RTX 3060 12GB (32GB RAM) vs 8GB VRAM class (RTX 4060 8GB, 32GB RAM) for local LLMs
Memory fit, speed and context compared with the same math as the advisor. RTX 3060 12GB (32GB RAM) has 4.0 GB more usable memory (11.0 vs 7.0 GB) and lower bandwidth than the other (360 vs 272 GB/s).
Verdict from the numbers
- Fit. RTX 3060 12GB (32GB RAM) fits 4 of 13 models fully at 16k context (largest: Gemma 4 12B Q6_K); 8GB VRAM class (RTX 4060 8GB, 32GB RAM) fits 2 (largest: Qwen3.5 9B Q4_K_M). Only RTX 3060 12GB (32GB RAM) fits fully: Gemma 4 12B, Qwen3-VL 8B.
- Speed. On the 2 models that fit fully on both, RTX 3060 12GB (32GB RAM) is faster by a typical 1.3x. RTX 3060 12GB (32GB RAM) is ahead on 2, 8GB VRAM class (RTX 4060 8GB, 32GB RAM) on 0. No model is measured on both machines, so every comparison involves an estimate; read it as a guide.
- Trade-off. RTX 3060 12GB (32GB RAM) has 4.0 GB more usable memory (11.0 vs 7.0 GB) and lower bandwidth than the other (360 vs 272 GB/s).
Where to check prices
Price tier note
We do not list prices because they move daily and differ between new and used stock. What the data does show is what the memory difference buys: RTX 3060 12GB (32GB RAM) has 4.0 GB more usable memory, which moves 2 of 13 models into full-GPU fit (Gemma 4 12B, Qwen3-VL 8B). Use the Amazon links above to compare current listings and decide whether the difference is worth it for the models you want to run.
Model by model · 16k context, f16 KV cache
| Model | RTX 3060 12GB (32GB RAM) fit | tok/s | 8GB VRAM class (RTX 4060 8GB, 32GB RAM) fit | tok/s | Faster / fits |
|---|---|---|---|---|---|
| Gemma 4 26B-A4B UD-Q4_K_M | MoE offload to CPU | 27 estimated | MoE offload to CPU | 29 estimated | - |
| Gemma 4 31B Q4_K_M | partial offload (slow) | 3 estimated | partial offload (slow) | 4 estimated | - |
| Gemma 4 12B Q6_K | fits fully | 29 scaled from measured | partial offload (slow) | 21 estimated | RTX 3060 12GB (32GB RAM) (fits) |
| Qwen3.6 27B Q4_K_M | partial offload (slow) | 4 estimated | partial offload (slow) | 4 estimated | - |
| Qwen3.6 35B-A3B UD-Q4_K_M | MoE offload to CPU | 28 estimated | MoE offload to CPU | 36 estimated | - |
| Qwen3.5 9B Q8_0 | fits fully | 37 measured | fits fully | 33 scaled from measured | RTX 3060 12GB (32GB RAM) |
| Qwen3.5 4B Q8_0 | fits fully | 63 measured | fits fully | 42 scaled from measured | RTX 3060 12GB (32GB RAM) |
| gpt-oss 20B Q4_K_M | MoE offload to CPU | 77 estimated | MoE offload to CPU | 44 estimated | - |
| gpt-oss 120B | does not fit | - | does not fit | - | - |
| Qwen3-Coder 30B-A3B Q4_K_M | MoE offload to CPU | 28 estimated | MoE offload to CPU | 34 estimated | - |
| Mistral Small 3.2 24B Q4_K_M | partial offload (slow) | 5 estimated | partial offload (slow) | 5 estimated | - |
| Qwen3-VL 8B Q6_K | fits fully | 42 scaled from measured | partial offload (slow) | 31 estimated | RTX 3060 12GB (32GB RAM) (fits) |
| GLM-4.5-Air | does not fit | - | does not fit | - | - |
Badges: measured medians of real llama.cpp runs; scaled from measured and estimated come from our bandwidth model. See sources and methods.
How much context fits?
Largest context where weights plus KV cache stay inside usable memory, per model that fits fully on at least one side. q8_0 KV roughly halves the cache.
| Model | RTX 3060 12GB (32GB RAM) f16 | 8GB VRAM class (RTX 4060 8GB, 32GB RAM) f16 | RTX 3060 12GB (32GB RAM) q8_0 | 8GB VRAM class (RTX 4060 8GB, 32GB RAM) q8_0 |
|---|---|---|---|---|
| Gemma 4 12B | 32k | - | 64k | - |
| Qwen3.5 9B | 32k | 32k | 64k | 64k |
| Qwen3-VL 8B | 16k | - | 32k | - |
| Qwen3.5 4B | 128k | 64k | 256k | 128k |
Spec summary
| RTX 3060 12GB (32GB RAM) | 8GB VRAM class (RTX 4060 8GB, 32GB RAM) | |
|---|---|---|
| Usable memory for model + KV cache | 11.0 GB | 7.0 GB |
| Memory bandwidth | 360 GB/s | 272 GB/s |
| Models that fit fully at 16k | 4 of 13 | 2 of 13 |
| Models that need offload or do not fit | 9 | 11 |
| Measured data points on file | 11 | 2 |
FAQ
Which is faster for local LLMs, RTX 3060 12GB (32GB RAM) or 8GB VRAM class (RTX 4060 8GB, 32GB RAM)?
On the 2 models that fit fully on both, RTX 3060 12GB (32GB RAM) is typically 1.3x faster (geometric mean). RTX 3060 12GB (32GB RAM) wins 2 and 8GB VRAM class (RTX 4060 8GB, 32GB RAM) wins 0. 0 comparisons are measured on both.
Which fits more models, RTX 3060 12GB (32GB RAM) or 8GB VRAM class (RTX 4060 8GB, 32GB RAM)?
RTX 3060 12GB (32GB RAM) fits 4 of 13 models fully at 16k context and 8GB VRAM class (RTX 4060 8GB, 32GB RAM) fits 2. Only RTX 3060 12GB (32GB RAM): Gemma 4 12B, Qwen3-VL 8B.
Can I run long context on RTX 3060 12GB (32GB RAM) or 8GB VRAM class (RTX 4060 8GB, 32GB RAM)?
At f16 KV cache, the largest-model context limits are in the table above (for example Gemma 4 12B on RTX 3060 12GB (32GB RAM): 32k; Qwen3.5 9B on 8GB VRAM class (RTX 4060 8GB, 32GB RAM): 32k). A q8_0 KV cache extends it further.
Do you state prices for RTX 3060 12GB (32GB RAM) or 8GB VRAM class (RTX 4060 8GB, 32GB RAM)?
No. Prices change daily, so we link to Amazon search results instead and focus on measured and estimated speed and memory fit.
Related
RTX 3060 12GB (32GB RAM) · 8GB VRAM class (RTX 4060 8GB, 32GB RAM) · Measured model pages: Qwen3.5 9B
By memory: 8 GB · 12 GB · 16 GB · 24 GB · 32 GB · 48 GB · 64 GB · 96 GB · 128 GB · 192 GB · 256 GB · 512 GB