It’s been a while since I last wrote here. Lately, I’ve been diving deep into AI inference—the process of running AI models to generate responses—specifically exploring whether we truly need expensive GPUs for running modern language models. Spoiler alert: the answer might surprise you.
After extensive testing on Oracle Cloud Infrastructure (OCI), comparing ARM-based Ampere processors against the latest AMD EPYC chips, I discovered that the right combination of software optimizations and compressed models can deliver remarkable performance—all without a single GPU.
The “GPU Myth” and the CPU Reality
There’s a pervasive belief that serious AI inference requires expensive, power-hungry GPUs. Let me be clear: GPUs remain essential for large-scale inference and training. However, for small and medium chat applications—internal tools, customer support bots, or moderate-traffic services—modern CPUs are more than sufficient.
The key lies in quantization: a technique that compresses large AI models while preserving most of their intelligence. Think of it as converting a high-resolution image to a smaller file that still looks great on your phone screen—you trade minimal quality for significant resource savings.
The Quality vs. Efficiency Tradeoff
A common concern: “If we compress the model, won’t it become dumb?” The data says otherwise. According to recent research [arXiv:2601.14277] , here’s what happens when you compress Llama 3.1 8B:
| Compression Level | Model Size | Quality Retained | Use Case |
|---|---|---|---|
| FP16 (Original) | 16.0 GB | 100% | Research, baseline |
| Q8 (Light) | 8.5 GB | ~99.9% | Production |
| Q4 (Medium) | 4.8 GB | 99.7% | Recommended |
| Q3 (Heavy) | 3.9 GB | 94.5% | Memory-constrained |
The sweet spot is Q4: You get nearly identical quality at 70% less memory. This pattern applies to all major open-source models (Mistral, Gemma, Phi, Qwen, etc.).
The Ampere Advantage: Beyond Standard ARM
What really surprised me wasn’t just that CPUs could run these models—it was how fast they could run them with proper optimizations.
Ampere Computing has created a special version of llama.cpp specifically tuned for their processors, along with an optimized model format that reorganizes data for maximum efficiency. Pre-optimized models are available on Ampere’s HuggingFace page .
Performance Results
I tested Llama 3.1 8B (Q8 quantization) across different processor configurations. To understand these benchmarks: tokens per second (t/s) measures how fast the AI generates text. For context, humans read at roughly 4-5 words per second, so anything above ~6 t/s feels instantaneous.
| Processor | Configuration | Model Format | Processing Speed | Response Speed | vs. Baseline |
|---|---|---|---|---|---|
| Ampere A2 | 8 OCPUs (16 vCPUs) | Q8R16 (Optimized) | ~158 t/s | ~9.2 t/s | 2× faster |
| AMD EPYC “Turin” | 8 OCPUs (16 vCPUs) | Q8_0 (Standard) | ~72 t/s | ~4.3 t/s | Baseline |
| Ampere A2 | 8 OCPUs (16 vCPUs) | Q8_0 (Standard) | ~32 t/s | ~4.3 t/s | 0.4× |
The takeaway: With Ampere’s optimized Q8R16 format, the same hardware delivers 5× faster prompt processing and 2× faster token generation compared to the standard format.
Even Faster: Optimized Q4_K_4 Format
For chatbots and real-time applications where response speed is critical, the optimized Q4 format delivers the best user experience:
| Configuration | Processing Speed | Response Speed | Best For |
|---|---|---|---|
| Ampere A2 (8 OCPUs) + Q4_K_4 | ~79 t/s | ~11.5 t/s | Interactive apps, chatbots |
| Ampere A2 (8 OCPUs) + Q8R16 | ~158 t/s | ~9.2 t/s | Batch processing, quality-critical |
At 11.5 tokens per second, users experience near-instant responses—comparable to commercial AI services like ChatGPT.
Cost Analysis
Based on OCI Cost Estimator for VM.Standard.A2.Flex:
| Resource | Price |
|---|---|
| OCPU | $0.014/hour |
| Memory | $0.002/GB/hour |
Test configuration: 8 OCPUs / 16 vCPUs + 32GB = $0.19/hour (~$140/month)
Cost per 1M tokens (at 100% utilization):
| Configuration | Tokens/Hour | Cost per 1M Tokens |
|---|---|---|
| Q4 Optimized (11.5 t/s) | 41,400 | ~$4.6 |
| Q8 Optimized (9.2 t/s) | 33,120 | ~$5.7 |
Note: This assumes continuous generation. Real cost depends on your utilization—idle time increases effective per-token cost.
Choose self-hosted when you need:
- Data privacy: Your data never leaves your infrastructure
- Fine-tuned models: Run your own custom-trained models
- Predictable costs: Fixed monthly bill, no surprises from usage spikes
- No rate limits: Full capacity available anytime
- No vendor dependency: Switch models freely
When to Use CPU Inference
This is not about replacing GPUs—it’s about using the right tool for the job.
CPU inference is ideal for:
- Small to medium chat applications (up to ~50 concurrent users)
- Internal tools and employee-facing assistants
- Prototypes and proof-of-concepts
- Privacy-sensitive workloads requiring on-premise deployment
- Environments where GPUs aren’t available
GPUs remain necessary for:
- Large-scale production (hundreds/thousands of concurrent users)
- Larger models (70B+ parameters)
- Training and fine-tuning
- Low-latency requirements at high volume
Technical Implementation
For teams ready to implement, here’s a quick start on OCI’s Ampere instances:
1. Launch an Ampere Instance
Spin up a VM.Standard.A2.Flex instance on OCI.
2. Run the Optimized Container
sudo docker run --rm --privileged=true \
--name llama \
-v "$(pwd)/models:/models:rw" \
--entrypoint /bin/bash \
-it amperecomputingai/llama.cpp:latest-ampereone
3. Optimize Your Models
Inside the container, convert models to Ampere’s optimized formats. For this test, I used Dolphin3.0-Llama3.1-8B-GGUF :
# Q4 format: Best for interactive applications
./llama-quantize /models/Dolphin3.0-Llama3.1-8B-F16.gguf /models/Dolphin3.0-Llama3.1-8B-Q4_K_4.gguf Q4_K_4
# Q8 format: Best for quality-critical applications
./llama-quantize /models/Dolphin3.0-Llama3.1-8B-F16.gguf /models/Dolphin3.0-Llama3.1-8B-Q8R16.gguf Q8R16
4. Validate Performance
./llama-bench -m /models/Dolphin3.0-Llama3.1-8B-Q4_K_4.gguf -p 512 -n 512
Conclusions
GPUs aren’t going anywhere—they remain essential for large-scale AI deployments. But for small and medium chat applications, optimized CPU inference is now a viable option. With the right software and model formats, you can deploy production-ready AI assistants on standard cloud instances.
And this is just the beginning. OCI is introducing the new A4 shape based on Ampere’s next-generation processors, promising even greater performance for AI workloads. Stay tuned for benchmarks on the new hardware.