Batch processing is the GPU equivalent of doing laundry — nobody cares how long each item takes as long as the whole load finishes before morning and the electricity bill stays reasonable. For overnight jobs like bulk classification, content moderation queues, or dataset annotation, the only metric that matters is cost per million tokens processed. We put LLaMA 3 8B and DeepSeek 7B through the wash to see which one costs less per load.
Batch Throughput and GPU Utilisation
Both models ran on an RTX 3090 with INT4 quantisation, vLLM continuous batching maxed out, processing a queue of 50,000 prompts averaging 200 input tokens and 150 output tokens each. Current speeds on the benchmark tool.
| Model (INT4) | Batch tok/s | Cost/M Tokens | GPU Utilisation | VRAM Used |
|---|---|---|---|---|
| LLaMA 3 8B | 276 | $0.14 | 96% | 6.5 GB |
| DeepSeek 7B | 255 | $0.18 | 92% | 5.8 GB |
LLaMA pushes 276 tok/s versus DeepSeek’s 255, an 8% throughput lead. More importantly, LLaMA hits 96% GPU utilisation compared to 92% — it saturates the card more effectively under batch conditions, meaning less wasted compute per hour of runtime. At $0.14 per million tokens against $0.18, the cost advantage compounds quickly across large jobs.
Model Specs for Batch Sizing
| Specification | LLaMA 3 8B | DeepSeek 7B |
|---|---|---|
| Parameters | 8B | 7B |
| Architecture | Dense Transformer | Dense Transformer |
| Context Length | 8K | 32K |
| VRAM (FP16) | 16 GB | 14 GB |
| VRAM (INT4) | 6.5 GB | 5.8 GB |
| Licence | Meta Community | MIT |
DeepSeek uses less VRAM (5.8 GB versus 6.5 GB), which theoretically allows larger batch sizes. In practice, the 32K context window allocates more KV cache memory per sequence, partially eating that VRAM saving. For short-prompt batch jobs, though, DeepSeek’s smaller model footprint does allow slightly larger in-flight batches. See the LLaMA VRAM guide and DeepSeek VRAM guide.
Monthly Running Costs
| Cost Factor | LLaMA 3 8B | DeepSeek 7B |
|---|---|---|
| GPU Required (INT4) | RTX 3090 (24 GB) | RTX 3090 (24 GB) |
| VRAM Used | 6.5 GB | 5.8 GB |
| Est. Monthly Server Cost | £95 | £155 |
| Throughput Advantage | 9% faster | 10% cheaper/tok |
Same card, same power draw. The throughput difference means LLaMA finishes the same batch job about 50 minutes sooner on a 10-hour run. Whether that time saving matters depends on your scheduling window. Plug in your actual volumes at the cost-per-million-tokens calculator.
The Bottom Line
LLaMA 3 8B is the batch processing pick. Higher throughput, better GPU utilisation, lower cost per million tokens. Unless your batch job specifically requires 32K context windows — processing very long documents in a single pass, for example — LLaMA is the more efficient engine for grinding through large queues. Explore further at the GPU comparisons hub.
DeepSeek only makes sense for batch work if your prompts regularly exceed 8K tokens, at which point LLaMA would need chunking that adds complexity and can degrade quality. For everything else, LLaMA wins on pure economics. Read the best GPU for LLM inference guide and self-host LLM guide for deployment details.
See also: LLaMA 3 vs DeepSeek for Chatbots | LLaMA 3 vs Mistral for Batch Processing
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