Mistral 7B at 475 tok/s versus LLaMA 3 8B at 276 tok/s. That is not a typo — Mistral processes batch workloads 72% faster than LLaMA on the same GPU. For anyone running nightly classification jobs, content moderation queues, or large-scale data annotation, this throughput gap changes the economics completely.
Batch Processing Numbers
Both models on an RTX 3090, INT4 quantisation, vLLM with maximum batch sizes. Queue of 50,000 prompts, 200 input tokens average. Current speed data.
| Model (INT4) | Batch tok/s | Cost/M Tokens | GPU Utilisation | VRAM Used |
|---|---|---|---|---|
| LLaMA 3 8B | 276 | $0.07 | 97% | 6.5 GB |
| Mistral 7B | 475 | $0.08 | 96% | 5.5 GB |
The sliding window attention architecture that makes Mistral faster for interactive use becomes a superpower in batch mode. SWA reduces the per-token compute overhead, and when you are processing thousands of requests without caring about latency, that savings multiplies. Both models hit near-perfect GPU utilisation (96-97%), but Mistral extracts more actual throughput per clock cycle.
The cost-per-million-tokens difference is interesting: LLaMA is marginally cheaper at $0.07 versus $0.08 despite being slower. This reflects LLaMA’s slightly lower overhead per token in isolation — the gap inverts when you measure cost per wall-clock hour of batch processing.
Spec Sheet
| Specification | LLaMA 3 8B | Mistral 7B |
|---|---|---|
| Parameters | 8B | 7B |
| Architecture | Dense Transformer | Dense Transformer + SWA |
| Context Length | 8K | 32K |
| VRAM (FP16) | 16 GB | 14.5 GB |
| VRAM (INT4) | 6.5 GB | 5.5 GB |
| Licence | Meta Community | Apache 2.0 |
Mistral’s lower VRAM allows larger batch sizes in-flight, which is part of why it achieves higher throughput. Details at the LLaMA VRAM guide and Mistral VRAM guide.
Monthly Cost Breakdown
| Cost Factor | LLaMA 3 8B | Mistral 7B |
|---|---|---|
| GPU Required (INT4) | RTX 3090 (24 GB) | RTX 3090 (24 GB) |
| VRAM Used | 6.5 GB | 5.5 GB |
| Est. Monthly Server Cost | £148 | £135 |
| Throughput Advantage | 14% faster | 2% cheaper/tok |
Same card, same power bill. The effective cost advantage goes to Mistral because it finishes the same job in roughly 58% of the time. That means your GPU is free sooner — for another batch job, or to power down and save on electricity. Use the cost calculator to model your workload. For hardware options see the best GPU for inference guide.
The Verdict
Mistral 7B is the batch processing champion in this pairing. Nearly double the throughput, comparable GPU utilisation, lower VRAM usage. If your batch work involves straightforward tasks — classification, extraction, tagging, summarisation of short texts — Mistral gets the job done faster and frees up your hardware sooner. Check the comparison hub for more.
LLaMA 3 8B only makes sense for batch work where output quality is critical enough to justify a 72% throughput penalty — think grading essays, generating customer-facing content, or annotating training data where accuracy directly impacts a downstream model. For setup help see the self-host LLM guide.
See also: LLaMA 3 vs Mistral for Chatbots | LLaMA 3 vs DeepSeek for Batch Processing
Batch Process at Scale
Run Mistral 7B or LLaMA 3 8B on dedicated GPU servers. No shared resources, no token caps.
Browse GPU Servers
