Best GPU for Llama 3.1 8B Instruct
The smallest memory budget in our catalog that runs Llama 3.1 8B Instruct cleanly at 8,192 tokens is 22 GiB net, with BF16 (16.6 GiB–18.5 GiB total). Cards at that budget: GeForce RTX 3090 24GB, GeForce RTX 4090 24GB, Radeon RX 7900 XTX 24GB. The largest file, BF16, is comfortable from 30 GiB net: GeForce RTX 5090 32GB.
Which card, by net memory
| Net budget | Devices | Best file that runs | Total at 8,192 | Result | Same file at 32,768 |
|---|---|---|---|---|---|
| 18 GiB | Radeon RX 7900 XT 20GB | BF16 | 16.6 GiB–18.5 GiB | Tight | No |
| 22 GiB | GeForce RTX 3090 24GB, GeForce RTX 4090 24GB, Radeon RX 7900 XTX 24GB | BF16 | 16.6 GiB–18.5 GiB | Fits | Tight |
| 30 GiB | GeForce RTX 5090 32GB | BF16 | 16.6 GiB–18.5 GiB | Comfortable | Fits |
Rows start at the first budget where the smallest official file is at least tight and stop at the first where the largest file is comfortable. Devices in one row have the same net budget (capacity minus the catalog's default reserve), so they get the same verdict; choose between them on bandwidth, software support and price, which this page does not rank. llama.cpp, one sequence, fp16 KV cache, typical extras. Unified-memory devices use the reserve in their catalog record. "n/a" in the last column means the model's native window is shorter than 32,768 tokens.
Files this ranking uses
| File | Weights | Source repository |
|---|---|---|
| BF16 | 14.96 GiB (16,060,556,376 bytes) | meta-llama/Llama-3.1-8B-Instruct |
Byte counts come from the repository file listings recorded on the model page. Memory estimates are formulas, not measurements on these cards. No speed figure is given: none was measured here.
Data sources
- Llama 3.1 8B Instruct model card (Meta) — Context length 128k, GQA, Llama 3.1 Community License. Parameters as printed on the card.
- Meta llama-models sku_list.py (architecture arguments) — arch_args dim=4096, n_layers=32, n_heads=32, n_kv_heads=8. head_dim is dim ÷ n_heads = 128 (Meta's reference model derives it that way; sku_list.py has no head_dim key). The Hugging Face config.json is gated (HTTP 401), so it was not read.
- Hugging Face API — sha 0e9e39f249a16976918f6564b8830bc894c89659; gated manual; safetensors.total=8030261248; 4 safetensors siblings.
- Method and reserves — catalog dataset 2026-10-02.1; hardware capacities from vendor spec pages listed on each device page