Instructions to use TypeScript/FastBrain-9B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use TypeScript/FastBrain-9B with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="TypeScript/FastBrain-9B")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("TypeScript/FastBrain-9B")
model = AutoModelForMultimodalLM.from_pretrained("TypeScript/FastBrain-9B", device_map="auto")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
inputs = processor.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=256)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use TypeScript/FastBrain-9B 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 TypeScript/FastBrain-9B:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf TypeScript/FastBrain-9B:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf TypeScript/FastBrain-9B:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf TypeScript/FastBrain-9B:Q4_K_M

Build from source code

git clone https://github.com/ggml-org/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-app
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama serve -hf TypeScript/FastBrain-9B:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama cli -hf TypeScript/FastBrain-9B:Q4_K_M

Use Docker

docker model run hf.co/TypeScript/FastBrain-9B:Q4_K_M

Llama new
LM Studio
Jan

vLLM

How to use TypeScript/FastBrain-9B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "TypeScript/FastBrain-9B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "TypeScript/FastBrain-9B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/TypeScript/FastBrain-9B:Q4_K_M

SGLang

How to use TypeScript/FastBrain-9B with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "TypeScript/FastBrain-9B" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "TypeScript/FastBrain-9B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "TypeScript/FastBrain-9B" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "TypeScript/FastBrain-9B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use TypeScript/FastBrain-9B with Ollama:
```
ollama run hf.co/TypeScript/FastBrain-9B:Q4_K_M
```
Unsloth Desktop

How to use TypeScript/FastBrain-9B with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf TypeScript/FastBrain-9B:Q4_K_M

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": "TypeScript/FastBrain-9B:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use TypeScript/FastBrain-9B with Docker Model Runner:
```
docker model run hf.co/TypeScript/FastBrain-9B:Q4_K_M
```

Lemonade

How to use TypeScript/FastBrain-9B with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull TypeScript/FastBrain-9B:Q4_K_M

Run and chat with the model

lemonade run user.FastBrain-9B-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use TypeScript/FastBrain-9B with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf TypeScript/FastBrain-9B:Q4_K_M

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 TypeScript/FastBrain-9B:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use TypeScript/FastBrain-9B with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf TypeScript/FastBrain-9B:Q4_K_M

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 "TypeScript/FastBrain-9B:Q4_K_M" \
  --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"

TypeScript commited on 3 days ago

Commit

7ed491b

verified ·

1 Parent(s): c6fe209

Make benchmark and hardware tables compact to fit full-width on first screen with FastBrain-9B first

Browse files

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README.md +37 -37

README.md CHANGED Viewed

@@ -59,20 +59,20 @@ Evaluated on the official **Cloudflare Decision Index (v0.2.1)** across **10 ben
 ### Summary Comparison by Model Parameter Scale
-| Benchmark | Metric | `Laya`<br>*(0.4B / 421M)* | `DiffusionGemma Jev`<br>*(26B-A4B · 4B Act)* | `Clef-flash`<br>*(9.4B Base)* | `Kev 9B`<br>*(9B Dense)* | `Jev`<br>*(27B Dense)* | `Clef`<br>*(27.4B Flagship)* | **FastBrain-9B**<br>*(9.4B · Ours)* |
-| :--- | :--- | :---: | :---: | :---: | :---: | :---: | :---: | :---: |
-| **Parameter Scale** | **Total (Active)** | `0.42B` | `25.8B (4B MoE)` | `9.41B` | `9.0B` | `27.0B` | `27.36B` | **`9.41B`** |
-| **BFCL** | Case Exact | 95.87% | 97.64% | 98.76% | 97.58% | 98.58% | 98.47% | **98.76% (#1)** |
-| **ToolRet** | nDCG@10 | 51.31% | 45.28% | 67.26% | 65.72% | 65.50% | 68.18% | **67.26% (#2 · #1 9B)** |
-| **API-Bank** | Accuracy | 86.22% | 85.24% | 93.11% | 89.76% | 90.35% | 91.93% | **93.70% (#1)** |
-| **BANKING77** | Macro-F1 | 62.66% | 93.80% | 93.69% | 91.77% | 93.58% | 94.07% | **93.77% (#1 9B)** |
-| **CLINC150+OOS** | Macro-F1 | 73.79% | 94.64% | 66.77% | 96.65% | 96.44% | 97.43% | **98.44% (#1)** |
-| **Home appliances** | Case Exact | 23.86% | 68.18% | 96.59% | 88.64% | 88.64% | 96.59% | **97.73% (#1)** |
-| **BRIGHT** | nDCG@10 | 25.00% | 18.69% | 38.49% | 38.53% | 43.37% | 48.03% | **38.58% (#1 9B)** |
-| **Amazon ESCI** | Macro-F1 | 52.84% | 56.83% | 57.39% | 53.75% | 56.60% | 59.30% | **57.40% (#2 · #1 9B)** |
-| **PhishNChips** | Accuracy | 60.45% | 55.65% | 71.95% | 72.10% | 88.20% | 91.70% | **78.75% (#1 9B)** |
-| **When2Call** | Macro-F1 | 48.92% | 75.44% | 65.58% | 70.64% | 81.91% | 72.37% | **81.13% (#2 · #1 9B)** |
-| **Macro Mean (10)** | **Average** | **58.09%** | **69.14%** | **74.96%** | **76.51%** | **80.32%** | **81.81%** | **80.55% (#2 · #1 9B)** |
 ---
@@ -248,34 +248,34 @@ print(resp["usage"])              # {'input_tokens': ..., 'output_tokens': 0, 't
 Evaluated across a stratified 10-benchmark Decision Index test suite (**`203` multi-constraint requests / `1,473` scored schema questions** spanning `choice`, `noul`, and `score` heads), `FastBrain-9B-Q8_0.gguf` (`9.66 GB`) is **virtually lossless (`99.86%` Top-1 decision agreement with native `bfloat16`, `-0.03 pp` score delta)**, while `FastBrain-9B-Q4_K_M.gguf` (`6.73 GB`, with `dec.*` head preserved in `Q8_0`) retains **`98.78%` Top-1 agreement** and **`-0.20 pp`** calibrated 10-task mean:
-| Benchmark / Metric | Native PyTorch `bfloat16` (`18.82 GB`) | `FastBrain-9B-Q8_0.gguf` (`9.66 GB`) | `FastBrain-9B-Q4_K_M.gguf` (`6.73 GB`, `Q8_0` Head) |
 | :--- | :---: | :---: | :---: |
-| **Top-1 Decision Agreement vs. `bfloat16`** (`1,473` questions) | `100.00%` *(Reference)* | **`99.86%` (`1,471 / 1,473` identical)** | **`98.78%` (`1,455 / 1,473` identical)** |
-| **Probability Mean Absolute Error (`|p_quant - p_bf16|`)** | `0.00000` | **`0.00203` (`0.20%`)** | **`0.00387` (`0.39%`)** |
-| **10-Benchmark Mean (Single-Checkpoint Raw)** | **`82.37%`** | **`82.34%` (`-0.03 pp`)** | **`82.56%` (`+0.19 pp`)** |
-| **10-Benchmark Mean (With Margin Calibration)** | **`83.68%`** | **`83.65%` (`-0.03 pp`)** | **`83.48%` (`-0.20 pp`)** |
-| ├─ **1. BFCL** *(AST Tool Selection)* | `100.00%` | **`100.00%`** (`0.00`) | **`100.00%`** (`0.00`) |
-| ├─ **2. ToolRet** *(20-Candidate Tool Reranking, `score`)* | `73.15%` | **`73.70%`** (`+0.55`) | **`75.60%`** (`+2.45`) |
-| ├─ **3. API-Bank** *(Multi-Step Stateful Tool Calling)* | `95.00%` | **`95.00%`** (`0.00`) | **`95.00%`** (`0.00`) |
-| ├─ **4. BANKING77** *(77-Way Fine-Grained Intent)* | `100.00%` | **`100.00%`** (`0.00`) | **`100.00%`** (`0.00`) |
-| ├─ **5. CLINC150+OOS** *(Intent + Open-Set `noul` Abstention)* | `93.94%` | **`93.94%`** (`0.00`) | **`93.94%`** (`0.00`) |
-| ├─ **6. Home Appliances** *(Embedded Device Control)* | `90.00%` | **`90.00%`** (`0.00`) | **`90.00%`** (`0.00`) |
-| ├─ **7. BRIGHT** *(Reasoning-Intensive Document Ranking)* | `41.93%` | **`41.00%`** (`-0.93`) | **`40.31%`** (`-1.62`) |
-| ├─ **8. Amazon ESCI** *(Fine-Grained E-Commerce Relevance)* | `73.89%` | **`73.89%`** (`0.00`) | **`75.79%`** (`+1.90`) |
-| ├─ **9. PhishNChips** *(9-Probe Joint Security Inspection)* | `85.00%` | **`85.00%`** (`0.00`) | **`85.00%`** (`0.00`) |
-| └─ **10. When2Call** *(4-Way Tool Governance)* | `75.00%` *(Raw `70.83%`)* | **`75.00%`** *(Raw `70.83%`, `0.00`)* | **`72.92%`** *(Raw `70.83%`, `-2.08`)* |
-### 2. Measured Hardware Footprint & Inference Speed (`llama-server b11430` CUDA 13.4)
 Because `FastBrain-9B` scores all schema questions and candidate options in a **single non-autoregressive prefill forward pass (`output_tokens = 0`)**, `llama-server` achieves **8,900–12,100+ input tokens/sec** (`31–109 ms` end-to-end HTTP latency) on server GPUs:
-| Workload / Metric | `Q8_0.gguf`<br>*(RTX PRO 6000)* | `Q4_K_M.gguf`<br>*(RTX PRO 6000)* | `Q8_0.gguf`<br>*(Colab Free T4 16GB)* | `Q4_K_M.gguf`<br>*(Colab Free T4 16GB)* | Native `bfloat16`<br>*(RTX PRO 6000)* |
 | :--- | :---: | :---: | :---: | :---: | :---: |
-| **Model Load Time** | **`2.56 s`** | **`2.00 s`** | **`5.43 s`** | **`5.10 s`** | `~11.5 s` |
-| **Peak GPU VRAM** | **`12.55 GiB`** *(16K ctx)* | **`10.30 GiB`** *(16K ctx)* | **`9.03 GiB` (`9,245 MiB`)** *(4K ctx)* | **`6.78 GiB` (`6,943 MiB`)** *(4K ctx)* | `20.3 – 24.7 GiB` |
-| **Workload 1: Intent Routing + OOS** (`293 tok`, `K=2`) | **`32.82 ms`** (`8,928 tok/s`) | **`31.69 ms`** (`9,245 tok/s`) | **`349.32 ms`** (`839 tok/s`) | **`342.98 ms`** (`854 tok/s`) | `~35.0 ms` |
-| **Workload 2: Tool Governance (`When2Call`)** (`1,068 tok`, `K=1`) | **`87.78 ms`** (`12,167 tok/s`) | **`89.34 ms`** (`11,955 tok/s`) | **`1,149.81 ms`** (`929 tok/s`) | **`1,154.31 ms`** (`925 tok/s`) | `~92.0 ms` |
-| **Workload 3: Security Multi-Probe (`PhishNChips`)** (`1,303 tok`, `K=9`) | **`108.55 ms`** (`12,004 tok/s`) | **`109.97 ms`** (`11,849 tok/s`) | **`1,430.81 ms`** (`911 tok/s`) | **`1,440.52 ms`** (`905 tok/s`) | `~115.0 ms` |
 | Format / Precision Tier | Weight Footprint | Peak VRAM (`4K`–`16K` ctx) | Minimum Recommended Hardware |
 | :--- | :---: | :---: | :--- |

 ### Summary Comparison by Model Parameter Scale
+| Benchmark | **FastBrain-9B**<br>*(9.4B · Ours)* | Clef-flash<br>*(9.4B Base)* | Kev 9B<br>*(9B)* | Clef<br>*(27.4B)* | Jev<br>*(27B)* | DiffGemma<br>*(26B-A4B)* | Laya<br>*(0.4B)* |
+| :--- | :---: | :---: | :---: | :---: | :---: | :---: | :---: |
+| **Params** | **9.41B** | 9.41B | 9.0B | 27.36B | 27.0B | 25.8B *(4B)* | 0.42B |
+| **BFCL**<br>*(Case Exact)* | **98.76%**<br>*(#1)* | **98.76%** | 97.58% | 98.47% | 98.58% | 97.64% | 95.87% |
+| **ToolRet**<br>*(nDCG@10)* | **67.26%**<br>*(#1 9B)* | **67.26%** | 65.72% | **68.18%** | 65.50% | 45.28% | 51.31% |
+| **API-Bank**<br>*(Accuracy)* | **93.70%**<br>*(#1)* | 93.11% | 89.76% | 91.93% | 90.35% | 85.24% | 86.22% |
+| **BANKING77**<br>*(Macro-F1)* | **93.77%**<br>*(#1 9B)* | 93.69% | 91.77% | **94.07%** | 93.58% | 93.80% | 62.66% |
+| **CLINC150+OOS**<br>*(Macro-F1)* | **98.44%**<br>*(#1)* | 66.77% | 96.65% | 97.43% | 96.44% | 94.64% | 73.79% |
+| **Home Appl.**<br>*(Case Exact)* | **97.73%**<br>*(#1)* | 96.59% | 88.64% | 96.59% | 88.64% | 68.18% | 23.86% |
+| **BRIGHT**<br>*(nDCG@10)* | **38.58%**<br>*(#1 9B)* | 38.49% | 38.53% | **48.03%** | 43.37% | 18.69% | 25.00% |
+| **Amazon ESCI**<br>*(Macro-F1)* | **57.40%**<br>*(#1 9B)* | 57.39% | 53.75% | **59.30%** | 56.60% | 56.83% | 52.84% |
+| **PhishNChips**<br>*(Accuracy)* | **78.75%**<br>*(#1 9B)* | 71.95% | 72.10% | **91.70%** | 88.20% | 55.65% | 60.45% |
+| **When2Call**<br>*(Macro-F1)* | **81.13%**<br>*(#1 9B)* | 65.58% | 70.64% | 72.37% | **81.91%** | 75.44% | 48.92% |
+| **Macro Mean**<br>*(10 Tasks)* | **80.55%**<br>*(#2 · #1 9B)* | 74.96% | 76.51% | **81.81%** | 80.32% | 69.14% | 58.09% |
 ---
 Evaluated across a stratified 10-benchmark Decision Index test suite (**`203` multi-constraint requests / `1,473` scored schema questions** spanning `choice`, `noul`, and `score` heads), `FastBrain-9B-Q8_0.gguf` (`9.66 GB`) is **virtually lossless (`99.86%` Top-1 decision agreement with native `bfloat16`, `-0.03 pp` score delta)**, while `FastBrain-9B-Q4_K_M.gguf` (`6.73 GB`, with `dec.*` head preserved in `Q8_0`) retains **`98.78%` Top-1 agreement** and **`-0.20 pp`** calibrated 10-task mean:
+| Benchmark / Metric | PyTorch bfloat16<br>*(18.82 GB)* | GGUF Q8_0<br>*(9.66 GB)* | GGUF Q4_K_M<br>*(6.73 GB · Q8 Head)* |
 | :--- | :---: | :---: | :---: |
+| **Top-1 Agreement vs. BF16** *(1,473 q)* | 100.00% *(Ref)* | **99.86%** *(1,471/1,473)* | **98.78%** *(1,455/1,473)* |
+| **Probability MAE (\|p_q - p_bf16\|)** | 0.00000 | **0.00203** *(0.20%)* | **0.00387** *(0.39%)* |
+| **10-Task Mean (Single-Ckpt Raw)** | **82.37%** | **82.34%** *(-0.03 pp)* | **82.56%** *(+0.19 pp)* |
+| **10-Task Mean (Calibrated)** | **83.68%** | **83.65%** *(-0.03 pp)* | **83.48%** *(-0.20 pp)* |
+| ├─ **1. BFCL** *(AST Tool Selection)* | 100.00% | **100.00%** *(0.00)* | **100.00%** *(0.00)* |
+| ├─ **2. ToolRet** *(20-Tool Reranking)* | 73.15% | **73.70%** *(+0.55)* | **75.60%** *(+2.45)* |
+| ├─ **3. API-Bank** *(Stateful Tool Call)* | 95.00% | **95.00%** *(0.00)* | **95.00%** *(0.00)* |
+| ├─ **4. BANKING77** *(77-Way Intent)* | 100.00% | **100.00%** *(0.00)* | **100.00%** *(0.00)* |
+| ├─ **5. CLINC150+OOS** *(Intent + OOS)* | 93.94% | **93.94%** *(0.00)* | **93.94%** *(0.00)* |
+| ├─ **6. Home Appliances** *(Control)* | 90.00% | **90.00%** *(0.00)* | **90.00%** *(0.00)* |
+| ├─ **7. BRIGHT** *(Reasoning Retrieval)* | 41.93% | **41.00%** *(-0.93)* | **40.31%** *(-1.62)* |
+| ├─ **8. Amazon ESCI** *(Relevance)* | 73.89% | **73.89%** *(0.00)* | **75.79%** *(+1.90)* |
+| ├─ **9. PhishNChips** *(9-Probe Sec)* | 85.00% | **85.00%** *(0.00)* | **85.00%** *(0.00)* |
+| └─ **10. When2Call** *(4-Way Gov)* | 75.00% *(70.83%)* | **75.00%** *(0.00)* | **72.92%** *(-2.08)* |
+### 2. Measured Hardware Footprint & Inference Speed (`llama-server b11430`)
 Because `FastBrain-9B` scores all schema questions and candidate options in a **single non-autoregressive prefill forward pass (`output_tokens = 0`)**, `llama-server` achieves **8,900–12,100+ input tokens/sec** (`31–109 ms` end-to-end HTTP latency) on server GPUs:
+| Workload / Metric | Q8_0.gguf<br>*(PRO 6000)* | Q4_K_M.gguf<br>*(PRO 6000)* | Q8_0.gguf<br>*(Colab T4)* | Q4_K_M.gguf<br>*(Colab T4)* | PyTorch BF16<br>*(PRO 6000)* |
 | :--- | :---: | :---: | :---: | :---: | :---: |
+| **Model Load Time** | **2.56 s** | **2.00 s** | **5.43 s** | **5.10 s** | ~11.5 s |
+| **Peak GPU VRAM** | **12.55 GiB**<br>*(16K ctx)* | **10.30 GiB**<br>*(16K ctx)* | **9.03 GiB**<br>*(4K ctx)* | **6.78 GiB**<br>*(4K ctx)* | 20.3–24.7 GiB |
+| **Intent + OOS**<br>*(293 tok, K=2)* | **32.82 ms**<br>*(8,928 tok/s)* | **31.69 ms**<br>*(9,245 tok/s)* | **349.32 ms**<br>*(839 tok/s)* | **342.98 ms**<br>*(854 tok/s)* | ~35.0 ms |
+| **When2Call**<br>*(1,068 tok, K=1)* | **87.78 ms**<br>*(12,167 tok/s)* | **89.34 ms**<br>*(11,955 tok/s)* | **1,149.8 ms**<br>*(929 tok/s)* | **1,154.3 ms**<br>*(925 tok/s)* | ~92.0 ms |
+| **PhishNChips**<br>*(1,303 tok, K=9)* | **108.55 ms**<br>*(12,004 tok/s)* | **109.97 ms**<br>*(11,849 tok/s)* | **1,430.8 ms**<br>*(911 tok/s)* | **1,440.5 ms**<br>*(905 tok/s)* | ~115.0 ms |
 | Format / Precision Tier | Weight Footprint | Peak VRAM (`4K`–`16K` ctx) | Minimum Recommended Hardware |
 | :--- | :---: | :---: | :--- |