Can a 3.8B parameter model keep up with an 8B model for code generation? The HumanEval numbers say it nearly can: Phi-3 Mini scores 48.7% pass@1 against LLaMA 3 8B‘s 45.5%. That is right — the smaller model actually produces more correct code completions, while using half the VRAM. The trade-off is speed: LLaMA completes faster per request.
Code Generation Numbers
RTX 3090, vLLM, INT4, continuous batching. Python, TypeScript, and SQL completion prompts. Live data.
| Model (INT4) | HumanEval pass@1 | Completions/min | Avg Latency (ms) | VRAM Used |
|---|---|---|---|---|
| LLaMA 3 8B | 45.5% | 39 | 258 | 6.5 GB |
| Phi-3 Mini | 48.7% | 32 | 325 | 3.2 GB |
Phi-3 wins on accuracy by 3.2 percentage points. LLaMA wins on throughput with 22% more completions per minute and 26% lower latency. This creates a genuine dilemma: do you want each completion to be more likely correct, or do you want to generate more completions in less time?
For IDE autocomplete where the developer sees one suggestion at a time, accuracy matters more. For batch code review or test generation where you can sample multiple outputs and filter, throughput matters more.
Why Phi-3 Punches Up
| Specification | LLaMA 3 8B | Phi-3 Mini |
|---|---|---|
| Parameters | 8B | 3.8B |
| Architecture | Dense Transformer | Dense Transformer |
| Context Length | 8K | 128K |
| VRAM (FP16) | 16 GB | 7.6 GB |
| VRAM (INT4) | 6.5 GB | 3.2 GB |
| Licence | Meta Community | MIT |
Microsoft trained Phi-3 on heavily filtered, high-quality data including textbook-style code examples. Fewer parameters, but each one works harder. The 128K context window also means Phi-3 can see entire codebases in a single pass, which helps with cross-file completions. Details in the LLaMA VRAM guide and Phi-3 VRAM guide.
Cost Analysis
| Cost Factor | LLaMA 3 8B | Phi-3 Mini |
|---|---|---|
| GPU Required (INT4) | RTX 3090 (24 GB) | RTX 3090 (24 GB) |
| VRAM Used | 6.5 GB | 3.2 GB |
| Est. Monthly Server Cost | £95 | £95 |
| Throughput Advantage | 15% faster | 1% cheaper/tok |
Identical server costs. Phi-3’s tiny VRAM footprint means it could run on a much cheaper GPU — even an RTX 4060 Ti with 16 GB — bringing monthly costs down further. Model your options at the cost calculator. More hardware guidance at best GPU for inference.
It Depends on the Integration
Phi-3 Mini for single-suggestion IDE plugins. When a developer presses tab and expects one correct completion, the 3.2-point accuracy advantage matters. The lower latency of LLaMA matters less than getting the answer right. MIT licensing keeps things clean for commercial IDE products. See the comparison hub for more.
LLaMA 3 8B for bulk code generation. When you are generating test suites, migrating codebases, or running batch refactoring where you generate many candidates and select the best, LLaMA’s higher throughput processes more code per hour. Setup walkthrough in the self-host guide.
See also: LLaMA 3 vs Phi-3 for Chatbots | LLaMA 3 vs DeepSeek for Code Generation
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