Your SLA says p99 latency under 400 ms. Your product manager says the API needs to handle 15 requests per second at peak. Your budget says one GPU. When those three constraints collide, the choice between LLaMA 3 8B and DeepSeek 7B becomes a straightforward engineering decision rather than a model preference.
Raw Throughput Under Load
We hit both models with a sustained load test on an RTX 3090 — vLLM, INT4 quantisation, continuous batching, ramping from 1 to 50 concurrent connections. The live benchmark tool has current numbers.
| Model (INT4) | Requests/sec | p50 Latency (ms) | p99 Latency (ms) | VRAM Used |
|---|---|---|---|---|
| LLaMA 3 8B | 19.2 | 72 | 352 | 6.5 GB |
| DeepSeek 7B | 13.6 | 93 | 229 | 5.8 GB |
LLaMA handles 41% more requests per second. At the median, it responds in 72 ms against DeepSeek’s 93 ms. But look at the p99 column: DeepSeek’s tail latency is actually tighter at 229 ms versus LLaMA’s 352 ms. That means DeepSeek is more predictable under pressure — fewer outlier requests that blow past your latency budget.
Hardware and Architecture Specs
| 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’s lower VRAM footprint at 5.8 GB gives vLLM more room for its KV cache, which is why its tail latency stays tighter even though peak throughput is lower. For sizing details see the LLaMA 3 VRAM guide and DeepSeek VRAM guide.
Cost per Request at Scale
| 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 | £130 | £129 |
| Throughput Advantage | 8% faster | 12% cheaper/tok |
Near-identical monthly server costs. The difference is how many requests you squeeze out of that spend. LLaMA serves more total requests; DeepSeek serves each one more cheaply per token. Model your own traffic shape with the cost-per-million-tokens calculator.
The Decision Framework
LLaMA 3 8B wins if your API serves short-output requests at high concurrency — think classification endpoints, sentiment analysis, or summarisation where each response is under 100 tokens. The raw requests-per-second advantage dominates. For hardware guidance see best GPU for LLM inference.
DeepSeek 7B wins if your API handles variable-length outputs and your SLA is defined by tail latency rather than median throughput. Its tighter p99 makes it safer for user-facing endpoints where one slow response triggers a timeout. Compare more options in our comparison hub.
Either model slots behind a vLLM deployment on dedicated GPU hardware without shared-tenancy interference.
See also: LLaMA 3 vs DeepSeek for Chatbots | LLaMA 3 vs Mistral for API Serving
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