Gemma 3 vs Llama 3 — Which Runs Better on Your Hardware?
Head-to-head comparison of Google's Gemma 3 and Meta's Llama 3 for local inference. Compare VRAM requirements, performance, and quality at each size tier to pick the right model for your GPU.
Gemma 3 and Llama 3 are the two model families that dominate local AI. Meta’s Llama 3.1 set the standard for open-weight models in 2024. Google’s Gemma 3, released in March 2025, answered with smaller parameter counts, built-in vision, and aggressive VRAM efficiency. If you are picking a model to run on your own GPU, these are the two lineups to compare.
This post breaks down the matchup at each size tier so you can pick the right model for your hardware.
Small Tier: Gemma 3 4B vs Llama 3.1 8B
This is where the comparison gets interesting. Gemma 3 4B is half the parameter count of Llama 3.1 8B, yet competitive on many tasks.
| Gemma 3 4B (Q4) | Llama 3.1 8B (Q4) | |
|---|---|---|
| VRAM | 5.0 GB | 6.3 GB |
| File size | 3.3 GB | 4.1 GB |
| Context | 128K | 128K |
| Vision | Yes | No |
| MMLU | 62.0 | 73.0 |
Llama 3.1 8B is the stronger model on benchmarks — 73.0 vs 62.0 MMLU is a real gap. You will notice it in reasoning-heavy tasks, code generation, and complex instructions. But Gemma 3 4B uses 1.3 GB less VRAM and includes vision support out of the box. If you want to ask questions about images or screenshots, Gemma 3 4B does that; Llama 3.1 8B does not.
On an 8 GB GPU, both models fit at Q4. Llama 3.1 8B is the better choice for pure text work. Gemma 3 4B is the pick if you want multimodal capabilities or need that extra VRAM headroom for longer context.
Medium Tier: Gemma 3 12B vs Llama 3.1 8B at Q8
At the medium tier, Gemma 3 12B at Q4 (10.5 GB VRAM) competes against Llama 3.1 8B at Q8 (10.0 GB VRAM). Both need roughly the same VRAM, but you get a very different trade-off.
| Gemma 3 12B (Q4) | Llama 3.1 8B (Q8) | |
|---|---|---|
| VRAM | 10.5 GB | 10.0 GB |
| File size | 8.1 GB | 7.2 GB |
| Vision | Yes | No |
| MMLU | 76.0 | 73.0 |
Gemma 3 12B at Q4 wins on raw capability — more parameters, higher benchmarks, and vision support. Llama 3.1 8B at Q8 gives you less quantization loss, which means more faithful outputs from a well-understood model with a massive ecosystem of fine-tunes.
If you have a 12 GB GPU like the RTX 3060 12GB, Gemma 3 12B is the stronger choice. If you are already invested in Llama fine-tunes or tooling, bumping Llama 3.1 8B to Q8 is a meaningful quality upgrade within the same VRAM budget.
Large Tier: Gemma 3 27B vs Llama 3.1 70B
Here the VRAM gap is massive.
| Gemma 3 27B (Q4) | Llama 3.1 70B (Q4) | |
|---|---|---|
| VRAM | 20.0 GB | 43.5 GB |
| File size | 17.0 GB | 34.9 GB |
| Vision | Yes | No |
| MMLU | 78.5 | 83.6 |
Llama 3.1 70B scores higher on benchmarks, but it needs more than double the VRAM. You need a 48 GB card or multi-GPU setup to run it. Gemma 3 27B fits on a single RTX 3090 or RTX 4090 with room to spare.
For most people with 24 GB GPUs, Gemma 3 27B is the practical ceiling. It delivers strong reasoning, coding, creative writing, and vision — all from one card. Llama 3.1 70B is only worth the hardware cost if you specifically need that last few points of benchmark performance and have the VRAM to back it up.
What About Older GPUs?
If you are running a GTX 1070 or RTX 2070 Super, you have 8 GB of VRAM. Here is what works:
GTX 1070 (8 GB GDDR5, Pascal): Both Gemma 3 4B at Q4 (5.0 GB) and Llama 3.1 8B at Q4 (6.3 GB) fit. Llama 3.1 8B will leave less headroom, and the GTX 1070’s lower memory bandwidth (256 GB/s) means slower token generation with the larger model. Gemma 3 4B is the safer bet — it loads faster, leaves more room for context, and gives you vision as a bonus.
RTX 2070 Super (8 GB GDDR6, Turing): Same VRAM, but faster memory bandwidth (448 GB/s) and tensor cores help. Llama 3.1 8B at Q4 runs noticeably faster here than on the GTX 1070. Both models are comfortable fits. Go with Llama 3.1 8B for better text quality, or Gemma 3 4B if you want vision support.
Neither card can run Gemma 3 12B (10.5 GB at Q4) or Llama 3.1 8B at Q8 (10.0 GB) — both exceed the 8 GB limit. If you want to move beyond 7B-class models, you will need a GPU upgrade.
Use our compatibility checker to see exactly what fits your hardware.
The Verdict
There is no single winner. The right choice depends on your hardware and what you need:
- 8 GB GPU, text only: Llama 3.1 8B at Q4 for the best quality
- 8 GB GPU, want vision: Gemma 3 4B at Q4
- 12-16 GB GPU: Gemma 3 12B at Q4 — more capable and multimodal
- 24 GB GPU: Gemma 3 27B at Q4 — flagship quality from one card
- 48 GB+ GPU: Llama 3.1 70B at Q4 if you want the highest benchmarks
Both families run through Ollama with one command. Try them both and decide based on your own use case — benchmarks only tell part of the story.