Instructions to use IsValorum/Nex-N2.5-mini-APEX-I-MiniPlus-V1-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use IsValorum/Nex-N2.5-mini-APEX-I-MiniPlus-V1-GGUF with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf IsValorum/Nex-N2.5-mini-APEX-I-MiniPlus-V1-GGUF:Q8_0
# Run inference directly in the terminal:
llama cli -hf IsValorum/Nex-N2.5-mini-APEX-I-MiniPlus-V1-GGUF:Q8_0

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf IsValorum/Nex-N2.5-mini-APEX-I-MiniPlus-V1-GGUF:Q8_0
# Run inference directly in the terminal:
llama cli -hf IsValorum/Nex-N2.5-mini-APEX-I-MiniPlus-V1-GGUF:Q8_0

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf IsValorum/Nex-N2.5-mini-APEX-I-MiniPlus-V1-GGUF:Q8_0
# Run inference directly in the terminal:
./llama-cli -hf IsValorum/Nex-N2.5-mini-APEX-I-MiniPlus-V1-GGUF:Q8_0

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf IsValorum/Nex-N2.5-mini-APEX-I-MiniPlus-V1-GGUF:Q8_0
# Run inference directly in the terminal:
./build/bin/llama-cli -hf IsValorum/Nex-N2.5-mini-APEX-I-MiniPlus-V1-GGUF:Q8_0

Use Docker

docker model run hf.co/IsValorum/Nex-N2.5-mini-APEX-I-MiniPlus-V1-GGUF:Q8_0

LM Studio
Jan

vLLM

How to use IsValorum/Nex-N2.5-mini-APEX-I-MiniPlus-V1-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "IsValorum/Nex-N2.5-mini-APEX-I-MiniPlus-V1-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "IsValorum/Nex-N2.5-mini-APEX-I-MiniPlus-V1-GGUF",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/IsValorum/Nex-N2.5-mini-APEX-I-MiniPlus-V1-GGUF:Q8_0

Ollama
How to use IsValorum/Nex-N2.5-mini-APEX-I-MiniPlus-V1-GGUF with Ollama:
```
ollama run hf.co/IsValorum/Nex-N2.5-mini-APEX-I-MiniPlus-V1-GGUF:Q8_0
```
Unsloth Desktop

How to use IsValorum/Nex-N2.5-mini-APEX-I-MiniPlus-V1-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf IsValorum/Nex-N2.5-mini-APEX-I-MiniPlus-V1-GGUF:Q8_0

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "IsValorum/Nex-N2.5-mini-APEX-I-MiniPlus-V1-GGUF:Q8_0"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use IsValorum/Nex-N2.5-mini-APEX-I-MiniPlus-V1-GGUF with Docker Model Runner:
```
docker model run hf.co/IsValorum/Nex-N2.5-mini-APEX-I-MiniPlus-V1-GGUF:Q8_0
```

Lemonade

How to use IsValorum/Nex-N2.5-mini-APEX-I-MiniPlus-V1-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull IsValorum/Nex-N2.5-mini-APEX-I-MiniPlus-V1-GGUF:Q8_0

Run and chat with the model

lemonade run user.Nex-N2.5-mini-APEX-I-MiniPlus-V1-GGUF-Q8_0

List all available models

lemonade list

Hermes Agent

How to use IsValorum/Nex-N2.5-mini-APEX-I-MiniPlus-V1-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf IsValorum/Nex-N2.5-mini-APEX-I-MiniPlus-V1-GGUF:Q8_0

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default IsValorum/Nex-N2.5-mini-APEX-I-MiniPlus-V1-GGUF:Q8_0

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use IsValorum/Nex-N2.5-mini-APEX-I-MiniPlus-V1-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf IsValorum/Nex-N2.5-mini-APEX-I-MiniPlus-V1-GGUF:Q8_0

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "IsValorum/Nex-N2.5-mini-APEX-I-MiniPlus-V1-GGUF:Q8_0" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

IsValorum commited on 9 days ago

Commit

019801d

verified ·

1 Parent(s): 65dcb68

Remove exact laptop and RAM-offload throughput figures

Browse files

Files changed (1) hide show

README.md +7 -7

README.md CHANGED Viewed

@@ -39,10 +39,10 @@ quantized_by: IsValorum
 > This repository hosts the **MiniPlus V1** edition of **Nex-N2.5-mini**. Our releases are precision-engineered for specific hardware budgets and memory topologies. **V1 is NOT obsolete or inferior; it represents our leanest, most agile operating profile:**
 >
 > - **MiniPlus V1 (Lean & Agile Profile):** Highly compact footprint with uncompressed `F32` router gates, a fully armored `Q6_K` output head, `Q8_0` attention gates, and `IQ3_XXS` core experts. **Both V1 and V2.1 run flawlessly with the vast majority of the model residing in system RAM (DDR4/DDR5)**, thanks to linear CPU-friendly vectorization that avoids lookup stalls. V1 is dramatically superior to generic community APEX-I-Mini releases (which crush core reasoning down to 2-bit `IQ2_S`) and flat 3-bit quants.
-> - **MiniPlus V2.1 (System RAM Streaming Specialist with Deep Context):** Specially prepared to run **totally or partially in system RAM (DDR4/DDR5)** across massive multimodal and agentic context windows (up to 256k tokens). Upgrades all 40 shared foundation experts to `Q5_K`, armors attention gates in `Q8_0`, and uses linear CPU-friendly vectorization that eliminates AVX2 lookup stalls (+24 to 28+ tok/s). Depending on your processor and memory bandwidth (DDR4/DDR5), **streaming generation in system RAM can be almost as fast as having everything in VRAM**, while supporting deep context keeping the dedicated `Q8_0` multimodal vision projector (`mmproj`) explicitly loaded in GPU VRAM for instant screen parsing and OCR. All for **only approx. 180 MB more** (approx. 13.74 GiB vs approx. 13.56 GiB)—an overhead that is completely negligible in system RAM.
 >
 > **Which one should you choose? (Official Recommendation: V2.1)**
-> - **⭐ PRIMARY RECOMMENDATION — [Nex-N2.5-mini APEX-I-MiniPlus V2.1](https://huggingface.co/IsValorum/Nex-N2.5-mini-APEX-I-MiniPlus-V2.1-GGUF):** For virtually all users and deployments, **V2.1 is the strictly recommended release**. V2.1 reports a **Perplexity of 6.4725 ± 0.1635** (ΔPPL ≈ +0.07 from unquantized baseline (approx. 6.40)), matching the token fidelity of **Q5_K / Q6_K** class quantizations while weighing only **approx. 14.7 GB** (same footprint as Q3_K_M). Furthermore, it completely eliminates AVX2 CPU stalls, providing blistering **+24 to 28+ tok/s streaming** under system RAM offload.
 > - **MiniPlus V1 Legacy:** Maintained for architectural transparency and users seeking specialized configurations for their workflow.
 >
 > *Both editions are handcrafted and vastly outperform flat 3-bit quants and generic community APEX-I-Mini releases.*
@@ -62,7 +62,7 @@ quantized_by: IsValorum
 - [Comparative Quantization Analysis (vs. Flat Quants & Generic APEX)](#comparative-analysis)
 - [Independent Benchmark of the APEX-I-MiniPlus Family (Occamy V2 Reference)](#independent-benchmark)
 - [Bundled Q8_0 High-Precision Vision Projector](#vision-projector)
-- [Everyday Laptop Benchmarks (DDR4 / DDR5 RAM)](#laptop-benchmarks)
 - [The 24GB Miracle: Full 256K Context Runs In VRAM!](#context-scaling)
 - [Hardware Throughput & Offload Benchmarks (RTX 30 / 40 / 50)](#throughput-projections)
 - [Recommended Generation Parameters (Creator Official)](#generation-parameters)
@@ -108,7 +108,7 @@ The evaluation tested the APEX-I hybrid MoE engine across **348 unseeded trials*
 <a id="comparative-analysis"></a>
 ## Comparative Quantization Analysis (vs. Flat Quants & Generic APEX)
-Also, don't confuse **APEX-I-MiniPlus (Standard)** with a generic baseline APEX-I-Mini. Traditional APEX-I-Mini drops core experts aggressively to 2-bit `IQ2_S` and leaves `output.weight` at 3-bit `Q3_K_M`, which creates a noticeable perplexity hit on complex reasoning tasks. Standard MiniPlus avoids that degradation floor while keeping boundary layers in linear `Q3_K` for single-cycle vectorized AVX2 CPU dequantization (hitting 23 to 26+ tok/s on DDR4 laptops), while protecting output in `Q6_K` and routers in `F32`.
 To put the numbers in perspective: this cuts nearly **2 GB off a flat 3-bit quant** (approx. 15.6 GB), and weighs only about **approx. 1 GB more than a generic APEX-I-Mini** (approx. 12.5 GB). For that single extra gigabyte of VRAM, you get a massive jump in reasoning and syntactic stability while maximizing CPU/RAM execution throughput.
@@ -122,7 +122,7 @@ Take a look at the tensor-by-tensor comparison table below to inspect the exact
 | **Attention Gates (`attn_gate.weight`)** | Blindly compressed to 3-bit | Compressed to **`Q3_K`** (middle) / **`Q4_K`** (edges) | **`Q4_K`** / **`Q8_0`** (linear high-precision) | **Attention Routing Dynamics:** High-precision linear gating modulating query-key projections without CPU dequantization latency. |
 | **Shared Foundation Expert (`ffn_*_shexp`)** | Flat **`IQ3_S` / `Q3_K_S`** (3.44 BPW) | Linear **`Q4_K`** (middle) / **`Q5_K`** (edges) | Linear **`Q4_K`** (middle) / **`Q5_K`** (edges) + `imatrix` | **Foundational Knowledge Stability:** Keeps the universal pathway in high-fidelity linear blocks, eliminating quantization drift while maintaining rapid single-cycle dequantization. |
 | **Core MoE Layers (Middle: 10–29)** | Flat **`IQ3_S` / `Q3_K_S`** (uniform bit-rate across all layers) | Aggressive **`IQ2_S` (2.50 BPW)** | **`IQ3_XXS` (3.06 BPW) + calibrated `imatrix`** | **Above the Quality Threshold:** Generic 2-bit `IQ2_S` baselines drop below the critical quality floor for 35B MoEs, resulting in perplexity spikes on reasoning tasks. Our `IQ3_XXS` with imatrix achieves deep compression (272 MiB → 98 MiB per block) without sacrificing logic. |
-| **Edge MoE Layers (Layers 0–9 & 30–39)** | Flat **`IQ3_S` / `Q3_K_S`** (no layer-wise gradient) | `Q3_K` (limited to first/last 5 layers only: L0–4, L35–39) | **`Q3_K` (expanded to 10 input & 10 output layers)** | **AVX2 Single-Cycle Speed:** Expanded 10+10 layer protection using linear `Q3_K` blocks enables single-cycle vectorized AVX2 CPU dequantization, unlocking **23 to 26+ tok/s** on budget DDR4 laptops. |
 | **Multimodal Vision (`mmproj`)** | Often omitted, or left as uncompressed **`FP16` (approx. 900 MB)** | Often omitted or separate uncompressed `FP16` | **Bundled `Q8_0` (582 MB)** with **27 critical F32/F16 fallbacks** | **Saves approx. 320 MB VRAM with Zero Loss:** Handcrafted quantization preserves normalization and bias tensors in F32/F16, ensuring razor-sharp OCR, DOM viewport reading, and coordinate detection without visual noise. |
 | **Normalization & Biases** | Often degraded | Standard | **`F32` uncompressed** | **Numerical Stability:** Prevents cumulative floating-point underflow/overflow across deep 40-layer computation. |
@@ -138,7 +138,7 @@ Unlike text-only MoEs, Nex-N2.5-mini is designed for computer use, visual ground
 ---
 <a id="laptop-benchmarks"></a>
-## Everyday Laptop Benchmarks (23–26+ tok/s on DDR4)
 ### *Empirically Verified in Unsloth Studio*
 - **GPU VRAM Offload:** Uses only **3.8 GB VRAM** (fits effortlessly on budget 4GB and 6GB laptop GPUs like the RTX 4050, 3050, or older 1660 Ti/2060).
@@ -168,7 +168,7 @@ Unlike text-only MoEs, Nex-N2.5-mini is designed for computer use, visual ground
 | **NVIDIA RTX 5080 / 5090 (Blackwell)** | Full GPU (`-ngl 99`) | **approx. 247 – 251 tok/s** | **2,800 – 3,900+ tok/s** | Empirically verified on RTX 5090 by zephel01 (Occamy V2 Reference) |
 | **NVIDIA RTX 4090 (24GB GDDR6X)** | Full GPU (`-ngl 99`) + mmproj | **75 – 100+ tok/s** | **1,700 – 2,500+ tok/s** | Real-time computer-use screen analysis & tool calling |
 | **NVIDIA RTX 3090 (24GB GDDR6)** | Full GPU (`-ngl 99`) + mmproj | **62 – 78+ tok/s** | **1,350 – 1,950+ tok/s** | Full 256k multi-modal context in dedicated VRAM |
-| **Consumer Laptop (4GB GPU + 32GB RAM)**| Hybrid Offload | **20 – 24+ tok/s** | **300 – 420+ tok/s** | Smooth streaming from system DDR4/DDR5 RAM |
 <a id="generation-parameters"></a>
 ### ⚙️ Recommended Generation Parameters (nex-agi Official)

 > This repository hosts the **MiniPlus V1** edition of **Nex-N2.5-mini**. Our releases are precision-engineered for specific hardware budgets and memory topologies. **V1 is NOT obsolete or inferior; it represents our leanest, most agile operating profile:**
 >
 > - **MiniPlus V1 (Lean & Agile Profile):** Highly compact footprint with uncompressed `F32` router gates, a fully armored `Q6_K` output head, `Q8_0` attention gates, and `IQ3_XXS` core experts. **Both V1 and V2.1 run flawlessly with the vast majority of the model residing in system RAM (DDR4/DDR5)**, thanks to linear CPU-friendly vectorization that avoids lookup stalls. V1 is dramatically superior to generic community APEX-I-Mini releases (which crush core reasoning down to 2-bit `IQ2_S`) and flat 3-bit quants.
+> - **MiniPlus V2.1 (System RAM Streaming Specialist with Deep Context):** Specially prepared to run **totally or partially in system RAM (DDR4/DDR5)** across massive multimodal and agentic context windows (up to 256k tokens). Upgrades all 40 shared foundation experts to `Q5_K`, armors attention gates in `Q8_0`, and uses linear CPU-friendly vectorization that eliminates AVX2 lookup stalls. Depending on your processor and memory bandwidth (DDR4/DDR5), **streaming generation in system RAM can be almost as fast as having everything in VRAM**, while supporting deep context keeping the dedicated `Q8_0` multimodal vision projector (`mmproj`) explicitly loaded in GPU VRAM for instant screen parsing and OCR. All for **only approx. 180 MB more** (approx. 13.74 GiB vs approx. 13.56 GiB)—an overhead that is completely negligible in system RAM.
 >
 > **Which one should you choose? (Official Recommendation: V2.1)**
+> - **⭐ PRIMARY RECOMMENDATION — [Nex-N2.5-mini APEX-I-MiniPlus V2.1](https://huggingface.co/IsValorum/Nex-N2.5-mini-APEX-I-MiniPlus-V2.1-GGUF):** For virtually all users and deployments, **V2.1 is the strictly recommended release**. V2.1 reports a **Perplexity of 6.4725 ± 0.1635** (ΔPPL ≈ +0.07 from unquantized baseline (approx. 6.40)), matching the token fidelity of **Q5_K / Q6_K** class quantizations while weighing only **approx. 14.7 GB** (same footprint as Q3_K_M). Furthermore, it eliminates AVX2 CPU stalls and is optimized for system RAM offload.
 > - **MiniPlus V1 Legacy:** Maintained for architectural transparency and users seeking specialized configurations for their workflow.
 >
 > *Both editions are handcrafted and vastly outperform flat 3-bit quants and generic community APEX-I-Mini releases.*
 - [Comparative Quantization Analysis (vs. Flat Quants & Generic APEX)](#comparative-analysis)
 - [Independent Benchmark of the APEX-I-MiniPlus Family (Occamy V2 Reference)](#independent-benchmark)
 - [Bundled Q8_0 High-Precision Vision Projector](#vision-projector)
+- [Everyday Laptop Guidance (DDR4 / DDR5 RAM)](#laptop-benchmarks)
 - [The 24GB Miracle: Full 256K Context Runs In VRAM!](#context-scaling)
 - [Hardware Throughput & Offload Benchmarks (RTX 30 / 40 / 50)](#throughput-projections)
 - [Recommended Generation Parameters (Creator Official)](#generation-parameters)
 <a id="comparative-analysis"></a>
 ## Comparative Quantization Analysis (vs. Flat Quants & Generic APEX)
+Also, don't confuse **APEX-I-MiniPlus (Standard)** with a generic baseline APEX-I-Mini. Traditional APEX-I-Mini drops core experts aggressively to 2-bit `IQ2_S` and leaves `output.weight` at 3-bit `Q3_K_M`, which creates a noticeable perplexity hit on complex reasoning tasks. Standard MiniPlus avoids that degradation floor while keeping boundary layers in linear `Q3_K` for single-cycle vectorized AVX2 CPU dequantization (optimized for DDR4/DDR5 laptop streaming), while protecting output in `Q6_K` and routers in `F32`.
 To put the numbers in perspective: this cuts nearly **2 GB off a flat 3-bit quant** (approx. 15.6 GB), and weighs only about **approx. 1 GB more than a generic APEX-I-Mini** (approx. 12.5 GB). For that single extra gigabyte of VRAM, you get a massive jump in reasoning and syntactic stability while maximizing CPU/RAM execution throughput.
 | **Attention Gates (`attn_gate.weight`)** | Blindly compressed to 3-bit | Compressed to **`Q3_K`** (middle) / **`Q4_K`** (edges) | **`Q4_K`** / **`Q8_0`** (linear high-precision) | **Attention Routing Dynamics:** High-precision linear gating modulating query-key projections without CPU dequantization latency. |
 | **Shared Foundation Expert (`ffn_*_shexp`)** | Flat **`IQ3_S` / `Q3_K_S`** (3.44 BPW) | Linear **`Q4_K`** (middle) / **`Q5_K`** (edges) | Linear **`Q4_K`** (middle) / **`Q5_K`** (edges) + `imatrix` | **Foundational Knowledge Stability:** Keeps the universal pathway in high-fidelity linear blocks, eliminating quantization drift while maintaining rapid single-cycle dequantization. |
 | **Core MoE Layers (Middle: 10–29)** | Flat **`IQ3_S` / `Q3_K_S`** (uniform bit-rate across all layers) | Aggressive **`IQ2_S` (2.50 BPW)** | **`IQ3_XXS` (3.06 BPW) + calibrated `imatrix`** | **Above the Quality Threshold:** Generic 2-bit `IQ2_S` baselines drop below the critical quality floor for 35B MoEs, resulting in perplexity spikes on reasoning tasks. Our `IQ3_XXS` with imatrix achieves deep compression (272 MiB → 98 MiB per block) without sacrificing logic. |
+| **Edge MoE Layers (Layers 0–9 & 30–39)** | Flat **`IQ3_S` / `Q3_K_S`** (no layer-wise gradient) | `Q3_K` (limited to first/last 5 layers only: L0–4, L35–39) | **`Q3_K` (expanded to 10 input & 10 output layers)** | **AVX2 Single-Cycle Speed:** Expanded 10+10 layer protection using linear `Q3_K` blocks enables single-cycle vectorized AVX2 CPU dequantization, supporting efficient streaming on budget DDR4/DDR5 laptops. |
 | **Multimodal Vision (`mmproj`)** | Often omitted, or left as uncompressed **`FP16` (approx. 900 MB)** | Often omitted or separate uncompressed `FP16` | **Bundled `Q8_0` (582 MB)** with **27 critical F32/F16 fallbacks** | **Saves approx. 320 MB VRAM with Zero Loss:** Handcrafted quantization preserves normalization and bias tensors in F32/F16, ensuring razor-sharp OCR, DOM viewport reading, and coordinate detection without visual noise. |
 | **Normalization & Biases** | Often degraded | Standard | **`F32` uncompressed** | **Numerical Stability:** Prevents cumulative floating-point underflow/overflow across deep 40-layer computation. |
 ---
 <a id="laptop-benchmarks"></a>
+## Everyday Laptop Guidance
 ### *Empirically Verified in Unsloth Studio*
 - **GPU VRAM Offload:** Uses only **3.8 GB VRAM** (fits effortlessly on budget 4GB and 6GB laptop GPUs like the RTX 4050, 3050, or older 1660 Ti/2060).
 | **NVIDIA RTX 5080 / 5090 (Blackwell)** | Full GPU (`-ngl 99`) | **approx. 247 – 251 tok/s** | **2,800 – 3,900+ tok/s** | Empirically verified on RTX 5090 by zephel01 (Occamy V2 Reference) |
 | **NVIDIA RTX 4090 (24GB GDDR6X)** | Full GPU (`-ngl 99`) + mmproj | **75 – 100+ tok/s** | **1,700 – 2,500+ tok/s** | Real-time computer-use screen analysis & tool calling |
 | **NVIDIA RTX 3090 (24GB GDDR6)** | Full GPU (`-ngl 99`) + mmproj | **62 – 78+ tok/s** | **1,350 – 1,950+ tok/s** | Full 256k multi-modal context in dedicated VRAM |
+| **Consumer Laptop (4GB GPU + 32GB RAM)**| Hybrid Offload | Hardware-dependent | Hardware-dependent | Smooth streaming from system DDR4/DDR5 RAM |
 <a id="generation-parameters"></a>
 ### ⚙️ Recommended Generation Parameters (nex-agi Official)