugin fuses dequantize into the matmul kernel, giving 1.67× speedup with zero accuracy loss.

When to Use Each Variant

Target hardware	Recommended variant	Reason
GPU (XPU / discrete)	`openvino-fp16`	Fastest; native FP16 compute
GPU (memory-constrained)	`openvino-int8_w8a16`	2× smaller, only 4% slower
CPU	`openvino-int8_w8a16`	1.68× faster than FP16, lossless
CPU (ultra-low memory)	`openvino-int4_sym`	Smallest, but F1 may drop on some datasets
Older iGPU	`openvino-int8_sym`	Better for architectures without optimized FP16

Hardware & Software

GPU benchmark: Intel Arc discrete GPU (XPU), OpenVINO 2025.x
CPU benchmark: Intel CPU, OpenVINO 2025.x
NNCF version: 3.1.0 (W8A16: nncf.compress_weights, PTQ: nncf.quantize)
Datasets: COCO subsets (Potatoes 10 imgs / Candies 12 imgs / Nuts 21 imgs / LED 15 imgs)

Usage

from instantlearn.models.sam3 import SAM3OpenVINO
from instantlearn.models.sam3.sam3_openvino import SAM3OVVariant

# FP16 — fastest on GPU
model = SAM3OpenVINO(variant=SAM3OVVariant.FP16, device="GPU")

# W8A16 — recommended for CPU or memory-constrained GPU
model = SAM3OpenVINO(variant=SAM3OVVariant.INT8_W8A16, device="CPU")
model = SAM3OpenVINO(variant=SAM3OVVariant.INT8_W8A16, device="GPU")

Library: openvino
License: other
Created: Jul 23, 2026
Updated: Jul 23, 2026