590 tok/s. That is Phi-3 Mini‘s batch throughput on an RTX 3090 — more than double LLaMA 3 8B‘s 276 tok/s. When you are processing hundreds of thousands of items overnight, that 2.1x speed advantage cuts your job time from ten hours to under five. The surprise is that Phi-3 achieves this while using less than half the VRAM.
Batch Processing Numbers
RTX 3090, vLLM, INT4, maximum continuous batching. 50,000 prompts at 200 input tokens. Current speeds.
| Model (INT4) | Batch tok/s | Cost/M Tokens | GPU Utilisation | VRAM Used |
|---|---|---|---|---|
| LLaMA 3 8B | 276 | $0.05 | 88% | 6.5 GB |
| Phi-3 Mini | 590 | $0.12 | 95% | 3.2 GB |
Phi-3’s tiny 3.2 GB footprint leaves massive VRAM headroom for the batch scheduler. vLLM can run far more sequences in parallel, which is why GPU utilisation hits 95% versus LLaMA’s 88%. The raw throughput numbers tell the story: for pure batch grinding, Phi-3 is simply faster.
Note the cost-per-million-tokens anomaly: LLaMA shows $0.05 versus Phi-3’s $0.12. This reflects per-token cost, not per-job cost. Because Phi-3 finishes twice as fast, the total GPU-hours spent per batch job favours Phi-3 despite the higher per-token rate.
Spec Comparison
| Specification | LLaMA 3 8B | Phi-3 Mini |
|---|---|---|
| Parameters | 8B | 3.8B |
| Architecture | Dense Transformer | Dense Transformer |
| Context Length | 8K | 128K |
| VRAM (FP16) | 16 GB | 7.6 GB |
| VRAM (INT4) | 6.5 GB | 3.2 GB |
| Licence | Meta Community | MIT |
Fewer parameters means less compute per forward pass, and less VRAM means more room for concurrent sequences. Both factors compound to give Phi-3 its batch processing edge. See the LLaMA VRAM guide and Phi-3 VRAM guide.
Running Costs
| Cost Factor | LLaMA 3 8B | Phi-3 Mini |
|---|---|---|
| GPU Required (INT4) | RTX 3090 (24 GB) | RTX 3090 (24 GB) |
| VRAM Used | 6.5 GB | 3.2 GB |
| Est. Monthly Server Cost | £113 | £85 |
| Throughput Advantage | 2% faster | 11% cheaper/tok |
Phi-3 could even run on a cheaper GPU card given its tiny VRAM requirements, pushing monthly costs even lower. Model the savings at the cost calculator. Hardware options at best GPU for inference.
Clear Winner
Phi-3 Mini is the batch processing champion. 2.1x the throughput, higher GPU utilisation, half the VRAM, MIT licence. For classification, tagging, extraction, moderation, and any other task where you need to grind through a large queue, Phi-3 finishes the job faster and frees up your GPU sooner. Browse more at the comparisons hub.
LLaMA 3 8B is only the better choice if your batch task requires the quality uplift that 8B parameters provide — think nuanced content generation or complex reasoning tasks where each output needs to be high-quality rather than just structurally correct. For everything else, Phi-3 wins on throughput economics. Deployment at the self-host guide.
See also: LLaMA 3 vs Phi-3 for Chatbots | LLaMA 3 vs DeepSeek for Batch Processing
Crunch Your Batch Jobs
Run Phi-3 Mini or LLaMA 3 8B on bare-metal GPU servers. No shared resources, no usage caps.
Browse GPU Servers
