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Models / Llama 3.1 8B
Llama 3.1 8B VRAM Requirements & GPU Pairing
Llama 3.1 8B is the de facto baseline of local inference: 8.03B dense parameters, 32 layers, GQA with 8 KV heads, and a native 128K context. Its ecosystem coverage is unmatched — nearly every GGUF quant repo, inference framework, and fine-tuning tutorial treats it as the first-class citizen; our own measured bpw calibration table is derived from bartowski's Llama 3.1 8B GGUF file sizes. Q4_K_M @8K totals ~7.5GB: comfortable on 12GB cards, and 16GB cards can go straight to Q8_0.
Theoretical speed at Q4 is ~154 tok/s on an RTX 4090, ~143 on a 3090, and ~42 on a Mac mini M4 Pro — all in the zero-perceived-latency range. It is the default answer for general chat, writing, RAG, and light coding, though Qwen3 8B has overtaken it in Chinese and mathematical reasoning. Pick Qwen3 for quality-first use, Llama for ecosystem maturity and tutorial abundance. License is llama3.1-community — check the 700M MAU clause for commercial use.
Architecture Specs
| Total parameters | 8.03B |
| Active parameters | 8.03B |
| Layers | 32 |
| KV heads | 8 |
| Head dim | 128 |
| Native context | 128K |
| License | llama3.1-community |
| KV bytes per token (fp16) | 128.0 KB |
VRAM by Quant Tier (@8K context, fp16 KV)
| Quant | Weights | Total (incl. 1.5GB runtime overhead) |
|---|---|---|
| Q8_0 | 8.5 GB | 11.1 GB |
| Q6_K | 6.6 GB | 9.2 GB |
| Q4_K_M | 4.9 GB | 7.5 GB |
| Q3_K_M | 4.0 GB | 6.6 GB |
| Q2_K | 3.2 GB | 5.8 GB |
KV cache and runtime overhead do not change across quants; longer contexts grow the KV part linearly — adjust context length in the GPU checker tool.
GPU Verdict Matrix (Q4_K_M @8K)
| GPU | Usable VRAM | Verdict | Recommended quant | Theoretical speed | |
|---|---|---|---|---|---|
| RTX 3060 12GB | 12.0 GB | Comfortable | Q8_0 | ≈32 tok/s | Try it → |
| RTX 3090 | 24.0 GB | Comfortable | FP16 | ≈44 tok/s | Try it → |
| RTX 4070 Ti Super | 16.0 GB | Comfortable | Q8_0 | ≈59 tok/s | Try it → |
| RTX 4090 | 24.0 GB | Comfortable | FP16 | ≈47 tok/s | Try it → |
| RTX 5090 | 32.0 GB | Comfortable | FP16 | ≈84 tok/s | Try it → |
| RTX A6000 | 48.0 GB | Comfortable | FP16 | ≈36 tok/s | Try it → |
| A100 80GB | 80.0 GB | Comfortable | FP16 | ≈95 tok/s | Try it → |
| H100 80GB | 80.0 GB | Comfortable | FP16 | ≈156 tok/s | Try it → |
| RX 7900 XTX | 24.0 GB | Comfortable | FP16 | ≈45 tok/s | Try it → |
| Mac mini M4 Pro (48GB) | 36.0 GB | Comfortable | FP16 | ≈13 tok/s | Try it → |
| Mac Studio M4 Max (64GB) | 48.0 GB | Comfortable | FP16 | ≈25 tok/s | Try it → |
| Mac Studio M3 Ultra (96GB) | 72.0 GB | Comfortable | FP16 | ≈38 tok/s | Try it → |
Theoretical speed = bandwidth × 0.75 ÷ per-token weight bytes (active params for MoE); real-world results vary with framework/driver/CPU, ±30%.
FAQ
- How much VRAM does Llama 3.1 8B need?
- Q4_K_M @8K is ~7.5GB (4.9GB weights + 1.1GB KV + 1.5GB overhead) — comfortable on 12GB. Q8_0 is ~11.1GB (needs a 16GB-class card); FP16 is ~18.6GB (needs 24GB).
- Is Llama 3.1 8B fast on an RTX 3060 12GB?
- Yes — comfortable verdict, theoretically ~55 tok/s at Q4_K_M (360 GB/s × 0.75 ÷ 4.9GB). Chat feels fluid, though long-context prefill is noticeably slower than on high-bandwidth cards.
- Llama 3.1 8B or Qwen3 8B?
- Pick Qwen3 8B for Chinese, math, and code (plus the freer Apache-2.0 license). Pick Llama 3.1 8B for the 128K native context, English tutorial ecosystem, and the widest choice of ready-made quants.
Related guides
- Quantization Explained: Q4 vs Q8 and What You Actually Lose
- Run GGUF Models Locally: llama.cpp and Ollama Walkthrough
- Best GPU for Local LLMs in 2026: Every Budget Tier
Try Llama 3.1 8B in the GPU compatibility checker →
Data verified 2026-09-01