Can I run Llama 3.1 8B on an RTX 4080 Super 16GB?
Yes: Llama 3.1 8B needs about 9.88 GB at Q4_K_M with 32K tokens of context, which fits the RTX 4080 Super 16GB with 6.12 GB to spare. At 32K the RTX 4080 Super holds up to Q8_0 (13.6 GB), and Q4_K_M runs up to 72K tokens.
Yes Q4_K_M with 32K tokens of context
- Q4_K_M, 32K
- 9.88 GB
- RTX 4080 Super
- 16 GB, 736 GB/s
- To spare
- 6.12 GB
- Tokens/s
- 41–59 tokens/s
At Q4_K_M with 32K tokens of context it writes about 41–59 tokens/s for one request on an RTX 4080 Super 16GB.
Best precision for Llama 3.1 8B on an RTX 4080 Super 16GB
The most precise setting that leaves at least 0.5 GB free; one that fits with less is marked tight.
| Context | Best fit | Memory | Free | Tokens/s |
|---|---|---|---|---|
| 8K | Q8_0 | 10.3 GB | 5.66 GB | 40–56 |
| 32K | Q8_0 | 13.6 GB | 2.36 GB | 30–42 |
| 128K (full) | Nothing fits | — | — | — |
Llama 3.1 8B on the RTX 4080 Super as the context fills
One request, FP16 KV cache, 0.5 GB plus 10% overhead; a minus sign is memory missing, and tight is less than 0.5 GB free.
| Context | Q4_K_M | Free | Tokens/s | Q8_0 | Free | Tokens/s |
|---|---|---|---|---|---|---|
| 4K | 6.03 GB | 9.97 GB | 67–97 | 9.79 GB | 6.21 GB | 42–59 |
| 8K | 6.58 GB | 9.42 GB | 62–89 | 10.3 GB | 5.66 GB | 40–56 |
| 16K | 7.68 GB | 8.32 GB | 53–76 | 11.4 GB | 4.56 GB | 36–50 |
| 32K | 9.88 GB | 6.12 GB | 41–59 | 13.6 GB | 2.36 GB | 30–42 |
| 64K | 14.3 GB | 1.72 GB | 29–40 | 18.0 GB | −2.04 GB | — |
| 128K (full) | 23.1 GB | −7.08 GB | — | 26.8 GB | −10.8 GB | — |
Llama 3.1 8B on more than one RTX 4080 Super
| Cards | Best at 32K | Tokens/s | Q4_K_M longest | Q4_K_M tokens/s |
|---|---|---|---|---|
| 2× (32 GB) | BF16 | 35–51 | 128K (full) | 68–104 |
| 4× (64 GB) | BF16 | 62–95 | 128K (full) | 109–184 |
Tensor parallel with the combined bandwidth, as in vLLM; llama.cpp splits layers by default and runs at about one card's speed.
Other options
- The next smaller setting, Q3_K_M, takes 8.92 GB at 32K, 7.08 GB under the RTX 4080 Super; it fits with 0.5 GB to spare up to 79K tokens.
- Smallest setup for Q4_K_M at 32K: RTX 3060 12GB.
Run Llama 3.1 8B on the RTX 4080 Super with llama-server
llama-server -hf bartowski/Meta-Llama-3.1-8B-Instruct-GGUF:Q4_K_M -c 73728Meta-Llama-3.1-8B-Instruct-Q4_K_M.gguf, 4.9 GB, from bartowski/
Questions
Can I run Llama 3.1 8B on an RTX 4080 Super 16GB?
Yes: Llama 3.1 8B needs about 9.88 GB at Q4_K_M with 32K tokens of context, which fits the RTX 4080 Super 16GB with 6.12 GB to spare. At 32K the RTX 4080 Super holds up to Q8_0 (13.6 GB), and Q4_K_M runs up to 72K tokens.
How fast is Llama 3.1 8B on an RTX 4080 Super 16GB?
At Q4_K_M with 32K tokens of context it writes about 41–59 tokens/s for one request on an RTX 4080 Super 16GB.
What does a second RTX 4080 Super 16GB change for Llama 3.1 8B?
Two RTX 4080 Super 16GB cards (32 GB in one tensor-parallel group) hold Llama 3.1 8B at BF16 with 32K, and Q4_K_M up to 128K (full) tokens, at about 68–104 tokens/s.
Try other settings in the VRAM calculator or the speed calculator. See also Llama 3.1 8B VRAM requirements, what LLMs an RTX 4080 Super 16GB can run and every pair, or detect your own GPU. Model data checked .