You have a support chatbot that needs to reply before the user loses patience — roughly 200 milliseconds from the moment the request hits vLLM to the moment the first token streams back. That constraint alone narrows the field. Between LLaMA 3 8B and DeepSeek 7B, which model actually delivers under that kind of pressure?
We ran both through a realistic multi-turn chatbot workload on dedicated GPU hardware to find out. The results are closer than the spec sheets suggest, but one model pulls ahead where it counts.
The Benchmark Numbers
Both models ran on an RTX 3090 with INT4 quantisation via vLLM, continuous batching enabled, identical prompt sets and evaluation rubric. Check the tokens-per-second benchmark tool for live comparisons.
| Model (INT4) | TTFT (ms) | Generation tok/s | Multi-turn Score | VRAM Used |
|---|---|---|---|---|
| LLaMA 3 8B | 55 | 88 | 7.2 | 6.5 GB |
| DeepSeek 7B | 43 | 83 | 7.4 | 5.8 GB |
DeepSeek actually posts a faster time-to-first-token at 43 ms versus LLaMA’s 55 ms. That 12 ms gap is noticeable under load when you are batching dozens of concurrent sessions. However, LLaMA claws back the advantage in sustained generation speed — 88 tok/s against 83 — meaning once the response starts flowing, LLaMA finishes sooner on longer replies.
Why the Architecture Matters for Chat
| 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 32K context window is four times LLaMA’s 8K. For multi-turn chat where conversation history piles up, that headroom matters — you can fit far more turns before you need to truncate or summarise. LLaMA’s shorter window forces earlier context pruning, which can degrade coherence in long sessions. For VRAM specifics, see the LLaMA 3 VRAM guide and DeepSeek VRAM guide.
On quality, DeepSeek edges LLaMA with a 7.4 multi-turn score versus 7.2. The difference is subtle but consistent across follow-up questions that require the model to track earlier context. More comparisons in our GPU comparisons hub.
Running Costs on Dedicated Hardware
| 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 | £179 | £98 |
| Throughput Advantage | 2% faster | 9% cheaper/tok |
DeepSeek’s smaller footprint (5.8 GB versus 6.5 GB at INT4) leaves more VRAM headroom for larger batch sizes. If your chatbot serves many concurrent users, that spare capacity translates directly into more simultaneous sessions on the same card. Plug your expected traffic into the cost-per-million-tokens calculator to see the real difference.
The Verdict
This one depends on your conversation length. If your chatbot handles short, transactional exchanges — order status, FAQ lookups, appointment booking — LLaMA 3 8B is the better pick. Its higher generation speed means crisper responses for quick back-and-forth, and the quality gap is negligible on simple queries.
If your chatbot runs extended conversations — technical support threads, advisory sessions, anything where users send five or more messages in a row — DeepSeek 7B is the stronger choice. The 32K context window keeps the full conversation in scope, the quality score holds up better across turns, and the lower VRAM footprint gives you room to scale concurrency.
For the full picture on self-hosting either model, read our self-hosted LLM guide and the best GPU for LLM inference breakdown.
See also: LLaMA 3 8B vs DeepSeek 7B for Code Generation | LLaMA 3 8B vs Mistral 7B for Chatbots
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