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Batch processing is the unglamorous workhorse of production AI — classifying millions of support tickets, tagging product catalogues, or extracting structured data from form submissions overnight. The metric that matters most is cost per million tokens, because latency is irrelevant when results are needed by morning, not by millisecond. We tested DeepSeek 7B and Qwen 2.5 7B in full batch mode on dedicated GPU hardware.
Spec Sheet
| Specification | DeepSeek 7B | Qwen 2.5 7B |
|---|---|---|
| Parameters | 7B | 7B |
| Architecture | Dense Transformer | Dense Transformer |
| Context Length | 32K | 128K |
| VRAM (FP16) | 14 GB | 15 GB |
| VRAM (INT4) | 5.8 GB | 5.8 GB |
| Licence | MIT | Apache 2.0 |
Both consume 5.8 GB at INT4, leaving ample headroom on an RTX 3090 for large batch queues. Memory planning: DeepSeek VRAM | Qwen VRAM.
Batch Numbers
Environment: RTX 3090, vLLM, INT4 quantisation, max batch packing. Workload: 200K classification prompts (48 input tokens, 16 output tokens). Throughput tracker: tokens-per-second benchmark.
| Model (INT4) | Batch tok/s | Cost/M Tokens | GPU Utilisation | VRAM Used |
|---|---|---|---|---|
| DeepSeek 7B | 255 | $0.16 | 96% | 5.8 GB |
| Qwen 2.5 7B | 264 | $0.08 | 95% | 5.8 GB |
The two models run neck-and-neck on raw throughput (264 vs 255 tok/s), but Qwen halves the cost per million tokens ($0.08 vs $0.16). Both achieve over 95% GPU utilisation, meaning the hardware is fully saturated with no idle cycles.
Also see: DeepSeek vs Qwen for Chatbots | LLaMA 3 vs DeepSeek for Batch Processing
Cost Breakdown
| Cost Factor | DeepSeek 7B | Qwen 2.5 7B |
|---|---|---|
| GPU Required (INT4) | RTX 3090 (24 GB) | RTX 3090 (24 GB) |
| VRAM Used | 5.8 GB | 5.8 GB |
| Est. Monthly Server Cost | £111 | £176 |
| Throughput Advantage | 10% faster | 3% cheaper/tok |
Model your exact volume with our cost-per-million-tokens calculator.
Use-Case Scenarios
Scenario A: Nightly ticket classification (500K items). Qwen’s $0.08/M token cost makes it roughly half the price for the same volume. At 264 tok/s, it finishes the job in about the same wall-clock time. Clear Qwen win.
Scenario B: Time-critical product data extraction (deadline in 4 hours). DeepSeek’s 10% throughput edge and 96% GPU utilisation squeeze the job in just under the wire. It also holds an MIT licence that avoids any commercial-use reviews.
Final Pick
Qwen 2.5 7B is the better batch processing model for most workloads. Matching throughput at half the per-token cost is hard to argue with. The 128K context window also means you can process longer documents without splitting them into multiple prompts, reducing pipeline complexity.
DeepSeek 7B is the fallback for organisations that need the MIT licence or prioritise the marginal throughput edge for time-constrained jobs.
Run your batch workloads overnight on dedicated GPU servers to maximise cost efficiency. For engine guidance: vLLM vs Ollama. For hardware: cheapest GPU for AI inference.
Batch Process at Scale
Run DeepSeek 7B or Qwen 2.5 7B on bare-metal GPUs — flat monthly pricing, no token limits, full root access.
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