Gemma · Dense · Gemma
Can I run Gemma 3 4B locally?
Small and multimodal — it can read images. A good edge pick when you need vision, not just text.
- Parameters
- 4.3B
- VRAM at Q4
- 4.3 GB
- Max context
- 131K
- Released
- 2025-03
Memory needed by quantisation
Total includes weights, an 8K-token KV cache and runtime overhead. Lower quants trade quality for size.
| Quant | Bits/weight | Weights | Total needed | Quality |
|---|---|---|---|---|
| FP16 | 16 | 8.0 GB | 10 GB | Full precision. Reference quality, 2× the size of 8-bit. |
| Q8_0 | 8.5 | 4.3 GB | 6.2 GB | Effectively lossless. The safe choice when it fits. |
| Q6_K | 6.56 | 3.3 GB | 5.2 GB | Near-lossless; quality loss is hard to measure. |
| Q5_K_M | 5.67 | 2.8 GB | 4.8 GB | Very good. A common sweet spot above Q4. |
| Q4_K_M | 4.83 | 2.4 GB | 4.3 GB | The default. Best size/quality trade-off for local use. |
| Q3_K_M | 3.91 | 2.0 GB | 3.8 GB | Noticeable degradation; useful to squeeze a size up. |
| Q2_K | 3.35 | 1.7 GB | 3.6 GB | Aggressive. Quality drops a lot — last resort to fit. |
Which hardware runs Gemma 3 4B?
Best quantisation that fits each device at 8K context, with a rough speed estimate. Try your exact setup →
| Hardware | Memory | Fit | Best quant | Speed |
|---|---|---|---|---|
| RTX 3060 12 GB | 12 GB | Runs well | FP16 | ~30 tok/s |
| RTX 4060 Ti 8 GB | 8 GB | Runs well | Q8 | ~45 tok/s |
| RTX 4060 Ti 16 GB | 16 GB | Runs well | FP16 | ~24 tok/s |
| RTX 3080 10 GB | 10 GB | Runs well | Q8 | ~120 tok/s |
| RTX 5070 | 12 GB | Runs well | FP16 | ~56 tok/s |
| RTX 4070 Super | 12 GB | Runs well | FP16 | ~42 tok/s |
| Radeon RX 7900 XT | 20 GB | Runs well | FP16 | ~67 tok/s |
| RTX 5070 Ti | 16 GB | Runs well | FP16 | ~75 tok/s |
| RTX 4070 Ti Super | 16 GB | Runs well | FP16 | ~56 tok/s |
| RTX 3090 | 24 GB | Runs well | FP16 | ~78 tok/s |
| Radeon RX 7900 XTX | 24 GB | Runs well | FP16 | ~80 tok/s |
| RTX 5080 | 16 GB | Runs well | FP16 | ~80 tok/s |
| RTX 4080 Super | 16 GB | Runs well | FP16 | ~62 tok/s |
| RTX 4090 | 24 GB | Runs well | FP16 | ~84 tok/s |
| RTX 5090 | 32 GB | Runs well | FP16 | ~150 tok/s |
| RTX A6000 | 48 GB | Runs well | FP16 | ~64 tok/s |
| RTX 6000 Ada | 48 GB | Runs well | FP16 | ~80 tok/s |
| A100 80 GB | 80 GB | Runs well | FP16 | ~171 tok/s |
| H100 80 GB | 80 GB | Runs well | FP16 | ~280 tok/s |
| Mac · M1/M2/M3 (base), 8 GB | 8 GB | Runs well | Q6 | ~20 tok/s |
| Mac · M1/M2/M3 (base), 16 GB | 16 GB | Runs well | FP16 | ~8.4 tok/s |
| Mac · M4 (base), 24 GB | 24 GB | Runs well | FP16 | ~10 tok/s |
| Mac · M4 Pro, 48 GB | 48 GB | Runs well | FP16 | ~23 tok/s |
| Mac · M1/M2/M3 Max, 32 GB | 32 GB | Runs well | FP16 | ~33 tok/s |
| Mac · M4 Max, 64 GB | 64 GB | Runs well | FP16 | ~46 tok/s |
| Mac · M1/M2/M3 Max, 64 GB | 64 GB | Runs well | FP16 | ~33 tok/s |
| Mac · M3/M4 Max, 128 GB | 128 GB | Runs well | FP16 | ~46 tok/s |
| Mac Studio · M1/M2 Ultra, 128 GB | 128 GB | Runs well | FP16 | ~67 tok/s |
| Mac Studio · M3 Ultra, 256 GB | 256 GB | Runs well | FP16 | ~69 tok/s |
| Mac Studio · M3 Ultra, 512 GB | 512 GB | Runs well | FP16 | ~69 tok/s |
| CPU only · 8 GB RAM | 8 GB | Runs well | Q5 | ~12 tok/s |
| CPU only · 16 GB RAM | 16 GB | Runs well | FP16 | ~5.0 tok/s |
| CPU only · 32 GB RAM | 32 GB | Runs well | FP16 | ~5.9 tok/s |
| CPU only · 64 GB RAM | 64 GB | Runs well | FP16 | ~6.7 tok/s |
| CPU only · 128 GB RAM | 128 GB | Runs well | FP16 | ~7.5 tok/s |
GPUs that run Gemma 3 4B well
The most affordable cards in our list that run it at a good quantisation.
RTX 3060 12 GB
12 GB · ~$279
Check price →RTX 4060 Ti 8 GB
8 GB · ~$379
Check price →RTX 4060 Ti 16 GB
16 GB · ~$449
Check price →Hardware links are affiliate links — they don't change the recommendation.
What Gemma 3 4B is good for
Related models
FAQ
How much VRAM does Gemma 3 4B need?
At Q4_K_M, Gemma 3 4B needs about 4.3 GB including a 8K-token context and overhead (2.4 GB for the weights alone). Higher quantisation needs more; see the table for every level.
What is the cheapest way to run Gemma 3 4B?
The smallest device that runs it well in our list is the RTX 4060 Ti 8 GB (8 GB). Anything with at least that much memory should handle it at a usable quantisation.
Is Gemma 3 4B good for low-end & edge hardware?
Small and multimodal — it can read images. A good edge pick when you need vision, not just text.
Estimates — see how we compute these. Memory figures assume an 8K context; long-context use needs more.