Lecture 05 — Physical AI and Multimodal Gemma 4: SigLIP, VLM Pipeline, and Robot Perception on Jetson

Collection: Gemma 4 Edge Deployment | Previous: ← Lecture 04

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The previous four lectures covered Gemma 4 as a text model — architecture, quantization, runtimes, speculative decode. This lecture closes the loop: what happens when you attach a camera and deploy Gemma 4 multimodal on a robot? Gemma 4's vision variants (4B-IT-VL, 12B-IT-VL, 27B-IT-VL) use a SigLIP-400M vision encoder to produce visual tokens, which are prefixed into the Gemma text decoder. The combined model is a Vision-Language Model (VLM) capable of answering spatial questions about what it sees, describing scenes, and driving agentic actions — tasks that define physical AI on Jetson.

This lecture covers the SigLIP-400M encoder architecture, the VLM pipeline's latency budget, deployment of Gemma 4-VL on Jetson Orin, quantization for the vision stack, and real robot perception use cases.

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Learning objectives

1. Describe the SigLIP-400M encoder architecture and how it encodes visual tokens for Gemma 4's decoder.
2. Compute the end-to-end VLM latency budget (image encode → prefill → decode) for Gemma 4 4B-VL on Jetson Orin.
3. Deploy Gemma 4-VL on Jetson using Ollama (vision support), llama.cpp mmproj, or HuggingFace Transformers.
4. Explain the quantization challenges specific to vision encoders and why you treat the vision encoder and text decoder as separate quantization targets.
5. Design a robot perception pipeline (camera → VLM → action) with a measured TTFT latency target for closed-loop control.

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1. SigLIP-400M — the vision encoder for Gemma 4

1.1 Architecture overview

SigLIP (Sigmoid Loss for Language-Image Pre-training, Zhai et al., 2023) is Google's successor to CLIP for vision-language alignment:

CLIP vs SigLIP:
  CLIP objective:     softmax contrastive loss (global normalization across batch)
                      numerically stable but requires large batch sizes (>4096)
  SigLIP objective:   sigmoid binary loss per image-text pair (local, per-pair)
                      stable at smaller batches, scales better, stronger at retrieval

SigLIP-400M for Gemma 4:
  Architecture:  ViT-L/14 variant (Vision Transformer Large with 14×14 patches)
  Parameters:    ~400M (400 million)
  Input:         images resized to 224×224 (baseline) or 448×448 (high-res variant)
  Patch size:    14×14 pixels → 256 patches at 224px, 1024 patches at 448px
  Embedding dim: 1024
  Transformer:   24 layers, 16 attention heads, FFN dim 4096
  Output:        256 or 1024 visual tokens (each 1024-dim)
  Projection:    linear proj 1024 → Gemma 4 hidden_dim (e.g., 2048 for 4B)

1.2 Image tokenization pipeline

from transformers import AutoProcessor, PaliGemmaForConditionalGeneration
import PIL.Image

# Gemma 4-VL uses the PaliGemma/SigLIP processor for image tokenization:
processor = AutoProcessor.from_pretrained("google/gemma-4-4b-it")

# Load an image (e.g., from Jetson camera feed):
image = PIL.Image.open("/dev/video0")  # or file path

# Processor tokenizes both image and text:
inputs = processor(
    images=image,
    text="<image>\nDescribe what the robot arm is holding.",
    return_tensors="pt"
)

# inputs["pixel_values"]: (1, 3, 224, 224) — resized + normalized image
# inputs["input_ids"]:    (<img> token × 256) + text tokens

# SigLIP encodes pixel_values → 256 visual tokens of dim 1024
# Linear projection → Gemma 4 hidden_dim tokens
# These 256 tokens are PREPENDED to the text token sequence
# Gemma 4 decoder processes all (256 + text_tokens) as the prefill

1.3 Visual token count and prefill cost

The image resolution determines how many visual tokens are prepended:

224×224 (standard):  256 visual tokens  → prefill adds 256 tokens
448×448 (high-res):  1024 visual tokens → prefill adds 1024 tokens

PREFILL COST on Orin (Gemma 4 4B INT4, 204 GB/s):
  Standard (256 tokens): prefill throughput ~500 tok/s (compute-bound)
                         latency: 256 / 500 = ~0.5 s prefill
                         (faster than text prefill because attention is local
                          for sliding-window layers)
  High-res (1024 tokens): latency: ~2.0 s prefill

  PLUS SigLIP encoding:  224×224 → ~80 ms on GPU (FP16 ViT-L/14)
                         448×448 → ~200 ms on GPU

Total TTFT (time-to-first-token):
  Standard:  80 ms (SigLIP) + 500 ms (prefill) + 16 ms (first decode) = ~600 ms
  High-res:  200 ms (SigLIP) + 2000 ms (prefill) + 16 ms = ~2.2 s

For real-time robot perception (target ≤ 500 ms TTFT), you must use standard 224×224 resolution. High-res is for offline analysis or less time-sensitive tasks.

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2. Deployment paths for Gemma 4-VL on Jetson

2.1 Ollama (recommended for fast bring-up)

Ollama v0.4+ supports Gemma 4 vision models natively:

# Pull and run Gemma 4 4B vision model:
ollama pull gemma4:4b-instruct-vision-q4_K_M  # ~2.2 GB

# Interactive vision query:
ollama run gemma4:4b-instruct-vision-q4_K_M

# Programmatic with image:
curl http://localhost:11434/api/generate -d '{
  "model": "gemma4:4b-instruct-vision-q4_K_M",
  "prompt": "What objects are on the table?",
  "images": ["'$(base64 -w0 /tmp/camera_frame.jpg)'"],
  "stream": false
}'

# Expected throughput on Orin (INT4, SigLIP 224px):
# encode:  ~80 ms
# prefill: ~500 ms
# decode:  ~55 tok/s
# TTFT:    ~600 ms for a typical scene description prompt

2.2 llama.cpp with mmproj (multimodal projection)

llama.cpp handles Gemma 4-VL via its mmproj (multimodal projection) system:

# You need TWO files:
# 1. The main GGUF (text model weights):
#    gemma4-4b-it-q4_k_m.gguf
# 2. The vision projection GGUF (SigLIP + projection):
#    mmproj-gemma4-4b-f16.gguf  (keep FP16 — quantizing mmproj loses accuracy)

# Interactive VLM CLI:
./build/bin/llama-cli \
    --model      gemma4-4b-it-q4_k_m.gguf \
    --mmproj     mmproj-gemma4-4b-f16.gguf \
    --n-gpu-layers 9999 \
    --image      /tmp/robot_view.jpg \
    -p "Describe what the gripper is touching."

# Server mode for API access:
./build/bin/llama-server \
    --model      gemma4-4b-it-q4_k_m.gguf \
    --mmproj     mmproj-gemma4-4b-f16.gguf \
    --n-gpu-layers 9999 \
    --port 8080 \
    --ctx-size 4096

# curl the server with an image:
curl http://localhost:8080/v1/chat/completions \
    -H "Content-Type: application/json" \
    -d '{
      "model": "gemma4-4b",
      "messages": [{
        "role": "user",
        "content": [
          {"type": "image_url", "image_url": {"url": "data:image/jpeg;base64,'$(base64 -w0 /tmp/frame.jpg)'"}},
          {"type": "text", "text": "What object is in front of the robot?"}
        ]
      }]
    }'

2.3 HuggingFace Transformers (full precision, good for prototyping)

from transformers import AutoProcessor, Gemma3ForConditionalGeneration
import torch, PIL.Image

# Gemma 4 VL models use the Gemma3 family in HF:
model_id = "google/gemma-4-4b-it"

processor = AutoProcessor.from_pretrained(model_id)
model = Gemma3ForConditionalGeneration.from_pretrained(
    model_id,
    torch_dtype=torch.bfloat16,
    device_map="cuda"
)

image = PIL.Image.open("/tmp/jetson_camera.jpg").convert("RGB")

messages = [{
    "role": "user",
    "content": [
        {"type": "image", "image": image},
        {"type": "text", "text": "Is the robot arm's gripper open or closed?"},
    ]
}]

inputs = processor.apply_chat_template(
    messages, add_generation_prompt=True, tokenize=True,
    return_dict=True, return_tensors="pt"
).to(model.device)

with torch.inference_mode():
    output = model.generate(**inputs, max_new_tokens=200, do_sample=False)

print(processor.decode(output[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True))

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3. Quantizing the vision stack

The Gemma 4-VL deployment has two quantization targets with different sensitivities:

3.1 Text decoder (same rules as Lecture 02)

Gemma 4 text decoder: Q4_K_M GGUF, AWQ INT4, or GPTQ INT4
  Sensitivity: moderate — QK-norm + GQA make it robust to INT4
  Target: 2.2 GB for 4B (vs 8.2 GB BF16)
  Accuracy loss: ≤0.3 MMLU points at Q4_K_M (see Lecture 02)

3.2 SigLIP vision encoder — keep at FP16

The vision encoder is more sensitive to quantization than the text decoder:

WHY FP16 FOR SIGLIP:
  1. The encoder produces 256 visual tokens that REPLACE the image for the decoder.
     Quantization errors in the visual tokens compound across all 256 token positions.
  2. Image patches are continuous-valued — no discrete tokenization to absorb errors.
     Unlike text (limited vocab = natural quantization), pixels vary continuously.
  3. SigLIP uses sigmoid loss training — the feature space is calibrated for FP32/FP16
     precision; INT8 introduces systematic bias that degrades spatial reasoning.
  4. The mmproj (projection layer) is tiny (~80 MB FP16 for 4B).
     Saving to INT8 saves ~40 MB — not worth the accuracy degradation.

RULE: Always keep the mmproj/SigLIP encoder in FP16.
      Only quantize the text decoder (the large component).

EXCEPTION: INT8 for SigLIP is acceptable if you measure VQA accuracy drop < 1%
           on your specific task. Use llm-compressor or quanto for dynamic INT8.

3.3 Memory layout for 4B-VL on Orin

Component                   FP16 Size   INT4 Size   Notes
─────────────────────────── ─────────── ─────────── ───────────────────────────
SigLIP-400M encoder         800 MB      N/A         Keep FP16
mmproj projection           80 MB       N/A         Keep FP16
Gemma 4 4B text decoder     8,200 MB    2,200 MB    Quantize to INT4
KV cache (4096 ctx, FP16)   ~200 MB     ~100 MB     Use INT8 KV (–kv-cache-type q8_0)
─────────────────────────── ─────────── ─────────── ───────────────────────────
TOTAL (text INT4 + vis FP16) 1,280 MB   3,480 MB    ← fits on Orin 64 GB
TOTAL (text BF16 + vis FP16) 9,280 MB                ← still fits, slower

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4. Robot perception pipeline on Jetson

4.1 Physical AI use cases for Gemma 4-VL

Gemma 4's combination of 128K context, bounded KV, and multimodal capability makes it the first edge VLM suitable for:

Object recognition and spatial reasoning:

"What objects are within reach of the gripper?" → closed-loop pick-and-place
"Is the bin empty?" → warehouse inventory
"Which cable is the red one?" → assembly robot guidance

Scene description for autonomous navigation:

"Describe the obstacles in front of the robot." → path planning assist
"Is the path to the dock clear?" → docking AI
"What is the surface condition of the floor?" → traction-aware navigation

Human-robot interaction:

"What is the person pointing at?" → instruction following
"Is the person wearing safety equipment?" → compliance monitoring
"What task is the human performing?" → collaborative manipulation

4.2 End-to-end latency budget

For a robot operating at 2 Hz (one action decision per 500 ms):

BUDGET: 500 ms total
  Image capture:     10 ms   (Jetson MIPI CSI pipeline, 1080p30)
  Image resize:      5 ms    (cudaResize to 224×224)
  SigLIP encode:     80 ms   (FP16, GPU)
  Prefill:           400 ms  (256 visual tokens + 50 text tokens = 306 tokens)
                             (Gemma 4 4B INT4, ~750 tok/s prefill on Orin)
  First decode:      18 ms   (1 token at ~55 tok/s)
  TOTAL TTFT:        513 ms  ← slightly over budget

OPTIMIZATION TO FIT 500 ms:
  1. Use INT8 KV cache: saves KV memory, more GPU cache available → prefill faster
  2. Reduce prompt length: 50 → 20 text tokens → saves ~30 ms
  3. Pipeline: start prefill while previous decode is completing (temporal overlap)
  4. Result: 80 + 330 + 18 = 428 ms ← fits in 500 ms budget with headroom

4.3 Python pipeline for closed-loop perception

import threading, queue, time
import torch, PIL.Image
from transformers import AutoProcessor, Gemma3ForConditionalGeneration

# Load model once at startup:
model_id = "google/gemma-4-4b-it"
processor = AutoProcessor.from_pretrained(model_id)
model = Gemma3ForConditionalGeneration.from_pretrained(
    model_id, torch_dtype=torch.bfloat16, device_map="cuda"
)

frame_queue = queue.Queue(maxsize=2)
result_queue = queue.Queue()

def perception_worker():
    while True:
        frame = frame_queue.get()  # (PIL.Image, timestamp)
        if frame is None: break
        image, t_capture = frame

        t0 = time.perf_counter()
        messages = [{
            "role": "user",
            "content": [
                {"type": "image", "image": image},
                {"type": "text", "text": "What object is nearest to the gripper?"},
            ]
        }]
        inputs = processor.apply_chat_template(
            messages, add_generation_prompt=True, tokenize=True,
            return_dict=True, return_tensors="pt"
        ).to(model.device)

        with torch.inference_mode():
            output = model.generate(
                **inputs, max_new_tokens=50, do_sample=False
            )

        answer = processor.decode(
            output[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True
        )
        latency_ms = (time.perf_counter() - t0) * 1000
        result_queue.put({"answer": answer, "latency_ms": latency_ms, "t_capture": t_capture})

# Start worker:
t = threading.Thread(target=perception_worker, daemon=True)
t.start()

# Main loop (simulated camera at 10 Hz, VLM at 2 Hz):
for frame_num in range(100):
    if frame_num % 5 == 0:  # every 5th frame → 2 Hz VLM rate
        img = PIL.Image.open(f"/tmp/frame_{frame_num:04d}.jpg").convert("RGB")
        if not frame_queue.full():
            frame_queue.put((img, time.perf_counter()))

    if not result_queue.empty():
        result = result_queue.get()
        print(f"Perception: '{result['answer']}' | latency: {result['latency_ms']:.0f} ms")

    time.sleep(0.1)  # 10 Hz main loop

frame_queue.put(None)  # shutdown
t.join()

4.4 Jetson camera integration (GStreamer + CUDA)

import cv2, PIL.Image
import numpy as np

# GStreamer pipeline for Jetson CSI camera (tested on Orin):
gst_pipeline = (
    "nvarguscamerasrc sensor-id=0 ! "
    "video/x-raw(memory:NVMM), width=1920, height=1080, framerate=30/1 ! "
    "nvvidconv flip-method=0 ! "
    "video/x-raw, width=224, height=224, format=BGRx ! "  # resize in hardware
    "videoconvert ! "
    "video/x-raw, format=BGR ! "
    "appsink"
)

cap = cv2.VideoCapture(gst_pipeline, cv2.CAP_GSTREAMER)
if not cap.isOpened():
    raise RuntimeError("Failed to open CSI camera with GStreamer pipeline.")

def get_frame_for_vlm():
    ret, frame = cap.read()
    if not ret: return None
    # Convert BGR numpy → RGB PIL:
    frame_rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
    return PIL.Image.fromarray(frame_rgb)  # already 224×224 from GStreamer

# SigLIP processes PIL images — GStreamer handles resize in hardware (free)

The GStreamer pipeline resizes from 1080p to 224×224 in the ISP/video converter hardware, eliminating the ~5 ms software resize from the latency budget.

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5. Multimodal quantization with LiteRT

For production deployments that require Google AI Edge certification or DLA acceleration, LiteRT exports include both the text and vision components:

import ai_edge_torch
from transformers import Gemma3ForConditionalGeneration, AutoProcessor
import torch

model_id = "google/gemma-4-4b-it"
model = Gemma3ForConditionalGeneration.from_pretrained(
    model_id, torch_dtype=torch.bfloat16
)
model.eval()

processor = AutoProcessor.from_pretrained(model_id)

# Sample inputs for tracing:
sample_image = torch.zeros(1, 3, 224, 224, dtype=torch.bfloat16)  # dummy image
sample_text = processor.tokenizer("Hello", return_tensors="pt")["input_ids"]

# Export SEPARATE models for vision and text components:
# (LiteRT handles them in two graphs, stitched at the projection layer)

# Vision encoder export:
vision_encoder = model.model.vision_tower  # SigLIP ViT-L
edge_vision = ai_edge_torch.convert(
    vision_encoder.eval(),
    sample_args=(sample_image,),
)
edge_vision.export("gemma4-4b-siglip-fp16.tflite")

# Text decoder export (with int8 quantization):
text_decoder = model.language_model
edge_text = ai_edge_torch.convert(
    text_decoder.eval(),
    sample_args=(sample_text,),
    quant_config=ai_edge_torch.quantize.QuantizationConfig(
        ai_edge_torch.quantize.IntQuantizationConfig(num_bits=8)
    )
)
edge_text.export("gemma4-4b-text-int8.tflite")

DLA acceleration for SigLIP on Thor:

Jetson AGX Thor's DLA (Deep Learning Accelerator) can run the SigLIP ViT-L/14 encoder at lower power than the discrete GPU:

SigLIP on Orin iGPU (FP16): ~80 ms, ~4W
SigLIP on Orin DLA (INT8):  ~120 ms, ~0.8W  ← 5× power reduction, 50% slower
SigLIP on Thor GPU (FP16):  ~40 ms, ~3W
SigLIP on Thor DLA (INT8):  ~60 ms, ~0.4W   ← 7.5× power reduction, 50% slower

RULE: Use DLA for SigLIP when power budget is critical (mobile robot, drone).
      Use GPU for SigLIP when latency budget is critical (manipulation robot).

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6. Performance comparison: Gemma 4-VL vs other edge VLMs

Model	Parameters	Runtime	Orin TTFT	Orin tok/s	KV at 4K ctx	Notes
Gemma 4 4B-VL INT4	4B	llama.cpp	~600 ms	55	200 MB	Best throughput/quality
Gemma 4 1B-VL INT4	1B	llama.cpp	~150 ms	180	50 MB	Fastest, lower quality
Llama 3.2 11B-VL INT4	11B	ollama	~900 ms	25	400 MB	Stronger reasoning
Qwen2.5-VL 7B INT4	7B	llama.cpp	~750 ms	38	300 MB	Excellent OCR
Phi-4 14B-VL INT4	14B	TRT-LLM	~1200 ms	18	550 MB	Needs Thor or +GPU
Gemma 4 12B-VL INT4	12B	llama.cpp	~1100 ms	22	450 MB	Best on Thor

Gemma 4 4B-VL is the recommended target for Orin. It hits 600 ms TTFT (acceptable for 1 Hz control), 55 tok/s decode for verbose descriptions, and 200 MB KV at 4K context — leaving ample space for the rest of the robot software stack.

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7. Capstone: end-to-end robot perception system

Capstone task

Build a perception module that: 1. Accepts a camera frame from a Jetson CSI camera 2. Runs Gemma 4 4B-VL to answer "Is there an obstacle in the robot's path?" 3. Returns a structured JSON answer: {"obstacle": true/false, "description": "...", "confidence": 0-1, "latency_ms": ...} 4. Operates at ≥ 1 Hz (TTFT ≤ 1000 ms)

Reference implementation

import json, time, re
import torch, PIL.Image
from transformers import AutoProcessor, Gemma3ForConditionalGeneration

SYSTEM_PROMPT = """You are a robot perception module.
Answer ONLY with a JSON object: {"obstacle": true/false, "description": "...", "confidence": 0.0-1.0}
Keep description under 20 words. Be conservative: if unclear, obstacle=true."""

def build_perception_model(model_id="google/gemma-4-4b-it"):
    proc = AutoProcessor.from_pretrained(model_id)
    model = Gemma3ForConditionalGeneration.from_pretrained(
        model_id, torch_dtype=torch.bfloat16, device_map="cuda"
    )
    model.eval()
    return proc, model

def perceive(image: PIL.Image.Image, proc, model) -> dict:
    t0 = time.perf_counter()

    messages = [
        {"role": "system", "content": SYSTEM_PROMPT},
        {"role": "user", "content": [
            {"type": "image", "image": image},
            {"type": "text", "text": "Is there an obstacle in the robot's path?"}
        ]}
    ]

    inputs = proc.apply_chat_template(
        messages, add_generation_prompt=True, tokenize=True,
        return_dict=True, return_tensors="pt"
    ).to(model.device)

    with torch.inference_mode():
        out = model.generate(**inputs, max_new_tokens=80, do_sample=False)

    raw = proc.decode(out[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True)

    # Extract JSON from model output:
    match = re.search(r'\{.*?\}', raw, re.DOTALL)
    if match:
        result = json.loads(match.group())
    else:
        result = {"obstacle": True, "description": raw[:100], "confidence": 0.5}

    result["latency_ms"] = (time.perf_counter() - t0) * 1000
    return result

# Usage:
proc, model = build_perception_model()
image = PIL.Image.open("/tmp/robot_view.jpg").convert("RGB")
result = perceive(image, proc, model)
print(result)
# → {"obstacle": false, "description": "Clear path, table and boxes visible", "confidence": 0.9, "latency_ms": 582}

Capstone verification checklist

• TTFT ≤ 1000 ms measured end-to-end (capture → JSON response) on Orin
• Obstacle=true rate for images with blockers ≥ 90% (test 20 samples)
• False positive rate (obstacle=true on clear path) ≤ 10% (test 20 samples)
• Continuous operation for 5 minutes without memory growth (check tegrastats)
• KV cache memory stays bounded (set --ctx-size 4096 to cap it)
• Pipeline produces valid JSON for every response (test 50 diverse images)

---

Key takeaways

• SigLIP-400M encodes images as 256 visual tokens (224×224) or 1024 tokens (448×448) that are prepended to the text context as standard input tokens — no special attention masking required.
• TTFT for Gemma 4 4B-VL on Orin is approximately 600 ms (80 ms SigLIP + 500 ms prefill + 20 ms decode), fitting within a 1 Hz robot control loop.
• Always keep the vision encoder in FP16; only quantize the text decoder to INT4. The SigLIP weights (880 MB FP16) add ~1 GB to the deployment footprint but are not large enough to justify the accuracy loss from INT4.
• llama.cpp with --mmproj is the fastest path to a working VLM deployment; Ollama wraps this for ease of use. Use HuggingFace Transformers for prototyping only (BF16 = 8.2 GB text decoder alone).
• DLA for SigLIP on Jetson Thor reduces encoder power by 7.5× at the cost of 50% higher latency — use on mobile or drone deployments where power dominates.
• The GStreamer hardware pipeline on Jetson resizes frames in the ISP/video converter, eliminating software resize cost and delivering 224×224 frames directly to CUDA.
• The key latency knob is not decode speed but prefill — 256 visual tokens dominate TTFT. Reduce text prompt length to save prefill time on the text side.

---

Course completion checklist

You have completed the Gemma 4 Edge Deployment course when you can:

• Explain the KV-cache math for Gemma 4's interleaved attention and compute the exact footprint for any (model, batch, context) triple.
• Calibrate and produce a Q4_K_M GGUF from a Gemma 4 BF16 checkpoint with ≤ 0.3 MMLU degradation.
• Deploy Gemma 4 4B (text only) on Jetson Orin with llama.cpp, measured at ≥ 45 tok/s.
• Configure speculative decoding with Gemma 4 1B as draft, measure acceptance length τ, and verify the speedup matches the τ / (1 + K×c) formula.
• Deploy Gemma 4 4B-VL on Jetson Orin, measure end-to-end TTFT, and verify it meets a ≤ 1 Hz perception loop requirement.
• Complete the capstone perception module, pass the checklist, and report latency_ms in your notes.

---

References

• "Sigmoid Loss for Language Image Pre-Training" (Zhai et al., 2023) — arXiv:2303.15343
• "Gemma 4 Technical Report" (Google DeepMind, April 2025) — check ai.google.dev/gemma
• "PaliGemma 2 VLM" (Beyer et al., 2024) — arXiv:2412.03555 (Gemma 4-VL builds on PaliGemma architecture)
• llama.cpp multimodal (--mmproj) — github.com/ggml-org/llama.cpp/tree/master/examples/llava
• Google AI Edge / LiteRT — ai.google.dev/edge/litert
• Jetson Thor SDK (DLA v3) — NVIDIA JetPack 7.x SDK documentation
• Qwen Inference Optimization → Lecture 05 — multimodal inference stack, same runtime layer

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Current as of 2026-06

Gemma 4 4B-VL available at google/gemma-4-4b-it (multimodal variant) on HuggingFace. SigLIP is integrated into the model; no separate download needed. llama.cpp mmproj support for Gemma 4-VL: check llama.cpp releases after v0.4.0. LiteRT AI Edge Torch: requires ai-edge-torch >= 0.3.0 for Gemma 4 export. Ollama vision support: ollama pull gemma4:4b (check ollama.com/library for updated tags). Jetson AGX Thor: JetPack 7.0+ required for DLA v3 and unified memory >128 GB.

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