HomeCan I run it?Llama 3.1 8BLlama 3.1 8B on an RTX 3080
Yes. Llama 3.1 8B at Q4_K_M needs about 6.6 GB, and the GeForce RTX 3080 10GB has 10.0 GB usable at a ~4k context.
Can I run Llama 3.1 8B on an RTX 3080?
Llama 3.1 8B at Q4_K_M needs about 6.6 GB, and the GeForce RTX 3080 10GB has 10.0 GB usable. That figure is 10 GB of VRAM on the spec sheet, of which 10.0 GB is usable for weights and cache, at 760 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
- 10.0 GB of 10 GB VRAM
- Est. tokens/sec
- 94–133 estimate
Memory at each quant
| Quant | Need vs 10.0 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: 94–133 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 16k 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 | Fits |
| 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 3070 TiFits
- 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 3080
- Qwen3-32B on an RTX 3080Won’t fit
- Qwen3-30B-A3B on an RTX 3080Won’t fit
- Qwen3.8-27B on an RTX 3080Won’t fit
- Gemma 3 27B on an RTX 3080Won’t fit
- Gemma 4 31B on an RTX 3080Won’t fit
- gpt-oss-20b on an RTX 3080Won’t fit
Quick answers
- Can I run Llama 3.1 8B on an RTX 3080?
- Yes. Llama 3.1 8B at Q4_K_M needs about 6.6 GB, and the GeForce RTX 3080 10GB has 10.0 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 3080?
- Our estimate is 94–133 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 16k tokens before the cache pushes the total over usable memory.