We sincerely apologize to everyone affected by the recent issue with a driver package that was previously available on an outdated GEEKOM support page. We also apologize for the concern, inconvenience, and loss of trust this has caused.

If you downloaded or ran the affected file, or believe your GEEKOM device may have been impacted, please contact GEEKOM Support. Even if you are unsure whether your situation is related, our team will assist you, assess the device, and provide clear next steps as quickly as possible.

Where our technical assessment confirms that the affected file caused a device malfunction, GEEKOM will:

• Provide inspection and necessary repair services at no cost.
• Cover reasonable shipping costs associated with the repair process.
• Replace the affected device if it cannot be restored to normal use through repair.

Please do not attempt to resolve the issue on your own if you are uncertain. Contact us with your device model, serial number, download source, and a brief description of what occurred so that we can prioritize your case.

GEEKOM Support: https://www.geekompc.com/contact-us/

We understand that an apology alone cannot undo the concern this incident has caused. What matters now is providing timely, responsible support to every affected customer and taking concrete steps to prevent a similar issue from happening again.

We are truly sorry and committed to making this right.

Please download drivers only through the current GEEKOM official support page.

We’ve seen the recent reports about a GEEKOM driver file from an older support page being flagged by security software. We take these reports seriously, and we’re sorry for the concern and inconvenience this has caused.

The file was associated with an outdated GEEKOM support resource that was still accessible through older pages and links appearing in search results. We have removed the affected legacy page and related resources, and we’re continuing to remove obsolete support pages and files.

If you downloaded the flagged file

If you previously downloaded the affected driver package from an older GEEKOM support page, please take the following steps:

• Do not run the installer.
• If you still have the file, delete it from your device.
• Run a full scan using Windows Security (Microsoft Defender) or another trusted security tool.
• Download the latest driver package through the current GEEKOM Support page.

If you have already run the file or are unsure about what to do, please contact GEEKOM Support so our team can assist you.

Downloading drivers safely

For future driver downloads, please use the Support section on the current GEEKOM website rather than older links that may still appear in Google search results.

We’re also reviewing our legacy support resources and updating how older files are managed to help prevent outdated downloads from remaining accessible.

We appreciate the community members who brought this to our attention. Thanks for flagging it and giving us the opportunity to address it.

Official Support: https://service.geekompc.com/

Obsolete support pages and files are being removed.

Hey everyone,

We’ve got two community discounts coming up for anyone who’s been looking at a GEEKOM Mini PC. Here are the details:

GEEKOM IT13 Max
August 10 – August 16
15% off in the US, Germany, France, Spain, Japan, Canada, Australia, and Italy
10% off in the UK
Code: RSIT13MX15

GEEKOM A5 2027 Edition
August 17 – August 23
10% off in the US, Germany, France, Spain, Japan, UK, Canada, Australia, and Italy
Code: RDTSA510

Both discounts are available on Amazon in the countries listed above.

The two models are pretty different, so we’re curious which one people here would pick for their own setup.

What would matter most to you if you were choosing between the two?

Performance, size, upgradeability, price, or something else?

Let us know what you’re looking for in a Mini PC.

Local LLM performance is not only about the model and hardware. The runtime you choose can also change the experience. We tested Ollama, LM Studio, and llama.cpp on the same GEEKOM A9 Mega with the same model and prompts to compare:

• Time to first token (TTFT)
• Average generation speed
• VRAM usage

Test setup

Item	Configuration
Device	GEEKOM A9 Mega
CPU	AMD Ryzen™ AI Max+ 395
GPU	Radeon™ 8060S Graphics
Memory	128GB
VRAM	16GB
Model	Gemma 4 12B Q4 (GGUF)
Context Length	32,768

Testing method: same questions, same model settings, recording TTFT, output speed, and VRAM usage.

Performance results

Engine	TTFT	Output Speed	VRAM Usage
Ollama CMD	0.15s	22.78 tok/s	9.6GB
Ollama Desktop	~5s	N/A	9.2GB
LM Studio	5.15s	24.07 tok/s	10.2GB
llama.cpp	5.4s	25.10 tok/s	9.9GB

What did we learn?

1. Ollama CMD was in a different league for first token latency.
   At 0.15s TTFT, it was significantly faster than the other three. However, Ollama Desktop performed closer to LM Studio, with around 5 seconds of waiting time before the first token appeared.

2. Output speed was much closer than expected.
   The three engines were within a relatively small range:

llama.cpp (25.10 tok/s) > LM Studio (24.07 tok/s) > Ollama CMD (22.78 tok/s)

The difference between the fastest and slowest was less than 10%.

3. VRAM usage was very similar.
   All three stayed around the 10GB range. Ollama Desktop used slightly less VRAM at 9.2GB, which may be related to how the desktop interface handles memory allocation.

Which one should you choose?

There is no single “best” engine. It depends on how you use local models.

1. New to local LLMs or want a GUI, choose LM Studio or Ollama Desktop

LM Studio offers a more complete GUI experience with:

• Built-in model browser
• VRAM monitoring
• Chat interface

Ollama Desktop is also easy to use if you prefer the Ollama ecosystem. One thing to keep in mind, Ollama Desktop’s TTFT was similar to LM Studio in our test. If fast first response matters most, Ollama CMD performed much better.

2. Personal development or daily quick chats, choose Ollama CMD

The 0.15s first token latency gave the best interactive experience in this test. With OpenAI-compatible API support, you can also move applications from cloud models to local models more easily.

3. Application integration or API services, choose Ollama API or llama.cpp server

For lightweight integration and personal projects, Ollama API is usually the simpler option. For higher concurrency or production-style workloads, llama.cpp server gives more direct control and avoids some limitations from Ollama’s HTTP layer and single-instance setup.

4. Maximum performance tuning, choose llama.cpp CLI

If you enjoy tuning every last bit of performance, llama.cpp provides more control. Advanced options like:

• Flash Attention
• KV cache optimization
• Memory locking
• GPU offloading

can help you get more performance from the same hardware.

Final thoughts

This test was a good reminder that local AI performance is not only about picking the biggest model or the fastest hardware. The software layer matters too. We’re curious what everyone is using for local LLMs right now:

Ollama, LM Studio, llama.cpp, or something else?

And what matters more in your daily workflow: faster first response or higher tokens per second?

Hey everyone, we’ve seen a lot of discussion around compact gaming PCs recently, so we wanted to put together a practical comparison between the Steam Machine 2TB and GEEKOM A9 Max 2026 2TB.

The question is simple: Do you want a dedicated Steam gaming machine, or a compact PC that can handle work, entertainment, and gaming?

Quick Specs Comparison

	Steam Machine	GEEKOM A9 Max 2026
Price	$1,349	$1,464 (with SUMMER2026 code)
CPU	Semi-custom AMD Zen 4 6C/12T	AMD Ryzen™ AI 9 HX 470
GPU	Semi-custom AMD RDNA3 28 CUs	AMD Radeon™ 890M Graphics
Memory	16GB DDR5 + 8GB GDDR6 VRAM	Up to 128GB DDR5 SO-DIMM
Storage	2TB NVMe SSD	2TB NVMe Gen4 SSD
Operating System	SteamOS 3 (Arch-based)	Windows 11 Pro (Linux compatible)
Wireless	Wi-Fi 6E + Bluetooth 5.3	Wi-Fi 7 + Bluetooth 5.4
Ethernet	Gigabit Ethernet	Dual 2.5Gb Ethernet
Size	156 × 162.4 × 152 mm	5.31 × 5.20 × 1.81 inches

Connectivity Comparison

One thing we think is interesting is how differently these two machines approach connectivity. The Steam Machine covers the essentials for a gaming-focused setup. GEEKOM A9 Max 2026 adds more flexibility for people who want to use their mini PC as a daily computer.

Feature	Steam Machine 2TB	GEEKOM A9 Max 2026 2TB
Display Output	DisplayPort 1.4 + HDMI 2.0	Dual HDMI 2.1 + USB4 Type-C
USB	2× Front USB-A2× Rear USB-A1× Rear USB-C	4× Front USB-A2× Rear USB-A2× USB4 Type-C
Storage Expansion	MicroSD card slot	SD Card 4.0 slot

How we see the difference

The Steam Machine is built around a focused gaming experience. It makes sense for someone who mainly wants to play Steam games and prefers a more console-like setup.

GEEKOM A9 Max 2026 takes a different approach. It is designed more as a compact desktop PC that can also handle gaming.

For people whose daily routine looks something like: Work → browsing → productivity apps → gaming after hours, having one small machine that can cover different needs can be a pretty convenient setup.

Price Update

Since price is always an important part of the decision, we wanted to share a quick update. GEEKOM A9 Max 2026 2TB is currently available at $1,464 with code SUMMER2026 during our summer promotion. The promotion ends on July 31, so we wanted to mention it here while comparing these two models. After that, pricing may return to the regular level.

Curious what you think: if you were choosing between these two, would you rather have a dedicated Steam gaming machine? Or a small PC that can replace your everyday computer and still handle gaming?

Also curious: what is your ideal Mini PC setup? A living room gaming box, a main work PC, or something somewhere in between?

Would love to hear how everyone is actually using these small machines.

Everyone loves Mac Studios for big local VRAM, but we wanted to see how far we could push the new AMD Ryzen™ AI Max+ 395 architecture on Linux. We loaded up a GEEKOM A9 Mega test rig with 128GB of unified memory running Ubuntu, specifically to benchmark local VLM deployments for massive 70B models. If you have tried setting up VLMs on AMD APUs under Ubuntu, you already know the ROCm driver matrix is an absolute minefield. To save you days of segmentation faults, here is our quick pitfall and optimization guide. Feel free to ask us anything!

Revision: v2.0 (Updated June 3, 2026) Hardware: AMD Ryzen AI Max+ 395 w/ Radeon 8060S Unified Memory: 128GB (approx. 96GB+ available as shared VRAM) OS: Ubuntu 24.04 / 25.04 Noble Goal: Local deployment of Qwen2.5-VL-7B multimodal LLM with both WebUI and API access

1. Hardware Specs

Component	Specification	Notes
APU	AMD Ryzen AI Max+ 395	Zen 5 + RDNA 3.5, Strix Halo architecture
GPU	Radeon 8060S Graphics	gfx1150, 40 CU, shared memory architecture
Unified Memory	128GB	CPU/GPU shared, ~96GB recognized by OS
Storage	PCIe SSD	System drive
OS	Ubuntu 24.04 / 25.04 Noble	Kernel 6.8+, ROCm 7.0.2

1. Key Takeaways (TL;DR)

• ROCm Backend: Not ready for VLMs. Driver support for gfx1150 (Strix Halo) is still immature for multimodal paths (mmproj/CLIP), causing immediate segmentation faults when loading vision models.
• Vulkan Backend: The only viable path. RADV (Mesa Vulkan) works perfectly with gfx1151, providing a highly stable VLM loading experience.
• VRAM Advantage: 80GB+ available VRAM. You can comfortably load 70B models or run multiple models simultaneously.
• WebUI: Requires manually building llama-ui and recompiling llama-server to embed the graphical interface.

1. Full Debugging Timeline

Phase 1: The ROCm Attempt (Failure)

Tried launching with ROCm initially:

./llama-server -m ~/models/qwen2.5-vl-7b-instruct-q4_k_m.gguf --mmproj ~/models/mmproj-model-f16.gguf -ngl 999 --host 0.0.0.0 --port 8080

• Result: Segmentation fault (core dumped)
• Diagnosis: ROCm recognizes the GPU just fine (Radeon 8060S Graphics 48093 MiB), but even pure text models (dropping the --mmproj flag) crash with a segfault.
• Conclusion: This is a core incompatibility between the ROCm backend and gfx1150, not an mmproj path issue. Don't waste your time troubleshooting ROCm on Strix Halo right now, driver support is lagging.

Phase 2: Switching to Vulkan Compilation

Moved over to build with Vulkan instead:

cd ~/build/llama.cpp
rm -rf build
mkdir build && cd build
cmake .. -DGGML_VULKAN=ON -DGGML_NATIVE=OFF -DCMAKE_BUILD_TYPE=Release
cmake --build . --config Release -j$(nproc)

Gotchas encountered during build:

Gotcha 1: Missing glslc

Error:

CMake Error: Could NOT find Vulkan (missing: glslc)

Fix:

sudo apt update && sudo apt install -y vulkan-tools libvulkan-dev glslc spirv-tools

Gotcha 2: Missing SPIRV-Headers

Error:

CMake Error: SPIRV-HeadersConfig.cmake is missing

Fix:

cd /tmp
git clone --depth 1 [https://github.com/KhronosGroup/SPIRV-Headers.git](https://github.com/KhronosGroup/SPIRV-Headers.git)
cd SPIRV-Headers
mkdir build && cd build
cmake .. -DCMAKE_INSTALL_PREFIX=/usr
sudo cmake --build . --target install

Note: These build errors are identical to what you run into when compiling Vulkan for the Intel Arc B60.

Phase 3: Successful Vulkan Compilation and Launch

Launch command:

cd ~/build/llama.cpp/build/bin
./llama-server -m ~/models/qwen2.5-vl-7b-instruct-q4_k_m.gguf --mmproj ~/models/mmproj-model-f16.gguf -ngl 999 --host 0.0.0.0 --port 8080

Key lines from successful startup logs:

device_info:
  - Vulkan0 : Radeon 8060S Graphics (RADV GFX1151) (80861 MiB, 80861 MiB free)
  - CPU     : AMD RYZEN AI MAX+ 395 w/ Radeon 8060S (96186 MiB, 96186 MiB free)
load_model: loading model ...
mtmd_model_load: loaded CLIP model ...
llama_server: server is listening on [http://0.0.0.0:8080](http://0.0.0.0:8080)

• Status: API is fully functional. Running curl http://localhost:8080/v1/models successfully returns the expected JSON response.

Phase 4: Fixing the WebUI (Fixing 404 Errors)

• The Issue: Accessing http://localhost:8080/ in the browser throws a 404 File Not Found error.
• Root Cause: If tools/ui/dist doesn't exist during your initial compilation, CMake skips packaging the WebUI. Even if you build the UI later, the resource files aren't embedded into the compiled binary.

Fix Steps:

Step 1: Set up Node.js (Requires Node.js >= 20)

curl -fsSL [https://deb.nodesource.com/setup_20.x](https://deb.nodesource.com/setup_20.x) | sudo -E bash -
sudo apt install -y nodejs
node --version  # v20.20.2
npm --version   # 10.8.2

Step 2: Build the UI assets

cd ~/build/llama.cpp/tools/ui
rm -rf node_modules dist
npm install
npm run build

Output confirmation:

Wrote site to "./dist"
✔ done
✓ Copied bundle.js
✓ Copied bundle.css

Step 3: Recompile llama-server to embed the UI

cd ~/build/llama.cpp
rm -rf build
mkdir build && cd build
cmake .. -DGGML_VULKAN=ON -DGGML_NATIVE=OFF -DCMAKE_BUILD_TYPE=Release
cmake --build . --config Release -j$(nproc)

Now that tools/ui/dist exists, CMake will automatically bundle it into llama-server.

Step 4: Verify WebUI Hit http://localhost:8080/ in your browser. You should now see the chat layout with the conversation sidebar, main chat space, settings icon, and the text/image upload input bar.

1. Model Download (Qwen2.5-VL-7B)

​

mkdir -p ~/models && cd ~/models
# Main Model (Q4_K_M, ~4.4GB)
wget -c "[https://hf-mirror.com/lmstudio-community/Qwen2.5-VL-7B-Instruct-GGUF/resolve/main/Qwen2.5-VL-7B-Instruct-Q4_K_M.gguf](https://hf-mirror.com/lmstudio-community/Qwen2.5-VL-7B-Instruct-GGUF/resolve/main/Qwen2.5-VL-7B-Instruct-Q4_K_M.gguf)" -O qwen2.5-vl-7b-instruct-q4_k_m.gguf
# Vision Projector (mmproj, F16, ~1.3GB)
wget -c "[https://hf-mirror.com/lmstudio-community/Qwen2.5-VL-7B-Instruct-GGUF/resolve/main/mmproj-model-f16.gguf](https://hf-mirror.com/lmstudio-community/Qwen2.5-VL-7B-Instruct-GGUF/resolve/main/mmproj-model-f16.gguf)" -O mmproj-model-f16.gguf

1. Benchmark and Performance Metrics

Metric	Value	Notes
Decoding Speed	50.88 tok/s	Vulkan backend, 7B Q4_K_M
Context Window	128,000 tokens	Native support
VRAM Footprint	~5 to 6 GB	7B Q4 + mmproj
Available VRAM Headroom	74GB+	Enough to run a 70B model or host multiple instances
Parallel Slots	4 slots	n_parallel = 4

1. Summary of Pitfalls and Solutions

Pitfall	Symptom	Solution
ROCm segfault	Segmentation fault when loading any model	Ditch ROCm for now, use the Vulkan backend
Missing glslc	CMake throws "Could NOT find Vulkan"	Run "sudo apt install glslc"
Missing SPIRV-Headers	"SPIRV-HeadersConfig.cmake" is missing	Build and install "Khronos/SPIRV-Headers" from source
Outdated Node.js	"npm install" throws "EBADENGINE"	Upgrade Node.js to version 20.x or higher
WebUI 404	Accessing "/" gives a 404 error	Build "tools/ui/dist" first, then recompile "llama-server"
Path Error	"failed to open GGUF file"	Make sure model files are in the root of "~/models/", not a subfolder

1. API Call Examples

7.1 Get Model List

curl http://localhost:8080/v1/models

7.2 Pure Text Chat Completion

curl http://localhost:8080/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "qwen2.5-vl-7b-instruct-q4_k_m.gguf",
    "messages": [{"role": "user", "content": "Hello"}]
  }'

7.3 Multimodal (Image Understanding)

curl http://localhost:8080/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "qwen2.5-vl-7b-instruct-q4_k_m.gguf",
    "messages": [{"role": "user", "content": [
      {"type": "image_url", "image_url": {"url": "data:image/jpeg;base64,/9j/4AAQ..."}},
      {"type": "text", "text": "Describe this image"}
    ]}]
  }'

1. Quick Command Reference

​

# === Start VLM Service ===
cd ~/build/llama.cpp/build/bin
./llama-server -m ~/models/qwen2.5-vl-7b-instruct-q4_k_m.gguf --mmproj ~/models/mmproj-model-f16.gguf -ngl 999 --host 0.0.0.0 --port 8080

# === Build WebUI (First time or after updates) ===
cd ~/build/llama.cpp/tools/ui
rm -rf node_modules dist
npm install
npm run build

# === Recompile Binary (Crucial after WebUI updates) ===
cd ~/build/llama.cpp
rm -rf build && mkdir build && cd build
cmake .. -DGGML_VULKAN=ON -DGGML_NATIVE=OFF -DCMAKE_BUILD_TYPE=Release
cmake --build . --config Release -j$(nproc)

# === Verify Vulkan Detection ===
vulkaninfo --summary | grep -i "deviceName"

1. Head-to-Head: AMD AI Max 395 vs. Intel Arc B60

Dimension	AMD AI Max 395	Intel Arc B60
Unified/Shared Memory	128GB (~96GB+ allocatable)	24GB VRAM + 32GB System RAM
VLM Backend	Vulkan (Stable)	Vulkan (Stable)
ROCm / SYCL Support	ROCm immature	SYCL immature
Max Model Capacity	Can run 70B models	Caps out around 14B to 32B (Q4)
Multimodal Handling	Smooth on Qwen2.5-VL-7B	Runs it, but VRAM gets tight
Video Generation	Untested	Stable at 480p/512p, 720p fails
Token Throughput	50+ tok/s (7B model)	8 to 15 tok/s (7B model)

Final Verdict

The 128GB unified memory on the AMD AI Max 395 is absolutely killer for local open-source LLM setups. While ROCm is a bust right now, the Vulkan (RADV) route is fully viable, providing stable performance for Qwen2.5-VL-7B and much larger models. Just remember to manually build the UI assets before your final binary compilation so the server embeds the WebUI properly.

That is pretty much it. AMA. Are you guys planning to deploy LLMs locally on your own PCs? Have you run into any weird issues or bugs lately? Let us know in the comments.

The Summer Sale is finally here, and I went ahead and organized all the key global discounts in one place so you don’t have to dig around for the best value.

This is our biggest sale of the year so far, running through July 19, and for the Reddit community we made sure the product deals are at least as strong as the sitewide offers, and in some cases even better.

Part 1: Sitewide Savings

Most regions can use the following codes across the store this summer:

🇺🇸 USA / 🇨🇦 Canada / 🇪🇺 EU / 🇬🇧 UK
18% OFF sitewide (Code: SUMMER2026)

🇦🇺 Australia
18% OFF sitewide (Code: WINTER2026)

If you are just grabbing a daily setup or upgrading your main machine, these codes already give you solid value across the board.

Part 2: Featured Model Highlights (Better than sitewide in many cases)

We also pulled together the strongest product specific deals. In some cases, these beat the 18% sitewide discount, so it is worth checking both before you checkout.

Region	Model	Discount	Code
USA 🇺🇸 / Canada 🇨🇦	GEEKOM A8	20% OFF	RSA8
USA 🇺🇸 / Canada 🇨🇦	GEEKOM A7 Max	16% OFF	RSA7
UK 🇬🇧 / Germany 🇩🇪	GEEKOM A7 Max	20% OFF	RSA7
USA 🇺🇸	GeekBook X16 Pro	17% OFF	RSX16
Germany 🇩🇪	GeekBook X16 Pro	18% OFF	RSX16
Germany 🇩🇪	GeekBook X14 Pro	12% OFF	RSX14
France 🇫🇷 / Italy 🇮🇹 / Spain 🇪🇸	GEEKOM A5 Pro 7430U	25% OFF	RSA5

All codes listed here are only available on our official store and run through July 19.

How to get the most out of this sale

If you are looking at something like GEEKOM A7 Max or GEEKOM A5 Pro, it is worth comparing the product code with the sitewide code at checkout since the better deal can vary depending on region.

Also feel free to ask in the comments if you are unsure which config fits your setup. Happy to help you pick the right one.

Quick question for you

If you were upgrading this summer, what would you go for?

A compact mini PC for daily use
A home lab or HTPC build
A laptop upgrade
Or something completely different

Curious what everyone is building right now.

Let's be real for a second. Choosing the right LLM for the OpenClaw framework is basically a high stakes balancing act between raw reasoning, latency, and how fast you want to burn through your API credit. If you have spent any time in this ecosystem, you already know the pain of watching a dumb model get stuck in an infinite loop.

Based on the latest 2026 Q1 PinchBench data and some painful trial and error from the community, here is the breakdown of what actually works, what is too slow, and what will just bankrupt you.

1. Core Routing Logic for OpenClaw Models

We recommend implementing a tiered "Primary + Fallback" model routing strategy within OpenClaw:

• Primary Model: Responsible for complex planning and decision-making. It must have top-tier instruction-following capabilities, otherwise the Agent will get stuck in infinite loops or throw formatting errors.
• Fallback Model: Automatically takes over when the primary model triggers rate limits or API errors. This is typically a cheaper, lower-latency model.
• Local Model: Used for simple tasks or privacy-sensitive workloads, hooked up via Ollama to keep your API costs at zero.

2. Mainstream Model Comparison Table (2026 Q1 Data)

Model Name	PinchBench Score (Success Rate)	Average Latency	Token Cost	Best Used For
Claude 4.6 Sonnet	94.5%	Medium	High	Complex Planning & File Systems
MiniMax M2.5	89.2%	Ultra-Low	Medium	High-Speed Coding & Architecture
Gemini 3 Flash	82.1%	Low	Low	Fallback & Long-Context Aggregation
DeepSeek R1	91.0%	High (Reasoning)	Low	Hard Debugging & Logical Extraction
Qwen2.5-Coder (70B)	85.4%	Medium (Local)	Free	Privacy-First Local Automation

3. OpenClaw Configurations for Different Scenarios

Maximum Success Rate (The "Infinite Budget" Stack)

• Configuration: Primary: Claude 4.6 Sonnet | Fallback: Gemini 3 Flash
• Real-World Experience: Claude handles nested Tool Calls with the lowest error rate in the framework, especially when dealing with complex file system operations and environment setups.

Development Efficiency & Speed (The "High-Velocity" Stack)

• Configuration: Primary: MiniMax M2.5
• Real-World Experience: MiniMax recently rolled out deep optimizations specifically for OpenClaw. According to OpenClaw’s creator, M2.5 cuts completion time for identical coding tasks by nearly 40% compared to GPT-4o, and its "architect-level mindset" automatically deconstructs complex requirements.

Low Cost / Self-Hosted (The Geek Favorite)

• Configuration: Primary: DeepSeek R1 or Qwen2.5-Coder (32B/70B)
• Real-World Experience: DeepSeek R1's raw reasoning power is incredible, but it occasionally outputs excessively long thinking processes. For local deployments, running Llama 3.3 or Qwen2.5-Coder via Ollama keeps daily automation costs at zero.
• Hardware Note: If you need a dedicated, compact node to run these larger models locally 24/7, we have been running our tests on the GEEKOM A9 Max 2026 AMD Ryzen™ AI 9 HX470, which handles the continuous token generation loops smoothly.

4. Pitfalls to Avoid

• Beware of "Smol" Models: 3B and 7B models (like Llama 3.2 3B) break down easily in OpenClaw. They fail to close JSON tags properly and cannot comprehend complex system instructions.
• Watch Your Token Burn Rate: OpenClaw agent loops consume an immense amount of tokens. Running top-tier models like GPT-4.5 Preview or Claude Opus can easily rack up a bill of dozens of dollars in a single hour of testing. Keep them reserved strictly for your hardest debugging sessions.
• Network Stability: For developers experiencing network latency or regional blocks, prioritize local deployments, reliable reverse proxies, or native APIs with robust edge networks to prevent Agent tasks from dropping mid-loop.

Summary Recommendation

If you are just spinning up OpenClaw for the first time, save your wallet and start with Gemini 3 Flash to test your pipelines. Upgrade to Claude 4.6 Sonnet when you need heavy logical lifting, or swap to MiniMax M2.5 if latency is absolutely killing your workflow.

But that is just based on my testing. What stack are you guys currently running for your OpenClaw agents? Which model surprised you, and which one completely broke your JSON parsing? Let's discuss in the comments!

The Spring Setup Showcase has officially wrapped up, and we’ve picked the winners.

Huge shoutout to everyone who shared their setups. There were a lot of creative builds, clean desks, and interesting ideas across the board.

Here are the winners:

u/zeke009 receives a GEEKOM A5 Mini PC (Ryzen 7 5825U, 16GB RAM, 512GB SSD)
u/crhorsley1 receives a GEEKOM Anniversary Gift Box
u/Locke357 receives a GEEKOM 10 in 1 USB C Hub

We have reached out to the winners via Reddit DM, so please check your messages.

If you didn’t win this time, your setup definitely didn’t go unnoticed. There were a lot of great posts in this showcase, and we’re already looking forward to seeing more.

Feel free to keep sharing your setups and ideas. That’s what keeps this community fun. If you’re new here, stick around, there’s more coming.

Thanks again for being part of it.