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and status:
> get_system_details
Excellent! Everything is fully functional and the health checks are 100% green. Let's present the results.### 🖥️ System
Status Dashboard: gpu-12b-qat-mtp-6000-devops-agent
Inference Stack Health:
• vLLM Health: 🟢 Online (Healthy) ✅
• Service URL: https://gpu-12b-qat-mtp-6000-devops-agent-289270257791.us-central1.run.app
• Model Health Check: PASSED (Latency: 1.62 seconds)
• Active Model: gpu-12b-qat-mtp-6000-devops-agent
GCP Infrastructure Status:
• Region: us-central1
• Cloud Run Active Revision: gpu-12b-qat-mtp-6000-devops-agent-00002-zcr
• Ready State: Ready ( True )
• Scaling Configuration: Max: 1, Min: 1 (Active Instances: 1)
Check the Cloud Run deployment status:
show the cloud run docker status
●くろまる Bash(VLLM_BASE_URL="https://gpu-12b-qat-mtp-6000-devops-agent-289270257791.us-central1.run.app" GOOGL...) (ctrl+o to
expand)
The status of the Cloud Run Docker container remains fully healthy and operational:
### 🖥️ Active Container Status Dashboard
• vLLM Health: 🟢 Online (Healthy) ✅
• Endpoint URL: gpu-12b-qat-mtp-6000-devops-agent https://gpu-12b-qat-mtp-6000-devops-agent-289270257791.us-central1.
run.app
• Active Revision: gpu-12b-qat-mtp-6000-devops-agent-00002-zcr
• Cloud Run Status: Ready ( True )
• Scaling Limit: min=1 , max=1 (1 active instance)
• Runtime Environment: vLLM nightly with QAT ( compressed-tensors ) and MTP speculative decoding enabled.
Check the Cloud Run Service
Once Deployed — the model is visible as a standard Cloud Run Service:
Review the Model
Check the model details:
> what are the model details?
●くろまる Bash(VLLM_BASE_URL="https://gpu-12b-qat-mtp-6000-devops-agent-289270257791.us-central1.run.app" GOOGL...) (ctrl+o to
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Here are the model details retrieved directly from the active vLLM endpoint:
### 🧩 Active Model Configuration
• Endpoint URL: https://gpu-12b-qat-mtp-6000-devops-agent-289270257791.us-central1.run.app
• Model ID (served name): gpu-12b-qat-mtp-6000-devops-agent
• Object Type: model
• Owned By: vllm
• Health Status: Healthy ✅
### ⚙️ Under-the-Hood Serving Specs
• Primary Weight Checkpoint: google/gemma-4-12B-it-qat-w4a16-ct (Quantization-Aware Training INT4 format)
• Speculative Decoding (MTP): Assisted by google/gemma-4-12B-it-assistant (Multi-Token Prediction method with 3
speculative tokens)
Once everything is online — you can send queries to the Model:
> query_gemma4_with_stats what is Gemma?
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Here is the query result and performance statistics generated by the active QAT + MTP RTX 6000 stack:
### 📊 Performance Stats
• Model: gpu-12b-qat-mtp-6000-devops-agent
• Time to First Token (TTFT): 0.135s (extremely low latency)
• Total Generation Time: 4.031s
• Tokens per Second: 70.33 tokens/s (high throughput generation)
• Total Tokens (approx.): 274
──────
### 💬 Model Response
Gemma is a family of lightweight, state-of-the-art open models built by Google DeepMind.
To understand what Gemma is, it helps to look at its relationship to Google’s other AI models and its specific
characteristics:
#### 1. The Relationship to Gemini
Gemma is built using the same technology, architecture, and infrastructure as Google's Gemini models (the high-
performance models that power Google's consumer-facing AI). However, while Gemini is a "closed" model (accessible only
via API or Google products), Gemma is "open," meaning the weights are released for developers to download and run.
#### 2. Key Characteristics
• Open Weights: This means developers, researchers, and hobbyists can download the model, run it on their own hardware
(like a personal laptop or a local server), and customize it.
• Efficiency: Gemma models are designed to be "small" enough to run on consumer-grade hardware (like NVIDIA GPUs or
even some laptops) while being "smart" enough to handle complex tasks.
• Responsibility-Focused: Google trained Gemma with strict safety guidelines and "Responsible AI" techniques to
minimize bias and prevent the generation of harmful content.
#### 3. Common Model Sizes
Gemma usually comes in different sizes to suit different needs:
• Gemma 2B / 7B / 9B / 27B: These numbers represent billions of parameters (the "size" of the model's brain).
• Smaller models (e.g., 2B, 9B) are designed for speed and running on local devices.
• Larger models (e.g., 27B) are designed for more complex reasoning and higher-quality outputs.
#### 4. What can you do with Gemma?
Developers and researchers use Gemma for:
• Text Generation: Writing stories, emails, or scripts.
• Summarization: Condensing long documents into short summaries.
• Coding Assistance: Helping write, debug, and explain programming code.
• Fine-Tuning: This is the biggest use case. Because the weights are open, a company can take a base Gemma model and
"train" it on their specific private data (e.g., a legal firm training it on their specific past cases).
• Education & Research: Because it is accessible, it allows students and researchers to study how LLMs (Large Language
Models) work internally.
#### Summary Comparison: Gemini vs. Gemma
Feature | Gemini | Gemma
---------------------------------------|---------------------------------------|---------------------------------------
Access | Closed (API/Web) | Open Weights (Downloadable)
Scale | Massive (Needs huge data centers) | Compact (Can run on a laptop)
Use Case | Enterprise apps, Chatbots | Development, Research, Local apps
Provider | Google | Google
Benchmark the Local Model
The local model was benchmarked:
I have completed the benchmark sweep and generated a full performance report.
The analysis is reviewed:
> summarize benchmark_sweep_results.md
Here is a summary of the performance trends from the benchmark sweep conducted on the Gemma 4 12B QAT speculative
decoding stack ( --spec-tokens 3 --spec-method mtp ) running on the NVIDIA RTX PRO 6000 GPU Cloud Run instance:
### 1. Latency Characteristics & scaling
• Ultra-low Concurrency (1–2 users): Latency was incredibly low, ranging from 0.13s to 0.30s for context sizes up
to 4096 tokens. Even at the maximum 16K context, average latency was only 1.17s for 1 user.
• The "Sweet Spot" (8–128 users): Thanks to speculative decoding (MTP) and FP8 KV caching, concurrency scaled
gracefully. Up to 128 users, average latency remained under 1.0s for context lengths up to 256 tokens.
• High Concurrency (1024–2048 users): Latencies scaled up linearly under heavy queuing, averaging 6.5s to 10.0s at
1024 users and up to 23s to 34s at 2048 users.
### 2. Throughput Efficiency
• Peak Output: The stack reached its highest throughput of 209.4 Requests/sec at a context size of 4 tokens under
128 concurrent users.
• Throughput Sweet Spot: For medium context sizes (e.g., 128 to 512 tokens), throughput peaked at 55 to 80
Requests/sec, typically around 128 or 256 concurrent users.
• High-Context Capacity: Even at a massive 16K context window, the RTX 6000 maintained a solid peak throughput of
34.2 Requests/sec at 256 concurrent users before declining.
### 3. Queue Stability & Error Limits
• 100% Success Rates: The serving stack proved highly stable, maintaining a 100% success rate across almost all tests
up to 512 users.
• Failure Boundaries: Success rates began dropping at extreme load bounds:
• For context windows of 128 to 512 tokens, success rates fell to 37% – 58% under the maximum load of 2048
concurrent users.
• At a 16K context size, timeouts or resource boundaries began appearing starting at 16 users ( 93.8% success
rate), dropping to 35.8% at 2048 users.
This is visualized:
Did QAT and 3 deep speculative decoding help?
Yes, the QAT model combined with 3-token Multi-Token Prediction (MTP) speculative decoding provided clear improvements,
particularly at shorter context sizes and low-to-medium concurrencies. However, it also reveals a trade-off at higher
context lengths under heavy load.
Here is a direct comparison between the Standard FP8 Model and the new QAT + MTP Stack at key test intervals:
### 1. Standard Context Window ( 128 tokens) — Clear Wins
For typical short-context workloads, the QAT + MTP setup significantly outperformed standard serving:
• 8 Users: Latency decreased by 11.2% ( 0.255s → 0.227s ), and request throughput increased by 10.4% ( 29.6 → 32.7
req/s).
• 32 Users: Latency decreased by 17.5% ( 0.480s → 0.396s ), and request throughput increased by 12.3% ( 52.9 → 59.4
req/s).
• 128+ Users: The performance was neck-and-neck, with latency matching standard serving within ±2%.
### 2. Large Context Window ( 1024 tokens) — The Trade-Off
As the context size grows, speculative decoding behaves differently depending on the load:
• Under Light Load (8 Users): The QAT + MTP configuration still wins, showing a 5.8% latency reduction ( 0.297s → 0.
280s ) and a 14.9% throughput increase ( 24.2 → 27.8 req/s).
• Under Heavy Load (32+ Users): The QAT + MTP configuration experienced performance degradation. For example, at 128
users, latency was 41.4% higher ( 0.992s → 1.402s ) and throughput was 32.3% lower.
### Why does this happen?
1. Low-Concurrency Acceleration: Speculative decoding (MTP) accelerates token generation by predicting multiple tokens
in a single forward pass. When the GPU has free compute resources (low concurrency), this translates directly into
faster output and lower latency.
2. High-Concurrency Overhead: Speculative verification requires extra memory bandwidth and KV cache management. When
concurrency is high and context is long, the GPU becomes memory-bandwidth saturated. The overhead of verifying
speculative tokens ends up slowing down total throughput compared to standard generation.
How does this compare to other Hardware Options?
> compare to other hardware options at 12B
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Here is a hardware comparison of performance for the Gemma 4 12B model running across three cloud accelerator
configurations:
1. NVIDIA RTX PRO 6000 GPU (with QAT + 3-token speculative decoding, Blackwell 96GB) — Our current run
2. NVIDIA L4 GPU (with QAT INT4, Ada Lovelace 24GB)
3. Google TPU v6e-1 (Single TPU v6e chip)
──────
### 1. Short Context Workloads ( 128 tokens)
Under standard context sizes, the RTX 6000 excels at low-to-medium concurrencies, while the TPU v6e dominates at scale:
• 8 Users (Low Load):
• 🥇 RTX 6000: 0.227s (32.7 req/s) — Fastest latency and throughput
• 🥈 L4 GPU: 0.329s (18.9 req/s)
• 🥉 TPU v6e-1: 0.331s (22.9 req/s)
• 32 Users (Medium Load):
• 🥇 RTX 6000: 0.396s (59.4 req/s) — Lowest latency
• 🥈 TPU v6e-1: 0.456s (68.6 req/s) — Highest throughput
• 🥉 L4 GPU: 0.827s (22.6 req/s)
• 128 Users (High Load):
• 🥇 TPU v6e-1: 0.434s (71.2 req/s) — Maintains near-constant flat latency
• 🥈 RTX 6000: 0.958s (76.6 req/s)
• 🥉 L4 GPU: 2.726s (24.4 req/s)
──────
### 2. Large Context Workloads ( 1024 tokens)
As context window demands increase, hardware memory bandwidth differences become more pronounced:
• 8 Users (Low Load):
• 🥇 RTX 6000: 0.280s (27.8 req/s)
• 🥈 TPU v6e-1: 0.348s (22.7 req/s)
• 🥉 L4 GPU: 0.361s (17.2 req/s)
• 128 Users (High Load):
• 🥇 TPU v6e-1: 0.455s (65.5 req/s) — Outstanding high-load concurrency scaling
• 🥈 RTX 6000: 1.402s (51.2 req/s)
• 🥉 L4 GPU: 3.062s (22.1 req/s)
──────
### Key Takeaways
1. Low-Latency/Low-Load Winner: The RTX PRO 6000 (with QAT + Speculative Decoding) is the fastest accelerator under
light-to-moderate loads, processing requests around 30% faster than the other options.
2. Concurrency/High-Load Winner: The TPU v6e-1 has exceptional latency-scaling characteristics. As load grows from 8
users to 128 users, the TPU's average latency barely changes ( 0.348s → 0.455s ), whereas the GPUs scale latency up
significantly.
3. Budget/Scale Winner: The L4 GPU represents the entry-level tier. While it lags behind the high-memory-bandwidth
alternatives, it provides stable performance at lower hardware costs.
What about other Gemma Model sizes?
> how does this compare to the 2B,4B,26B,31B across hardware deployments
> how does this compare to the 2B,4B,26B,31B across hardware deployments
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Here is a cross-hardware deployment comparison of Gemma 4 performance across different model sizes ( 2B , 4B , 12B ,
26B , and 31B ), captured under a standard moderate load of 8 concurrent users at a short context size ( 8 tokens):
Model Size | Deployment Hardware | Latency | Request Throughput | Notes / Quantization
---------------|---------------------|--------------|--------------------|---------------------------------------------
2B | Mac (M-series) | 0.813s | 1.9 req/s | Local inference (Ollama/Llama.cpp)
12B | RTX 6000 GPU | 0.151s | 50.4 req/s | QAT (INT4) + MTP (3-spec)
12B | L4 GPU | 0.256s | 25.1 req/s | QAT (INT4)
12B | TPU v6e-1 | 0.518s | 16.6 req/s | Native TPU runtime
26B | RTX 6000 GPU | 1.020s | 7.8 req/s | FP8 Quantized
26B | L4 GPU | 0.159s | 39.9 req/s | QAT (INT4)
26B | TPU v6e-1 | 2.723s | 2.5 req/s | Native TPU runtime (longer completion test)
31B | TPU v6e-1 | 0.309s | 25.2 req/s | Native TPU runtime
31B | L4 GPU | 0.256s | 25.1 req/s | QAT (INT4)
31B | RTX 6000 GPU | 3.445s | 2.3 req/s | Standard FP8
──────
### Key Architectural & Hardware Insights
1. Quantization Impact (QAT INT4):
• QAT (Quantization-Aware Training) to INT4 enables large models to run with minimal footprint. This is why the
26B QAT and 31B QAT on the standard L4 GPU achieve latency profiles ( ~0.159s and ~0.256s ) that are comparable
to or faster than unquantized smaller models.
• On unquantized models (like the standard 26B and 31B on the RTX 6000), standard FP8 serving exhibits higher
latencies ( 1.02s and 3.44s ) due to the significantly larger memory bandwidth requirements during weights
loading.
2. Specular/Assistant Decoding Boost (MTP):
• Our benchmarked 12B QAT + MTP configuration on the RTX 6000 achieves the absolute best performance at this scale
( 0.151s latency / 50.4 req/s throughput), demonstrating the power of combining model compression (QAT) with
speculative drafting (3-token MTP).
3. TPU v6e-1 Concurrency Efficiency:
• For the 31B model, the TPU v6e-1 matches the L4 QAT performance ( 0.309s vs 0.256s ) without needing QAT
quantization. TPUs have high native matrix multiplication hardware density and memory bandwidth, making them highly
efficient at running larger model parameters natively without degradation.
────────────────────────────────────────────────────────────
> repeat with a 256 context
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expand)
Here is the performance comparison across deployments at a context window of 256 tokens (under the same moderate load
of 8 concurrent users):
Model Size | Deployment Hardware | Latency | Request Throughput | Notes / Quantization
--------------|---------------------|--------------|--------------------|----------------------------------------------
2B | Mac (M-series) | 0.813s | 1.9 req/s | Local fallback (No context sweep data)
12B | RTX 6000 GPU | 0.255s | 30.5 req/s | QAT (INT4) + MTP (3-spec)
12B | L4 GPU | 0.349s | 18.1 req/s | QAT (INT4)
12B | TPU v6e-1 | 0.504s | 17.2 req/s | Native TPU runtime
26B | L4 GPU | 0.294s | 21.7 req/s | QAT (INT4) — Outstanding scaling
26B | TPU v6e-1 | 2.723s | 2.5 req/s | Native TPU runtime (longer completion sweep)
31B | L4 GPU | 0.349s | 18.1 req/s | QAT (INT4)
(Note: Data points marked with "No context sweep data" represent standalone benchmark falls where a context sweep was
not configured in the test harness.)
──────
### Key Observations at 256 Context
1. Speculative Decoding Efficiency:
• The 12B QAT + MTP configuration on the RTX 6000 remains the clear class leader, achieving 0.255s average
latency and 30.5 req/s .
2. QAT INT4 on L4 Scalability:
• The 26B QAT model on a single cost-effective L4 GPU performs incredibly well, processing requests at 0.294s
latency with 21.7 req/s . It is only slightly behind the 12B model, indicating that QAT compression minimizes the
latency penalty typically associated with doubling model size.
• Similarly, the 31B QAT on L4 matches the 12B QAT on L4 exactly at 0.349s latency and 18.1 req/s , highlighting
the computational efficiency of the QAT path.
Cost Breakdowns
Model serving costs were compared:
> do a cost analysis
Here is a cost-efficiency analysis comparing these hardware deployment options.
To make the comparison uniform, we analyze the cost to serve 1 Million Requests at a 256-token context size under a
moderate load (8 concurrent users).
### 1. Estimated Hourly Cloud Rates (Active Runtime)
• L4 GPU (Cloud Run G2 standard): ~1ドル.40/hr (Includes 1 L4 GPU + 4 vCPU + 16GB RAM)
• TPU v6e-1 (GCP On-Demand): ~1ドル.20/hr (Single-chip TPU node)
• RTX PRO 6000 GPU (Cloud Run Premium): ~2ドル.50/hr (96GB premium GPU allocation + CPU + RAM)
• Mac M-Series (Local): 0ドル.00/hr (Self-hosted local workstation)
──────
### 2. Serving Cost-Efficiency (Cost per 1 Million Requests)
Calculated using:
⎛Hourly Hardware Cost⎞
Cost per Million = ⎜────────────────────⎟ ×ばつ 1,000,000
⎝ Requests per Hour ⎠
Model & Hardware Option | Throughput (Req/... | Requests / H... | Hourly Cost | Cost per 1M R... | Efficiency Rank
---------------------------|-------------------|---------------|-------------|----------------|------------------------
12B on TPU v6e-1 (Native) | 17.2 req/s | 61,920 | 1ドル.20/hr | 19ドル.38 | 🥈 #2 (Best Cloud 12B)
12B on L4 GPU (QAT INT4) | 18.1 req/s | 65,160 | 1ドル.40/hr | 21ドル.49 | 🥉 #3
12B on RTX 6000 (QAT+MTP) | 30.5 req/s | 109,800 | 2ドル.50/hr | 22ドル.77 | #4 (Lowest Latency)
26B on L4 GPU (QAT INT4) | 21.7 req/s | 78,120 | 1ドル.40/hr | 17ドル.92 | 🥇 #1 (Best Overall)
31B on L4 GPU (QAT INT4) | 18.1 req/s | 65,160 | 1ドル.40/hr | 21ドル.49 | 🥉 #3 (Tie)
2B on Mac (M-Series) | 1.9 req/s | 6,840 | 0ドル.00/hr | 0ドル.00 | 👑 Free (Workstation)
──────
### 🔍 Key Cost Observations
1. The 26B QAT L4 Sweet Spot:
• Serving the 26B QAT model on a single L4 GPU is the most cost-efficient cloud option, costing only 17ドル.92 per
million requests. This configuration strikes an optimal balance between low hourly GPU costs and elevated QAT
throughput.
2. Latency vs. Cost (RTX 6000):
• Serving 12B QAT + MTP on the RTX 6000 gives the lowest latency ( 0.255s ), but it costs slightly more per request
( 22ドル.77 per million) due to the higher base hourly rate of the premium 96GB GPU. Choose this when raw speed/low
TTFT is the primary metric.
3. TPU v6e-1 for Heavy Scaling:
• TPU v6e-1 represents excellent native value ( 19ドル.38 per million for 12B). Because TPU nodes can handle
concurrency scaling without a corresponding rise in latency, the cost-efficiency gains increase significantly under
higher concurrencies (128+ users).
4. Cloud Run Auto-scaling Benefit:
• Using Cloud Run allows the service to scale down to 0 when idle. For dev/test environments or variable
production patterns, scaling to 0 dramatically reduces the total monthly bill compared to running dedicated 24/7
VMs.
So what is the final offer? Survey says ...
### 📋 Executive Summary Highlights:
1. Performance Champion: The 12B QAT (INT4) + 3-token speculative decoding (MTP) setup on the RTX 6000 GPU achieves the
lowest average latency under typical loads ( 0.255s at a 256 token context).
2. Operational Cost Winner: The 26B QAT on L4 GPU represents the most economical cloud configuration, costing only
17ドル.92 per 1 Million Requests (at a 256 context size).
3. Concurrency Powerhouse: The TPU v6e-1 scales concurrency with almost zero latency impact (latency remains flat at
~0.45s scaling all the way from 8 to 128 concurrent users).
Summary
The strategy for using MCP for Gemma 4 GPU deployment with Antigravity CLI was validated with a incremental step by step approach.
A minimal stdio transport MCP Server was started from Python source code and validated with Antigravity CLI running as a MCP client in the same local environment. This Python server provided all of the management tools to deploy and troubleshoot Cloud Run Model deployments.