This article provides a step by step guide for debugging a Gemma 4 model to a Google Cloud Run hosted GPU enabled system. The 31B dense model was unsuccessfully deployed to the L4 GPU and a deep dive into why this model won’t fit on systems with limited GPU resources.
What is this project trying to Do?
This project is a DevOps/SRE assistant that uses a Gemma 4 model hosted on Cloud Run with GPU. It provides tools to provision the Docker container and deploy the model, as well as for observability and performance testing.
This project is similar to a previous project that targeted GPU hosted Gemma4 instances on GCP:
Gemma-SRE: Self-Hosted vLLM Infrastructure Agent
Full Installation Instructions
The detailed installation instructions for Antigravity CLI are here:
Getting Started with Antigravity CLI
Where do I start?
The strategy for starting MCP development for model management is a incremental step by step approach.
First, the basic development environment is setup with the required system variables, and a working Antigravity CLI configuration.
Then, a minimal Python MCP Server is built with stdio transport. This server is validated with Antigravity CLI in the local environment.
This setup validates the connection from Antigravity CLI to the local server via MCP. The MCP client (Antigravity CLI) and the Python MCP server both run in the same local environment.
Setup the Basic Environment
At this point you should have a working Python environment and a working Antigravity CLI installation. The next step is to clone the GitHub samples repository with support scripts:
cd ~
git clone https://github.com/xbill9/gemma4-tips
Then run init.sh from the cloned directory.
The script will attempt to determine your shell environment and set the correct variables:
gpu-31B-qat-L4-devops-agent
source init.sh
If your session times out or you need to re-authenticate- you can run the set_env.sh script to reset your environment variables:
gpu-31B-qat-L4-devops-agent
source set_env.sh
Variables like PROJECT_ID need to be setup for use in the various build scripts- so the set_env script can be used to reset the environment if you time-out.
Antigravity CLI mcp_config.json
A sample MCP server file is provided in the .agents directory:
{
"mcpServers": {
"gpu-devops-agent": {
"command": "python3",
"args": [
"/home/xbill/gemma4-tips/gpu-31B-qat-L4-devops-agent/server.py"
],
"env": {
"GOOGLE_CLOUD_PROJECT": "aisprint-491218",
"GOOGLE_CLOUD_LOCATION": "us-east4",
"VLLM_BASE_URL": "https://gpu-31b-qat-l4-devops-agent-289270257791.us-east4.run.app",
"MODEL_NAME": "/mnt/models/gemma-4-31B-it-qat-w4a16-ct"
}
}
}
}
Validation with Antigravity CLI
The final connection test uses Antigravity CLI as a MCP client with the Python code providing the MCP server:
MCP Servers
Plugins (~/.gemini/antigravity-cli/plugins)
> ✓ google-dev-knowledge Tools: search_documents, answer_query, get_documents
✓ gpu-devops-agent Tools: save_hf_token, get_vllm_endpoint, list_vertex_models, list_bucket_models,
analyze_cloud_logging, +21 more
Getting Started with Gemma 4 on GPU
The Official vLLM repo also has Gemma4 specific information:
The Gemma 31B model is a dense model:
google/gemma-4-31B · Hugging Face
NVIDIA L4 on Cloud Run
The L4 on Google Cloud Run provides a cost-effective “scale to zero” option for providing inference:
Quantization-Aware Training (QAT)
One of the key issues with deploying models is available GPU memory based in the hardware footprint. Along with the new 12B model release- new versions of the model that use QAT to reduce the memory requirements have been released:
Gemma 4 QAT models: Optimizing model compression for mobile and laptop efficiency
Attempt to Deploy The Standard 31B Model on L4
Antigravity and the MCP server tools are used to analyze the deployment options:
> will a standard Gemma4 26AB fit on the L4 cloud run GPU with 24 GB memory?
No, a standard (unquantized bfloat16 ) Gemma 4 26B model will not fit natively in the VRAM of a single 24 GB
NVIDIA L4 GPU.
Here is why:
### 1. The Weight Math ( bfloat16 / 16-bit)
• Parameters: 26 Billion
• Precision: bfloat16 (requires 2 bytes per parameter)
• Weights-only Size: 26 B × 2 bytes ≈ 𝟓𝟐 GB of memory.
• GPU Limit: The NVIDIA L4 has only 24 GB of VRAM.
Since the unquantized weights alone (52 GB) are more than double the total VRAM of the L4 GPU (24 GB), the model
cannot be loaded into the GPU's memory.
──────
### 2. How to make 26B run on a single L4 GPU
To run a 26B model on a single L4, you have two options:
1. Use the QAT (Quantization-Aware Training) 4-bit version ( w4a16-ct ):
• Compressed to 4-bit, the weight footprint drops to ~13 GB, which easily fits on the 24 GB L4 GPU, leaving
~8-11 GB of VRAM for the KV cache.
2. CPU Offloading:
• You can force the unquantized bfloat16 model to load by offloading more than 30 GB of weights to the host
CPU memory. However, this is not recommended for production because swapping weights between the CPU RAM and
GPU VRAM over the PCIe bus will make inference extremely slow (often <1 token/sec).
What!?! The unquantized version of the model is larger than you would expect. The L4 Cloud Run GPU only has 24GB! Can this be done?
What about trying CPU offloading?
There has to be more than one way to skin a cat!
> why is CPU offloading not recommended?
▸ Thought Process
Analyzing CPU Offloading
CPU offloading is not recommended for production environments because it introduces a severe hardware bottleneck
that destroys inference performance.
Here is a breakdown of the technical reasons:
### 1. The PCIe Bandwidth Bottleneck (The Primary Culprit)
When you run model layers, the GPU needs to read the model weights for every single token generated.
• GPU VRAM Bandwidth: The NVIDIA L4 has an internal memory bandwidth of ~300 GB/s.
• PCIe Gen 4 Bandwidth (CPU ↔ GPU): The link between the host CPU RAM and the GPU maxes out at ~31.5 GB/s (and is
often lower in virtualized cloud environments).
Transferring weights from host CPU memory to GPU memory is 10x slower than reading them directly from VRAM. The
GPU spends most of its time idle, waiting for the next set of weights to arrive over the PCIe bus (known as a
"compute bubble").
──────
### 2. Severe Latency Degradation
Because of the PCIe bottleneck, token generation speed drops off a cliff:
• Native VRAM Serving: Typically generates 30 to 60+ tokens per second.
• CPU Offloaded Serving: Typically drops to 1 to 5 tokens per second (or even lower for larger context windows),
which feels sluggish and unusable for interactive chat or real-time SRE diagnostics.
──────
### 3. Increased Cloud Cost & CPU Overhead
To offload weights to CPU RAM, you have to request much larger host machine sizes:
• Extra Host RAM: You must provision at least 32 GB or 64 GB of system RAM just to hold the offloaded weights.
• CPU Resource Contention: The CPU must actively manage the high-speed paging/swapping of layers to the GPU,
increasing CPU overhead and potentially starving other processes (like web servers or logging agents) running on
the same container.
• Higher Cloud Bill: On Cloud Run, paying for large CPU and memory allocations (e.g., 8 vCPUs and 32 GB RAM)
around the clock negates the cost advantage of using a single, efficient GPU.
──────
### Summary
CPU offloading is a great development/debugging tool because it lets you test large models on cheap, low-VRAM
GPUs without triggering Out-Of-Memory (OOM) errors. However, for a production API, it is always better to
quantize the model further (e.g., using INT4/QAT) or scale up to a larger GPU (like an A100 or H100) to keep the
entire model footprint natively in VRAM.
QAT Model Versions
This leaves the new QAT encoded models as the best option:
> summary QAT 31B model deployment options on NVIDIA L4 Cloud Run
▸ Thought Process
Considering Deployment Options
Deploying the Gemma 4 31B QAT model ( google/gemma-4-31B-it-qat-w4a16-ct ) on a single NVIDIA L4 GPU on Cloud Run
(Gen2) requires balancing the 24 GB VRAM limit and Cloud Run's container execution constraints.
There are two primary deployment configurations on Cloud Run:
──────
### 1. The Concurrency-First Configuration (Current Setup)
Designed to handle multi-user API load without running out of memory.
• vLLM Arguments:
--cpu-offload-gb 14
--max-model-len 8192
--kv-cache-dtype fp8
--gpu-memory-utilization 0.90
• Cloud Run Specs: 8 vCPUs, 32 GiB RAM, 1 NVIDIA L4 GPU.
• Storage Strategy: GCS FUSE Volume Mount (with caching flags: max-conns-per-host=100;stat-cache-ttl=3600s;type-
cache-ttl=3600s ) for model weights.
• Performance:
• Concurrency: Up to 512 concurrent users with 100% request success rate.
• Latency: Moderate (swapping weights over the PCIe bus due to CPU offloading creates a speed bottleneck).
• Cold Start Time: ~6.5 minutes (400 seconds) to mount and load the weights into memory.
──────
### 2. The Speed-First Configuration (In-VRAM Serving)
Designed to maximize token generation speeds for a single user or a small team.
• vLLM Arguments:
--max-model-len 4096
--kv-cache-dtype fp8
--gpu-memory-utilization 0.95
# Omit --cpu-offload-gb completely (0 offload)
• Cloud Run Specs: 4 or 8 vCPUs, 16 or 32 GiB RAM, 1 NVIDIA L4 GPU.
• Storage Strategy: GCS FUSE Volume Mount.
• Performance:
• Concurrency: Low (supports ≤32 concurrent users before VRAM memory limits queue or fail requests).
• Latency: Very Fast (the model weights are entirely in GPU VRAM, eliminating CPU-GPU transfer bottlenecks).
• Cold Start Time: ~5.5 minutes to load weights into VRAM.
──────
### ⚠️ Cloud Run Platform Requirements & Gotchas
Whichever configuration you choose, Cloud Run requires the following settings to deploy successfully:
• Startup Probes: Because loading a 31B parameter model takes several minutes, standard startup timeouts will
crash the container. You must extend the startup probe:
• initialDelaySeconds=240 (maximum Cloud Run allows)
• periodSeconds=60 , failureThreshold=30 , timeoutSeconds=50 (gives the container up to 34 minutes to
start up).
• FUSE Mount Permissions: The official vllm-openai container runs as UID/GID 1001 ( vllm ). You must mount
your GCS bucket with the options uid=1001;gid=1001 or the container will crash with a Permission Denied error.
• No CPU Throttling: You must deploy with --no-cpu-throttling so that CPU resources remain allocated to
coordinate with the GPU during periods of inactivity.
What about the KV Cache? Can I Quant that to 4 bit?
At first pass — it seems like the best way to optimize GPU memory is to quant the KV Cache to 4 bits- but that has side-effects:
> what is the quant for the kv cache
The KV cache is quantized to FP8 (8-bit floating point), enabled via the --kv-cache-dtype=fp8 startup flag.
### Key Benefits of FP8 KV Cache:
1. 50% VRAM Savings: Quantizing the KV cache from standard 16-bit ( bfloat16 ) to 8-bit ( fp8 ) cuts the memory
required to store context history per token in half.
2. Higher Concurrency: By freeing up significant VRAM space, it allows the model to handle much higher
concurrency levels (up to 512 concurrent users on this setup) within the same 24 GB hardware limit.
3. Negligible Quality Loss: Standard FP8 KV cache quantization maintains near-identical accuracy compared to
unquantized bfloat16 caches.
────────────────────────────────────────────────────────────
> can the NVIDIA L4 use 4 bit quant for kv
▸ Thought for 2s, 325 tokens
Considering Hardware Support
Technically yes, but it is not recommended for the NVIDIA L4 in production due to hardware limitations and
performance trade-offs.
Here is the breakdown of why:
### 1. Lack of Native Hardware Support
The NVIDIA L4 (Ada Lovelace architecture) has native hardware Tensor Core support for FP8 and INT8 data formats.
It does not have native hardware acceleration for 4-bit calculations (like INT4 or FP4) inside the attention
layers. To use a 4-bit KV cache, the GPU must dequantize the values back to FP16 or FP8 on-the-fly during
attention computation, adding significant arithmetic overhead.
### 2. The "De-acceleration" Effect
Because of the on-the-fly dequantization overhead, running a 4-bit KV cache on the L4 can actually reduce your
token generation throughput (tokens/sec) compared to FP8, even though it saves more VRAM.
### 3. Serious Quality Degradation
While FP8 KV cache quantization is virtually lossless compared to bfloat16 , 4-bit KV cache quantization results
in noticeable loss of model quality. The model is much more likely to lose track of long-context reasoning,
generate repetitive text, or fail to follow complex prompt instructions.
### Summary
For the NVIDIA L4, FP8 is the optimal sweet spot—providing the maximum VRAM savings possible without sacrificing
speed or model accuracy.
So for the L4 hardware footprint — an 8 bit KV Cache is the best trade off between memory usage and performance.
This deployment looks feasible:
> what is the memory breakdown on the GPU
▸ Thought for 2s, 450 tokens
Analyzing GPU Memory Usage
I will query the logs to retrieve the exact memory profiling breakdown reported by vLLM on startup.
● Bash(gcloud logging read "resource.type=cloud_run_revision AND resource.labels.service_name=gpu-31b-qat-l4-devops-
...)
● gpu-devops-agent/status_vllm(vLLM Status)
● gpu-devops-agent/analyze_gpu_logs(GPU Logs Analysis)
● ListDir(/home/xbill/gemma4-tips/gpu-31B-qat-L4-devops-agent)
● Read(/home/xbill/gemma4-tips/gpu-31B-qat-L4-devops-agent/benchmark_report_summary.md)
● gpu-devops-agent/get_vllm_gpu_deployment_config(GPU deployment configuration)
● Search(Search server.py)
● Read(/home/xbill/gemma4-tips/gpu-31B-qat-L4-devops-agent/server.py) (ctrl+o to expand)
▸ Thought for 3s, 641 tokens
Analyzing VRAM Allocation
On the NVIDIA L4 GPU (24 GB physical VRAM, ~22.96 GiB usable), the memory breakdown for the Gemma 4 31B QAT model
deployment ( google/gemma-4-31B-it-qat-w4a16-ct ) is as follows:
### 📊 GPU Memory Allocation Breakdown
Memory Segment | Size (GB) | Percentage of Total | Purpose / Configuration Details
----------------------|-----------|---------------------|--------------------------------------------------------
Total Available VRAM | 24.00 GB | 100% | Physical VRAM limit on a single NVIDIA L4.
● Read(/home/xbill/gemma4-tips/gpu-31B-qat-L4-devops-agent/model_comparison.md)
vLLM Reserved Budget | 21.60 GB | 90% | Controlled by --gpu-memory-utilization=0.90 to
| | | allocate a dedicated pool for vLLM.
GPU Model Weights | ~5.80 GB | ~24% | The remaining weight footprint on the GPU. The rest
| | | (~13.6 GB of the ~19.5 GB total model footprint) is
| | | offloaded to the CPU host RAM (from the allocated
| | | 32Gi Cloud Run memory) using --cpu-offload-gb=14 .
KV Cache Pool (FP8) | ~15.80 GB | ~66% | Memory allocated for request context. By configuring
| | | --kv-cache-dtype=fp8 , the memory required per token
| | | is cut by 50%, allowing for a ~64x boost in concurrent
| | | capacity (up to 512 concurrent users).
CUDA & OS Overhead | 2.40 GB | 10% | Unallocated headroom reserved for CUDA context
| | | initialization, workspace memory, runtime PyTorch
| | | kernels, and operating system overhead to prevent out-
| | | of-memory (OOM) crashes.
### 💡 Key SRE & DevOps Takeaways
• Standard 31B vs QAT 31B: Without quantization and offloading, the standard 31B model in bfloat16 requires
over 60 GB of VRAM for weights alone, which fails to load on a single L4 GPU.
• CPU Offloading: Setting --cpu-offload-gb=14 is what makes this model run successfully on a single L4. It
splits the weight storage between the host RAM and GPU VRAM, keeping the VRAM active weight footprint low enough
• FP8 KV Cache: Using FP8 quantization for the KV cache maximizes context capacity, enabling the model to support
high concurrency (up to 512 concurrent users at 100% request success rate).
to leave significant room for the KV cache.
────────────────────────────────────────────────────────────
> what is the context size
The configured context size for this deployment is 8,192 tokens, defined by the --max-model-len=8192 argument
in the vLLM container startup settings.
### Key Details:
• Configured Limit: 8,192 tokens (caps the maximum context window per request to prevent VRAM depletion).
• Quantization Efficiency: Since --kv-cache-dtype=fp8 is enabled, the memory footprint of this 8K context is
halved compared to native 16-bit precision, leaving plenty of VRAM to maintain high concurrency.
────────────────────────────────────────────────────────────
> are there any better memory layouts? or is this the best?
▸ Thought Process
Considering Memory Layouts
This layout is highly optimized for high concurrency under strict hardware limitations, but whether it is the
"best" depends on your primary performance goal (e.g., maximizing request throughput vs. minimizing single-user
latency).
Here is an analysis of the current layout and alternative, potentially better memory layouts depending on your
needs.
──────
### 1. The Current Layout (The Concurrency-First Layout)
• Configuration: --cpu-offload-gb=14 , --max-model-len=8192 , --kv-cache-dtype=fp8 , --gpu-memory-
utilization=0.90
• How it works: By offloading 14 GB of model weights to the host CPU RAM, only ~5.5 GB of weights reside on
the GPU at any one time. This leaves a massive ~15.8 GB of VRAM open for the KV Cache.
• Pros: Supports up to 512 concurrent users with a 100% success rate.
• Cons: Single-user latency (time-to-first-token and inter-token latency) is slower because active weights must
be continuously transferred from host CPU RAM to GPU VRAM during the forward pass.
──────
### 2. Alternative Layout A: The Low-Latency/High-Speed Layout (Recommended for low concurrency)
If you only need to support a small number of concurrent requests (e.g., <16 or 32 users) but want much faster
token generation speed, you should run the model entirely on the GPU.
• Configuration: --cpu-offload-gb=0 (or remove the flag), --max-model-len=4096 , --kv-cache-dtype=fp8 , --
gpu-memory-utilization=0.95
• How it works: All ~13 GB of the 31B QAT weights are loaded into VRAM. With a tighter max context length of
4096 , the KV cache size is restricted, fitting the remaining ~9 GB of VRAM.
• Pros: Maximum inference speed. No CPU-to-GPU transfer bottlenecks during inference.
• Cons: Concurrency limits will drop (above 32-64 concurrent users, you may encounter OOM errors or requests
being queued).
──────
### 3. Alternative Layout B: The 12B QAT "Sweet Spot" Layout
If you want the best of both worlds (zero offloading latency and high concurrency) on a single L4 GPU, moving
down to the 12B QAT model is often a better production choice.
• Configuration: Use google/gemma-4-12B-it-qat-w4a16-ct , --cpu-offload-gb=0 , --max-model-len=8192
• How it works: The 12B QAT model weights are only ~6 GB . The entire model fits on the L4 GPU with zero
offloading, leaving ~16 GB of VRAM for the KV cache.
• Pros: Extremely fast token generation, supports high concurrency, and retains high reasoning accuracy.
──────
### Summary Recommendation
• Use the current layout if you must serve the 31B model to hundreds of users simultaneously.
• Switch to Alternative Layout A (eliminate offloading, reduce context to 4K) if you want the fastest response
time for a single user/small team.
So the trade-off becomes context size vs concurrency to optimize the limited GPU memory.
Did that Voodoo Work?
The Cloud Run Deployment was re-started:
> is the startup time enough for the health check timeout on cloud run
▸ Thought for 2s, 479 tokens
Analyzing Startup Time
Yes, the startup probe configuration is more than sufficient for the model load time:
### Startup Probe Configuration breakdown:
• initialDelaySeconds=240 : Cloud Run will wait 4 minutes (240 seconds) before sending the first health check
request.
• periodSeconds=60 : After the initial delay, it checks health every 60 seconds.
• failureThreshold=30 : It allows up to 30 failed health checks before declaring the deployment dead.
• timeoutSeconds=50 : Each individual health check request is allowed 50 seconds to respond.
### Total Startup Budget:
Total Timeout Budget = 240 s + (30 × 60 s) = 2040 seconds (≈34 minutes)
Typically, mounting the model weights via GCS FUSE and loading the ~13 GB Gemma 4 31B QAT weights into GPU memory
takes between 3 to 8 minutes on a cold start. Therefore, the 34-minute total budget provides a very safe margin
of error.
Wuhoo. We have deployment!
Did that tweak work?
The model deployment was re-checked:
Based on the system logs and vLLM configuration of the active Cloud Run instance (Revision gpu-31b-qat-l4-devops-
agent-00010-9jt ), here is the summary of memory allocation:
### 📟 GPU VRAM Allocation (NVIDIA L4 — 24 GB Total VRAM)
• GPU Memory Pool Limit ( --gpu-memory-utilization ): 90% of VRAM (~21.6 GB) is allocated to the vLLM process.
• Weights Compression: Utilizing Quantization-Aware Training (QAT) 4-bit weights ( w4a16-ct format) allows the
31B model to fit comfortably in VRAM. (Without QAT, a standard 31B bfloat16 model requires ~62 GB of VRAM,
which is impossible to host on a single L4).
• KV Cache Configuration:
• Precision: FP8 ( --kv-cache-dtype fp8 ), cutting KV cache token footprint in half.
• Capacity: 48,161 tokens allocated for KV cache.
• CUDA Graph Memory Profiling: PIECEWISE = 5 (largest context length 16), FULL = 4 (largest context length 8).
──────
### 🖥️ System RAM Allocation (Cloud Run Container — 32 GB Total RAM)
• Provisioned Memory: 32 GiB allocated to the container.
• CPU Offloading ( --cpu-offload-gb ): 14.0 GB of system RAM is designated for CPU offloading of weight
computations, reducing active VRAM usage.
• Loading Strategy: lazy safetensors loading strategy to minimize memory spikes on startup.
Uh-Oh. The model seems sluggish and that CPU offloading doesn’t look like it is helping!
Time for the smaller context but no offloading option:
The query appears to hang (or proceed extremely slowly) due to the current vLLM CPU Offloading configuration on
the Cloud Run deployment:
### 🔍 Diagnostic Details
1. Low Generation Throughput: The server logs show the generation throughput is currently running at only 0.8 to
1.6 tokens/sec:
Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 1.6 tokens/s, Running: 2 reqs
2. The Cause: The service is running with --cpu-offload-gb=14 (the default layout). Because some layers/weights
of the model are offloaded to system memory (CPU), vLLM must swap data between system RAM and GPU VRAM constantly
during inference, resulting in extremely slow token generation that feels like a hang.
──────
### ⚡ How to Fix (Deploy in Low-Latency Mode)
You can redeploy the service in Low-Latency/High-Speed Layout. This disables CPU offloading ( --cpu-offload-gb=0
), runs the entire model on the GPU, and limits the context length to 4096 to ensure it fits:
You can invoke the deploy_vllm tool with the low_latency flag set to True :
• MCP Tool: deploy_vllm(low_latency=True)
This will update the service parameters to:
• --cpu-offload-gb=0 (Run entirely on GPU)
• --max-model-len=4096
• --gpu-memory-utilization=0.95
But Will it Blend?
Now the deployment status can be reviewed again:
When we updated the default deployment to run entirely on the GPU ( --cpu-offload-gb=0 ), the container crashed
during initialization with the following error:
│ torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 2.62 GiB. GPU 0 has a total capacity of 21.96
GiB
│ of which 2.15 GiB is free. Process 1148 has 19.81 GiB memory in use.
#### Why did this happen?
1. Pre-Allocation Limit ( --gpu-memory-utilization=0.95 ): By default in low-latency mode, we set the GPU memory
utilization to 0.95 (95%). This instructs vLLM to reserve 95% of the total L4 VRAM (~20.86 GB) for its model
weights and KV cache pool.
2. PyTorch Compilation Overhead: This leaves only 5% of VRAM (~1.1 GB) unallocated. During startup, vLLM runs a
warmup step to profile memory and capture CUDA graphs for accelerated execution.
3. The Crash: During this warmup compilation phase, PyTorch tried to allocate an intermediate activation tensor
of 2.62 GiB, but only 2.15 GiB of free VRAM was available, triggering the CUDA OOM crash.
──────
### 🛠️ The Resolution
We modified the low-latency parameter defaults in server.py to reduce --gpu-memory-utilization from 0.95
to 0.85 (85%):
• More Headroom: This reserves 15% of the VRAM (~3.3 GB) for PyTorch workspace, activation tensors, and CUDA
graph warming.
• No OOM: The 3.3 GB headroom easily accommodates the 2.62 GiB allocation request.
• High Token Capacity: At 85% utilization, the KV cache pool still has room for ~47,000 tokens (plenty for high
concurrency).
The redeployment is currently running to apply this fix. I will notify you as soon as it completes.
Looks like it cut it too close! GPU memory is extremely tight. The deployment is restarted. Still no dice! This model does not want to run!
I have updated the low-latency configuration to use no CPU offloading ( --cpu-offload-gb=0 ) while incorporating
eager execution mode ( --enforce-eager ) to bypass CUDA graph memory overhead.
### ⚡ Updates Applied
1. Configured GPU-Only Eager Mode:
• Changed --cpu-offload-gb=0 back to ensure 100% execution on GPU.
• Added the --enforce-eager flag to disable CUDA graph profiling/capture. This frees up the ~2.6 GB VRAM
workspace that PyTorch otherwise reserves during compilation startup, allowing the entire model weights and
KV cache to fit on the L4 VRAM without offloading.
• Restored --gpu-memory-utilization=0.95 to maximize token capacity.
2. Synchronized Code & Configs: Applied these configuration parameters across server.py, the Makefile,
and the unit tests in test_agent.py.
3. Triggered Redeployment: Initiated the deployment task in the background.
I will monitor the progress of the revision creation and let you know when the service is online.
────────────────────────────────────────────────────────────
> what do these changes do
Here is a breakdown of what the latest changes do and how they solve both the latency and memory constraints on
the NVIDIA L4 GPU (24 GB VRAM):
### 1. --cpu-offload-gb=0 (Run 100% on GPU)
• What it does: Bypasses offloading any layer weights to system RAM.
• Why it matters: Previously, offloading 14 GB of the model weights to the CPU forced the engine to copy
computations back and forth over the PCIe bus during every forward pass. Eliminating offloading increases
generation speeds dramatically (from a slow ~1.6 tokens/sec to native hardware speeds of 15–30+ tokens/sec).
### 2. --enforce-eager (Bypass CUDA Graphs)
• What it does: Forces vLLM to run in eager execution mode, disabling CUDA graph capturing at startup.
• Why it matters: By default, vLLM traces and compiles CUDA graphs to optimize execution latency. However, this
tracing/profiling process requires allocating a large temporary memory workspace of ~2.6 GB VRAM at startup.
Because a 31B model already consumes almost the entire 24 GB VRAM, that 2.6 GB workspace triggered the CUDA Out-
of-Memory (OOM) crash. Disabling CUDA graphs saves this memory overhead, allowing the weights to load safely.
### 3. --gpu-memory-utilization=0.95 (Maximize Cache Space)
• What it does: Raises the VRAM budget back to 95% of total GPU memory.
• Why it matters: Now that we have bypassed the 2.6 GB CUDA graph tracing overhead, we can safely expand vLLM's
active memory pool to 95%. This maximizes the space available for the FP8 KV cache, allowing the server to handle
high numbers of concurrent users without running out of context memory.
One more deployment:
The CUDA Out of Memory (OOM) error occurs during the model weight loading/initialization phase, which happens
before the engine reserves any VRAM for the context window (KV Cache):
1. Static Weights Size: The Gemma 4 31B model's static weights are ~15.5 GiB (quantized to 4-bit).
2. Vocabulary & Output Layer Embeddings: The model has a very large vocabulary embedding and Language Model (LM)
head, which together account for another ~4.7 GiB.
3. Framework/CUDA Runtime Overhead: PyTorch, CUDA context variables, and internal drivers require ~1.5 to 2.0 GiB
of overhead.
Summing these up:
Total Base VRAM = 15.5 GiB (Weights) + 4.7 GiB (Embeddings) + 2.0 GiB (Overhead) ≈ 22.2 GiB
Because a single NVIDIA L4 GPU has exactly 24 GB (22.35 GiB usable) of VRAM, loading 100% of these parameters
onto the GPU leaves almost no space left for PyTorch's execution context, causing the engine to crash during
startup with a torch.OutOfMemoryError before it even has the chance to configure a context window of any size.
### Why the 5 GB CPU offload compromise works
By setting --cpu-offload-gb=5 , vLLM's UVAOffloader moves only the vocabulary embeddings and LM head (~4.7
GiB) into host CPU RAM, leaving 100% of the transformer layers (the actual attention/computation layers) running
on the GPU.
• No Compute Bottleneck: Because vocabulary lookup only occurs once at the start of a prompt and once when
decoding a token, offloading it has a negligible effect on latency.
• Full GPU Core Utilization: The actual heavy-lifting attention layers are entirely preserved in VRAM, allowing
the model to generate tokens at full speed.
How about this approach?
▸ Thought for 1s, 2.4k tokens
Analyzing Server Configurations
The generation speed is slow (~2 tokens/sec) because we are forced to offload the model's vocabulary embeddings
and language model (LM) head to CPU host memory.
### Why this bottlenecks performance
Even if you shrink the context window to 2048 or 1024 , the static model parameters themselves are too large
to fit in the L4 GPU's VRAM:
• Transformer Layers (INT4): ~15.5 GiB
• Vocabulary Embedding & LM Head (BF16): ~9.4 GiB (5.05 billion parameters)
• Total Model Weights: ~24.9 GiB
• NVIDIA L4 GPU Limit: 24.0 GB (approx. 22.35 GiB usable in practice)
Because the weights alone (~24.9 GiB) exceed the L4's physical VRAM limit, setting --cpu-offload-gb=0 will
result in an immediate CUDA Out of Memory error during boot, regardless of how small the context window is.
With --cpu-offload-gb=5 active, vLLM offloads the LM head matrix to CPU RAM. During generation, the model must
execute the LM head lookup for every single generated token. Reading these offloaded weights from CPU RAM is
bottlenecked by the CPU memory bandwidth and the PCIe bus, restricting token generation to ~2 tokens/second.
──────
### How to get high speed (30+ tokens/sec)
You have two main paths to avoid CPU offloading and achieve fast generation speeds:
#### Option 1: Use a Smaller Model (Recommended for L4 GPU)
If you must run on a single NVIDIA L4 GPU, you can switch to a smaller model footprint that fits 100% in VRAM:
• Gemma 4 12B QAT ( google/gemma-4-12b-it-qat-w4a16-ct ): The weights fit comfortably on the L4 GPU, leaving
plenty of VRAM for the KV cache and enabling lightning-fast generation.
• Gemma 4 4B QAT ( google/gemma-4-4b-it-qat-w4a16-ct ).
#### Option 2: Deploy to a GPU with More VRAM
If you need to use the 31B QAT model, you can run it on a GPU instance with a larger memory footprint:
• NVIDIA RTX 6000 Ada (48GB VRAM)
• NVIDIA A100 (40GB or 80GB VRAM)
On these GPUs, you can set --cpu-offload-gb=0 and run the model completely in VRAM.
What about a low-end TPU?
> is v6e-1 an option ?
● WebSearch(GCP TPU v6e minimum slice size v6e-1 v6e-4) (ctrl+o to expand)
Yes, v6e-1 is a fully supported option on Google Cloud.
Here are the details for deploying to a v6e-1 :
### 🛠 Hardware Topology
• GCP Accelerator Type: v6e-1 (which uses a 1x1 TPU chip topology).
• Machine Type: ct6e-standard-1t (contains 1 TPU chip and 1/8th of a physical TPU host slice).
• Cost: $1.375 per hour (or ~$33.00/day since TPUs must run continuously).
### 🚀 Why it's a great fit for Gemma 31B QAT
Because a single TPU v6e chip provides 32 GB of high-bandwidth memory (HBM), you can load the entire 31B model
weights + embeddings + LM head directly into the TPU's memory without any CPU offloading:
• Footprint: ~27.5 GB total for the model and runtime.
• Remaining Memory: ~4.5 GB remains free to act as the KV cache context window.
• Performance: Because the vocabulary embeddings and LM head remain 100% in local high-speed HBM (bandwidth of 1.
63 Terabytes per second), it will generate tokens at full speed (30+ tokens/sec) instead of crawling at 2
tokens/sec.
What about Cost Trade-offs?
A high level cost analysis was done:
> how much more expensive are these options?
● Read(/home/xbill/gemma4-tips/gpu-31B-6000-devops-agent/cost_per_token_analysis.md) (ctrl+o to expand)
▸ Thought Process
Analyzing Cloud Costs
Here is a cost comparison of the active serving options on Google Cloud Platform (GCP).
Deployment Option | Active Hourly… | Sca… | Daily Cost (If Lef… | Primary Use Case
---------------------------------|----------------|------|---------------------|---------------------------------
Current Setup: 1x L4 GPU (Cloud | ~$1.37 / hour | Yes | $0.00 (Spins down | Low-concurrency, spiky
Run) | | | to 0) | workloads (runs slow).
Option A: 2x L4 GPUs (GKE / | ~$1.36 / hour | No | ~$32.64 / day | Balanced production APIs. Runs
GCE) | | (Unl | | at full speed.
| | ess | |
| | usin | |
| | g | |
| | GKE | |
| | Auto | |
### 💳 Hardware Cost Breakdown
| | scal | |
| | er) | |
Option B: 1x RTX 6000 Pro | ~$3.76 / hour | Yes | $0.00 (Spins down | Spiky developers/SRE workflows
(Cloud Run) | | | to 0) | requiring full speed.
Option C: Cloud TPU v6e-4 (VM | ~$5.50 / hour | No | $132.00 / day | 24/7 high-throughput production
Slice) | | | | (especially long-context).
──────
### 💡 Financial Recommendations
1. For Spiky/Occasional SRE Diagnostics (Developers run it on-demand):
• Winner: RTX 6000 Pro (Cloud Run).
• Even though the hourly rate ($3.76/hr) is higher than the L4 GPU, because it scales to zero when idle, you
only pay when active. If your team uses it for a total of 2 hours per day, your daily bill is only $7.52/day,
and you get maximum generation speeds (30+ tokens/sec).
2. For Spiky SRE Diagnostics but on a Budget:
• Winner: Current 1x L4 GPU (Cloud Run).
• If you don't mind the slow ~2 tokens/second generation speed during SRE query investigations, this is the
cheapest option by far, as you pay ~$1.37/hr only when queries are running.
3. For 24/7 Production/API Gateways:
• Winner: Cloud TPU v6e-4 (or GKE with 2x L4s if context windows are short).
• TPUs are significantly more cost-efficient per million tokens when running continuous, high-concurrency, or
long-context workloads. For long contexts, TPUs perform 8x cheaper per token than GPUs due to their high
memory bandwidth.
Summary
Not every deployment is successful. Beyond storing model weights, the KV cache, context window, and initial setup need fast GPU memory to deploy successfully. The key to sizing a deployment with hardware constraints is managing the GPU memory and validating the trade-offs for latency and throughput.
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