Llama 3 8B excels in instruction following and generating structured outputs like JSON. Compared to models like Gemma, it delivers more accurate and reliable responses. Its performance and alignment make it ideal for real-world, prompt-driven applications.

LLaMa vs Gemma

If our prompts are working well with LLaMA but not with Gemma, despite being relatively concise and structured, the issue is likely due to differences in training data, instruction tuning, and architectural behavior.

Let’s break down why and how to adapt your approach:

🔍 Why LLaMA Works Better than Gemma

Factor LLaMA 3 Gemma (2B/7B)
Instruction tuning Heavily tuned for instructions & format Lighter instruction tuning
Reasoning capability Strong general reasoning Simpler chain-of-thought behavior
Training data Broad and diverse (code, instructions) Cleaner but narrower dataset
System prompt expectations Works well without explicit structure Often benefits from explicit task framing
Parsing robustness Good with structured JSON Tends to miss format expectations

LLaMa 3.1 8B

The official meta‑llama/Llama‑3.1‑8B‑Instruct available on Hugging Face is not quantized by default—it’s the full-precision model (~FP16/FP32). However, the community has provided alternative quantized versions (e.g., ZeroWw’s GGUF quantizations) that you can download separately (huggingface.co).

From the discussions on that model page, here are the typical GPU memory requirements:

Use Case GPU Memory Required
Run 4-bit quantized version ~6 GB VRAM (huggingface.co)
Fine-tune in 4-bit ≥ 15 GB VRAM
Run full model (FP16/FP32) ≥ 16 GB VRAM

✅ Summary

  • Is the HF model quantized? No—the default is full precision. You’d need to manually choose a community quantized version.

  • Needed GPU memory:

    • ~6 GB if you use a 4-bit quantized variant

    • = 16 GB for the full model

    • = 15 GB if you want to fine-tune in 4-bit

✅ What Distillation Typically Gives You

Benefit Applies to Your Case?
Smaller size Yes — if you’re tight on resources
Faster inference Yes — especially with quantized models
Retained general capability Partially — but lower than full LLaMA 3 8B
Lower cost to deploy Yes — helpful if hosting models

But distillation compresses general knowledge and ability, often at the cost of structured precision, format consistency, and long-context handling — exactly the traits that matter for JSON-to-JSON pipelines.

❌ What You Likely Lose with Distilled Models

Loss Area Impact for JSON Tasks
Precision in output format High impact (more format drift)
Structured reasoning depth Medium to high
Long context fidelity High (many distilled models only support 4k–32k)
Consistency on edge cases High — distilled models are less reliable under complex input rules

🟨 Exception: Distilled Models Tuned Specifically for Structure

If you found a distilled model that was:

  • Fine-tuned specifically for structured data I/O
  • Supports 128k context natively
  • Has validated consistency in JSON output

Then yes — it might outperform a base LLaMA 3 on your task.

✅ Conclusion:

Stick with LLaMA 3 8B — distilled LLaMA variants won’t likely add value for your structured JSON workflow.

LLama 3.1 8B on Customer Grade GPUs

Here’s what people are reporting for LLaMA 3’s 8B model performance on RTX 3090 and RTX 4090:

🧠 RTX 4090

Redditors and benchmarkers consistently note strong results:

  • A Reddit user summarized testing performance on a 4090 (likely with vLLM or TGI backend), though they didn’t share exact numbers (reddit.com, vast.ai).
  • The community benchmark repository by XiongjieDai reports around 127.7 tokens/sec (quantized Q4_K_M) and 54.3 tokens/sec (FP16) on LLaMA 3.8B using llama.cpp (github.com).
  • A vLLM performance test shows total throughput ~7000 tokens/sec (sum of input + output) for “Llama-8B” on 4090 (databasemart.com).

To sum it up:

  • ~125 t/s (Q4 quant)
  • ~50–55 t/s (FP16)

🚀 RTX 3090

Benchmarks include:

  • From XiongjieDai’s tests:

  • A YouTuber using LM Studio with a 3090 reported ~60 t/s on LLaMA 3 8B (youtube.com).
  • A Reddit user noted ~99 t/s on LLaMA 3 8B-Q4 variant with no power limit, dropping slightly to 91 t/s at reduced power (reddit.com).

So typical numbers:

  • ∼60–112 t/s (Q4 quant)
  • ∼46 t/s (FP16)

📊 Comparison Table

GPU Quantized Q4 (K/M) FP16
RTX 4090 ~127 t/s ~54 t/s
RTX 3090 ~112 t/s (99–60 t/s range) ~46 t/s
  • 5090–4090 difference: ~10–20 % faster in quantized mode, similar PF16 gains.
  • Best performance: RTX 4090 slightly ahead, but both are solid for LLaMA 3.
  • Quantization is key to fit 8B on 16–24GB cards and unlock speed advantages.

🔧 Format & Backend Notes

  • Most benchmarks use Q4_K_M quantization via llama.cpp, delivering ~110–130 t/s.
  • FP16 (unquantized) tops out around 46–55 t/s, but may OOM on 16GB cards.
  • Throughput depends on backend (e.g., llama.cpp vs. vLLM, TGI, llm.cpp).

✅ TL;DR

  • RTX 4090: ~⁣127 t/s (Q4), ~54 t/s (FP16)
  • RTX 3090: ~⁣112 t/s (Q4), ~46 t/s (FP16)
  • Quantized Q4 is highly recommended — it offers the best speed and VRAM efficiency for LLaMA 3 8B.