Chinese companies are great at keeping costs low, but is their hardware actually better than Nvidia’s?
When it comes to AI hardware, the story often boils down to one thing: Nvidia sets the bar, and everyone else is scrambling to keep up. In recent years, export bans and rising silicon prices have pushed Chinese chipmakers into a tough, defensive position.
It’s widely believed that Chinese tech companies shine most when it comes to cutting costs. They know how to trim excess, make the most of established manufacturing processes, and churn out hardware at prices Western silicon giants can’t compete with.
But as we look closely at emerging architectures in the edge-AI and embedded space—such as SpacemiT’s newly delivered Key Stone K3 RISC-V SoC—a deeper, more complicated question emerges: Is this hardware actually better than Nvidia, or is it just cheaper?
To answer that, we have to look past the marketing hype and examine the cold, hard engineering trade-offs.
Architecture: Heterogeneous vs. Homogeneous Fusion
To understand where Chinese design philosophy is taking a lead, let’s contrast how a classic Nvidia edge system (like the Jetson Orin Nano) processes data versus how an advanced Chinese RISC-V chip operates.
Nvidia design philosophy
Nvidia’s approach uses heterogeneous computing, with an ARM CPU cluster managing the operating system and core logic, and a separate Nvidia Ampere or Blackwell GPU cluster handling parallel matrix calculations. Since these systems operate on different structural languages, data has to be constantly copied and transferred over the internal PCIe bus. This back-and-forth creates a “data transfer tax,” eating up valuable clock cycles and increasing power consumption.
SpacemiT K3 design philosophy
Now, consider a processor like the SpacemiT K3. Instead of pairing two entirely different computing architectures together, it employs what engineers call Homogeneous Fusion.
The K3 integrates eight X100 general-purpose CPU cores alongside eight A100 AI-oriented compute cores. Because every single core runs on the exact same unified RISC-V ISA (RVA23 Profile), they share a single coherent memory pool. The data doesn’t move across a bus to a GPU; it stays right in the CPU pipeline, executing tailored matrix extensions natively.
- The Verdict: For raw architectural data flow at the edge, this unified-ISA approach isn’t just a workaround—it is fundamentally cleaner engineering than feeding a separate GPU.
Conclusion: Better is Relative
Is the hardware actually better?
If “better” means sheer brute-force computing power, massive memory bandwidth, and unlimited power consumption for training cutting-edge models, Nvidia still reigns supreme.
But if “better” is defined as architectural elegance, data-movement efficiency, power-to-performance scaling at sub-30W, and absolute cost-disruption at the edge, Chinese RISC-V AI hardware is no longer just a cheap knock-off. It represents a fundamental evolution in domain-specific silicon design—proving that clever architectural co-design can soundly defeat raw, expensive transistor scaling.
Running the official llama.cpp benchmark tool (llama-bench)
Here’s a step-by-step breakdown of what each command does:
| Step | Command | Action | Why it is done |
| 1 | cd ~ | Navigates to your user’s home folder (/home/tzah). | Ensures the project downloads into a clean, safe workspace instead of a restricted system folder. |
| 2 | git clone [https://github.com/ggerganov/llama.cpp](https://github.com/ggerganov/llama.cpp) | Downloads the entire up-to-date source code repository from GitHub. | Creates a local copy of the llama.cpp files on your device in a new folder named llama.cpp. |
| 3 | cd llama.cpp | Changes your current terminal directory into the newly created project folder. | Moves you inside the codebase so you can run configuration and build tools on its files. |
| 4 | cmake -B build | Generates a custom build configuration and creates a folder named build. | Inspects your system hardware (like your Spacemit processor features) to generate a tailored compilation “recipe.” |
| 5 | cmake --build build --config Release | Compiles the raw C++ code into a finished, ready-to-run executable binary. | The --config Release flag instructs the compiler to heavily optimize the code for raw speed and AI performance. |
- Once those finishes running, we will have a working compiled binary (usually an executable named
mainorllama-cliinside thebuild/bin/folder). Then we can use it to load quantized (compressed) AI models and text-generate completely offline.
- After completing step 5, we will have a finely tuned local AI engine ready to run language models directly on our K3 Pico-ITX hardware.
Running our test
We used the following Bash script to run our test. In plain language: what does it actually do?
It:
- Runs a full LLaMA performance benchmark on our Spacemit K3 hardware.
- Measures tokens/sec, latency, prompt processing speed, generation speed, and thread scaling
- Saves the results into a Markdown file you can open anywhere
- Names the file after your device so you can compare it later with your Mac mini results
This script is specifically designed to let us use the same model, parameters, and benchmark tool to compare it with other devices such as:
- Mac mini (M1/M2/M4)
- Any Linux or ARM device
| Step | What it does | Why it matters |
|---|---|---|
| 1 | MODEL="models/llama-7b-q4_0.gguf" | Sets the model file llama‑bench will test. |
| 2 | THREADS=$(nproc) | Automatically uses all CPU cores on your device. |
| 3 | OUTFILE="llama_bench_results_$(hostname).md" | Creates a results file named after your machine (e.g., llama_bench_results_spacemit-k3.md). |
| 4 | Writes a Markdown header with hostname, CPU model, and thread count | Makes the results readable and comparable across devices. |
| 5 | Create a directory to store our model | mkdir -p ~/models |
| 6 | Downloads LLaMA‑3 8B Q4_K_M (GGUF) | wget -O ~/models/llama3-8b-q4_k_m.gguf https://huggingface.co/QuantFactory/Meta-Llama-3-8B-GGUF/resolve/main/Meta-Llama-3-8B.Q4_K_M.gguf |
| 7 | Runs our script with the following parameters: • 512 prompt tokens • 128 generation tokens • all CPU threads • 2048 batch size • Markdown output | This is the actual LLaMA performance benchmark. |
| 8 | Appends the benchmark output to the Markdown file | Saves everything in one clean report. |
| 9 | Prints “Benchmark complete…” | Confirms the script finished. |
Our test script
Runing the most popular benchmark model on Mac mini: LLaMA‑3 8B (Q4_K_M)
We chose this model because it’s the most popular among Mac mini users and has been tested more than any other. It shows up in nearly every:
- GitHub llama.cpp benchmark thread
- Reddit r/LocalLLaMA performance post
- Apple Silicon comparison
- M1 vs M2 vs M4 benchmark
- CPU vs Metal backend test
Why it’s our favorite:
- Fits comfortably in 16 GB RAM
- Strong real‑world performance
- Good balance of speed + quality
- Works perfectly with llama.cpp
- Ideal for comparing different CPUs (like your K3)
#!/bin/bash
MODEL="$HOME/models/llama3-8b-q4_k_m.gguf"
LLAMABENCH="$HOME/llama.cpp/build/bin/llama-bench"
THREADS=$(nproc)
OUTFILE="llama_bench_results_$(hostname).md"
echo "# LLaMA Benchmark Results for $(hostname)" > $OUTFILE
echo "## CPU: $(lscpu | grep 'Model name')" >> $OUTFILE
echo "## Threads: $THREADS" >> $OUTFILE
echo "## Model: LLaMA‑3 8B Q4_K_M" >> $OUTFILE
echo "" >> $OUTFILE
$LLAMABENCH \
-m $MODEL \
-p 512 \
-n 128 \
-t $THREADS \
-b 2048 \
-o md >> $OUTFILE
echo "" >> $OUTFILE
echo "Benchmark complete. Results saved to $OUTFILE"Comparing the K3 Pico-ITX with the Mac mini series featuring 16 GB of memory.
LLaMA‑3 8B Q4_K_M — Spacemit K3 vs Mac mini (16 GB RAM) + Estimated Prices (USD)
| Device | Threads | Prompt Speed (pp512) | Generation Speed (tg128) | Estimated Price (USD) | Notes |
|---|---|---|---|---|---|
| K3 Pico-ITX (our result) | 8 | 9.04 t/s | 3.05 t/s | ~$300 | Low‑cost RISC‑V SBC |
| Mac mini M1 (16 GB) | 8 | ~55–65 t/s | ~35–45 t/s | $650–$800 (used) | Best budget Apple Silicon |
| Mac mini M2 (16 GB) | 8 | ~70–85 t/s | ~45–55 t/s | $900–$1,100 | Faster memory + CPU |
| Mac mini M4 (16 GB) | 8 | ~110–140 t/s | ~70–90 t/s | $1,200–$1,400 | Latest generation |
Final Conclusions:
What do the results mean?
✔ Our device can run an 8B model, but it’s a bit slow—about 3 tokens per second.
✔ In terms of value for the money, the SpacemiT K3 Pico-ITX is a winner.
✔ The Mac mini is 10×–20× faster.
✔ For interactive use, a 2B–4B model will likely feel much smoother.
