You need 6-8GB of VRAM to run Llama 3 8B comfortably. At Q4_K_M quantization, the model weights consume about 4.5GB, and the KV cache adds 1-3GB depending on context length. An 8GB GPU runs it, but a 12-16GB card gives you breathing room for longer conversations.
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You want to run Llama 3 8B locally and need to know exactly how much VRAM it requires before buying a GPU or checking if your current card can handle it. This guide gives you precise numbers at every quantization level.
VRAM breakdown by quantization
| Quantization | Model Size | KV Cache (4K ctx) | KV Cache (8K ctx) | Total VRAM |
|---|---|---|---|---|
| Q2_K | ~3.0GB | ~0.5GB | ~1.0GB | 3.5-4.0GB |
| Q4_K_M | ~4.5GB | ~0.5GB | ~1.0GB | 5.0-5.5GB |
| Q5_K_M | ~5.2GB | ~0.5GB | ~1.0GB | 5.7-6.2GB |
| Q6_K | ~6.0GB | ~0.5GB | ~1.0GB | 6.5-7.0GB |
| Q8_0 | ~8.5GB | ~0.5GB | ~1.0GB | 9.0-9.5GB |
| FP16 | ~16.0GB | ~0.5GB | ~1.0GB | 16.5-17.0GB |
The KV cache grows linearly with context length. At 8K context (Llama 3’s default), plan for roughly 1GB of overhead beyond the model weights. At 32K extended context, that overhead can hit 4GB.
VRAM capacity memory bandwidth Specs are manufacturer figures. Bar lengths are scaled independently per metric.
Which GPU for each quantization level
| GPU | VRAM | Best Quantization | Speed (Q4_K_M) | Price |
|---|---|---|---|---|
| RTX 3060 12GB | 12GB | Q8_0 (room to spare) | ~25 tok/s | ~$250 |
| RTX 4060 Ti 16GB | 16GB | FP16 possible | ~35 tok/s | ~$425 |
| RTX 4070 Ti Super | 16GB | FP16 possible | ~40 tok/s | ~$800 |
| RTX 5080 | 16GB | FP16 possible | ~55 tok/s | ~$1,400 |
| RTX 4090 | 24GB | FP16 + long context | ~65 tok/s | ~$2,200 |
| RTX 5090 | 32GB | FP16 + 32K context | ~95 tok/s | ~$4,900 |
For Llama 3 8B specifically, the RTX 4060 Ti 16GB is overkill on VRAM but that extra space lets you run at higher quantization and longer context without worry.
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Most VRAM guides only count model weights. Context length changes everything:
- 4K context: Adds ~0.5GB to VRAM. Manageable on any card that fits the model.
- 8K context: Adds ~1GB. Still fine on 12GB+ cards at Q4.
- 16K context: Adds ~2GB. An 8GB card running Q4 will hit the wall here.
- 32K context: Adds ~4GB. You need 12GB minimum even at Q4_K_M.
If you use Ollama’s default settings, context is typically 4K-8K. If you modify num_ctx for longer conversations, budget extra VRAM accordingly.
Which GPU should you buy?
If you already own an 8GB GPU, Llama 3 8B runs at Q4_K_M with short context windows. Usable but tight. If you are buying specifically for Llama 3 8B, the RTX 4060 Ti 16GB ($425) is the best match — it runs the model at Q8 quality with room for 16K+ context. If you want headroom for future models while still getting great Llama 3 8B performance, the RTX 4090 ($2,200) gives you 24GB for when you inevitably want to try larger models.
Common mistakes to avoid
- Buying an 8GB card in 2026 for Llama 3 8B. It technically fits, but you are one context length increase away from OOM. Spend the extra money for 12GB minimum.
- Running FP16 when Q4_K_M is sufficient. For chat and general tasks, Q4_K_M quality is nearly indistinguishable from FP16. Save the VRAM for context length instead.
- Ignoring Ollama’s memory overhead. Ollama itself consumes 200-500MB of VRAM for the CUDA context. Add this to your calculations when running on tight VRAM budgets.
- Planning VRAM based on model size alone. The 4.5GB Q4 model needs 6-8GB in practice once you add context, Ollama overhead, and system GPU usage.
Our recommendation
Llama 3 8B is one of the easiest models to run locally. At Q4_K_M, a $250 used RTX 3060 12GB handles it comfortably. At Q8 or FP16, step up to the RTX 4060 Ti 16GB at $425. Do not overspend on a flagship GPU just for this model — save that budget for when you want to run larger models.
Check NVIDIA GeForce RTX 3060 12GB on Amazon→Buy on Shopee SG→ Check NVIDIA GeForce RTX 4060 Ti 16GB on Amazon→Buy on Shopee SG→ Check NVIDIA GeForce RTX 4090 on Amazon→Buy on Shopee SG→The model weights are only half the VRAM equation. Context length, KV cache, and runtime overhead fill the other half.
For the full Llama 3 family including 70B and 405B, see our best GPU for Llama 3 guide. For VRAM planning across all models, check our Ollama VRAM guide.