HomeCan I run it?Llama 3.1 8BLlama 3.1 8B on an RTX 3070 Ti
Yes. Llama 3.1 8B at Q4_K_M needs about 6.6 GB, and the GeForce RTX 3070 Ti has 8.0 GB usable at a ~4k context.
Can I run Llama 3.1 8B on an RTX 3070 Ti?
Llama 3.1 8B at Q4_K_M needs about 6.6 GB, and the GeForce RTX 3070 Ti has 8.0 GB usable. That figure is 8 GB of VRAM on the spec sheet, of which 8.0 GB is usable for weights and cache, at 608 GB/s memory bandwidth. Meta’s widely used 8B instruct model, the default small local chat model for a few years running. These are estimates from our formula, not measured benchmarks.
- Quant that fits
- Q4_K_M Fits with headroom
- Memory needed
- 6.6 GB at Q4_K_M, ~4k context
- Usable VRAM
- 8.0 GB of 8 GB VRAM
- Est. tokens/sec
- 75–106 estimate
Memory at each quant
| Quant | Need vs 8.0 GB | Verdict |
|---|---|---|
| Q4_K_M | 6.6 GB | Q4Fits |
| Q8_0 | 10.8 GB | Q8Won’t fit |
| FP16 | 19.5 GB | FP16Won’t fit |
Estimated tokens per second
Estimate: 75–106 tokens/sec at Q4_K_M. This is a bandwidth-derived range, not a bench. Efficiency is a 60–85% GPU band from How we calculate.
Context headroom
At Q4_K_M, this machine has headroom out to about 8k tokens under our KV estimate. Longer chats need more memory for the cache. Figures assume an FP16 KV cache.
| Context | KV cache (FP16) | Total at Q4_K_M | Verdict |
|---|---|---|---|
| 4k | 0.5 GB | 6.6 GB | Fits |
| 8k | 1.1 GB | 7.1 GB | Fits |
| 16k | 2.1 GB | 8.2 GB | Won’t fit |
| 32k | 4.3 GB | 10.4 GB | Won’t fit |
| 64k | 8.6 GB | 14.7 GB | Won’t fit |
| 128k | 17.2 GB | 23.2 GB | Won’t fit |
What to do instead
- Q8_0 needs 10.8 GB and does not fit here. The GeForce RTX 2080 Ti has 11.0 GB usable, enough for Q8.
- See the best GPU for this model family and the Llama-3.1-8B-Instruct model page.
Other hardware for Llama 3.1 8B
- Llama 3.1 8B on an RTX 3060 12GBTight
- Llama 3.1 8B on an RTX 3080Fits
- Llama 3.1 8B on an RTX 4060 Ti 16GBFits
- Llama 3.1 8B on an RTX 4070 Ti SuperFits
- Llama 3.1 8B on an RTX 4090Fits
- Llama 3.1 8B on an RTX 5070 TiFits
Other models on an RTX 3070 Ti
- Qwen3-32B on an RTX 3070 TiWon’t fit
- Qwen3-30B-A3B on an RTX 3070 TiWon’t fit
- Qwen3.8-27B on an RTX 3070 TiWon’t fit
- Gemma 3 27B on an RTX 3070 TiWon’t fit
- Gemma 4 31B on an RTX 3070 TiWon’t fit
- gpt-oss-20b on an RTX 3070 TiWon’t fit
Quick answers
- Can I run Llama 3.1 8B on an RTX 3070 Ti?
- Yes. Llama 3.1 8B at Q4_K_M needs about 6.6 GB, and the GeForce RTX 3070 Ti has 8.0 GB usable.
- How much VRAM does Llama 3.1 8B need?
- About 6.6 GB at Q4_K_M, 10.8 GB at Q8_0 and 19.5 GB at FP16, estimated for a ~4k context.
- How fast is Llama 3.1 8B on an RTX 3070 Ti?
- Our estimate is 75–106 tokens per second at Q4_K_M. It is not a measured benchmark.
- What context length can I use?
- At Q4_K_M, our KV estimate supports up to about 8k tokens before the cache pushes the total over usable memory.