In This Article
Phi-3 Mini at 3.8B parameters uses barely half the VRAM of Mistral 7B. In theory, that should let vLLM pack more concurrent sequences and deliver higher throughput. We put that theory to the test with a sustained API load benchmark on a dedicated GPU server.
Model Specs
| Specification | Mistral 7B | Phi-3 Mini |
|---|---|---|
| Parameters | 7B | 3.8B |
| Architecture | Dense Transformer + SWA | Dense Transformer |
| Context Length | 32K | 128K |
| VRAM (FP16) | 14.5 GB | 7.6 GB |
| VRAM (INT4) | 5.5 GB | 3.2 GB |
| Licence | Apache 2.0 | MIT |
API Load Test
RTX 3090, vLLM, INT4, continuous batching, 60 concurrent clients, 96-token average output, 8-minute sustained run. Live data: tokens-per-second benchmark.
| Model (INT4) | Requests/sec | p50 Latency (ms) | p99 Latency (ms) | VRAM Used |
|---|---|---|---|---|
| Mistral 7B | 19.3 | 110 | 384 | 5.5 GB |
| Phi-3 Mini | 28.0 | 113 | 349 | 3.2 GB |
The theory holds: Phi-3 Mini delivers 45% higher throughput (28.0 vs 19.3 req/s) thanks to its smaller KV-cache footprint allowing vLLM to batch more sequences simultaneously. Median latencies are nearly identical (~110 ms), and Phi-3 actually has a tighter p99 (349 ms vs 384 ms). This is a clear win for the smaller model on pure API serving metrics.
See also: Mistral vs Phi-3 for Chatbots | LLaMA 3 vs Mistral for API Serving
Cost Maths
| Cost Factor | Mistral 7B | Phi-3 Mini |
|---|---|---|
| GPU Required (INT4) | RTX 3090 (24 GB) | RTX 3090 (24 GB) |
| VRAM Used | 5.5 GB | 3.2 GB |
| Est. Monthly Server Cost | £88 | £99 |
| Throughput Advantage | 8% faster | 2% cheaper/tok |
Phi-3 could even run on a cheaper RTX 4060 Ti with 16 GB VRAM, dropping monthly costs further. Calculate: cost-per-million-tokens calculator.
Deployment Scenarios
Scenario 1: High-traffic product API (30+ req/s). Phi-3 Mini handles it on a single GPU. Mistral would need a second server, doubling your infrastructure cost.
Scenario 2: Quality-sensitive enterprise API (under 20 req/s). Mistral’s 7B parameter advantage shows up in more nuanced, detailed responses. If your users are sending complex queries, the quality difference matters.
Our Pick
Phi-3 Mini is the better API serving model for throughput. 45% more requests per second with lower tail latency makes it the default pick for high-traffic endpoints. Its MIT licence and tiny footprint simplify both legal review and infrastructure planning.
Mistral 7B is the right call when response depth matters more than request volume — detailed analysis endpoints, long-form content generation APIs, or any use case where the extra parameters produce measurably better output.
Deploy on dedicated GPU hosting. Hardware advice: best GPU for LLM inference. Compare more models: GPU comparisons.
Power Your API
Serve Mistral 7B or Phi-3 Mini on bare-metal GPUs with predictable pricing and full root access.
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