Can the RTX 4090 Run Llama 3.1 8B?
Runs
Yes. Llama 3.1 8B at Q4_K_M is a 4.9 GB download (Hugging Face file listing for bartowski/Meta-Llama-3.1-8B-Instruct-GGUF), and the RTX 4090 has 24 GB of VRAM, leaving about 19.1 GB for the KV cache and runtime. Published runs put generation at a median of 126 tokens per second across 6 cited measurements. Fastest cited run: llama.cpp GitHub.
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Published runs for Llama 3.1 8B on the RTX 4090
| Generation | Quant | Runtime | Context | Source | Date |
|---|---|---|---|---|---|
| 165 tok/s | Q4_K_M | llama.cpp | — | llama.cpp GitHublabelled "Llama 3.1 8B" | 2024-08-22 |
| 131 tok/s | Q4_K_XL | llama.cpp | 4K | Hardware Cornerlabelled "llama3.1:8b" | 2025-11-06 |
| 128 tok/s | q4_K_M | llama.cpp | 1K | XiongjieDai/GPU-Benchmarks-on-LLM-Inference (GitHub)labelled "llama3.1:8b" | 2024-05-01 |
| 125 tok/s | q4_K_M | llama.cpp | 4K | MyAIHardwarelabelled "llama3.1:8b" | 2025-05-01 |
| 113 tok/s | q4_K_M | llama.cpp | 8K | Awesome Agents LLM Leaderboardlabelled "llama3.1:8b" | 2025-06-01 |
| 95.5 tok/s | Q4_K_M | ollama | — | DatabaseMartlabelled "llama3.1:8b" | 2025-01-01 |
More about this pair
How much VRAM does Llama 3.1 8B need?
The Q4_K_M file is 4.9 GB. Add roughly 1.5 GB for the runtime and an ~8K-token context, so about 6.4 GB of VRAM holds it comfortably. Longer contexts grow the KV cache and need more.
What is the fastest Llama 3.1 8B can generate on the RTX 4090?
Generation reads every weight once per token, so the RTX 4090's 1008 GB/s of memory bandwidth divided by the 4.9 GB file sets a ceiling near 205 tokens per second. Real runs land below it; SpecPicks rejects any published figure above it.
Check another pair
Llama 3.1 8B on other GPUs
- RTX 4060 — Runs, 41.7 tok/s median
- RTX 3060 Ti — Runs, 57.3 tok/s median
- RTX 3060 12GB — Runs, 53.6 tok/s median
- Intel Arc B580 — Runs, 40 tok/s median
- RTX 4070 SUPER — Runs, 53.4 tok/s median
- RTX 4060 Ti 16GB — Runs, 48.2 tok/s median
- RTX 5060 Ti 16GB — Runs, 59.2 tok/s median
- Intel Arc A770 16GB — Runs, 38.7 tok/s median
- Radeon RX 9070 XT — Runs
- RTX 4070 Ti SUPER — Runs, 53.9 tok/s median
- RTX 4080 — Runs, 101 tok/s median
- RTX 5070 Ti — Runs, 95.3 tok/s median
- RTX 5080 — Runs, 88.5 tok/s median
- RTX 3090 — Runs, 92 tok/s median
- Radeon RX 7900 XTX — Runs, 48.3 tok/s median
- RTX 5090 — Runs, 150 tok/s median
- RTX A6000 48GB — Runs
- RTX PRO 6000 Blackwell 96GB — Runs, 138 tok/s median
Other models on the RTX 4090
- Llama 3.2 1B — Runs
- Llama 3.2 3B — Runs
- Mistral 7B — Runs
- Qwen2.5 7B — Runs
- DeepSeek-R1-Distill-Llama 8B — Runs
- Qwen3 8B — Runs
- Gemma 2 9B — Runs
- Gemma 3 12B — Runs
- Qwen2.5 14B — Runs, 63.9 tok/s median
- DeepSeek-R1-Distill-Qwen 14B — Runs, 59 tok/s median
- Qwen3 14B — Runs, 82.8 tok/s median
- Phi-4 14B — Runs, 68.8 tok/s median
- gpt-oss-20b — Runs
- Gemma 3 27B — Runs
- Gemma 2 27B — Runs, 38 tok/s median
- Qwen3 30B-A3B — Runs, 196 tok/s median
- Qwen3 32B — Runs, 38 tok/s median
- Qwen2.5 32B — Runs, 34.4 tok/s median
- DeepSeek-R1-Distill-Qwen 32B — Runs, 34.2 tok/s median
- Llama 3.1 70B — Offload
- Llama 3.3 70B — Offload
- DeepSeek-R1-Distill-Llama 70B — Offload
How the checks work
The fit is the Q4_K_M file size against the card's VRAM, with 1.5 GB kept for the runtime and an ~8K context. A published run counts only if the pair fits, the run is Q4-class, its source does not call it an estimate, it is under the bandwidth ceiling above, and it is not one figure pasted across many cards. The whole matrix is downloadable as a CSV under CC BY 4.0.
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This page is editorial synthesis based on publicly available information. No independent first-party benchmarking is reported.