Can You Run Llama 3.1 8B? VRAM Requirements
Llama 3.1 8B has 8B total parameters. Running it at Q4_K_M (GGUF) with 16K context takes roughly 8.0 GB of memory (4.5 GB weights + 2.0 GB KV cache + runtime overhead). The minimum viable hardware is about RTX 3060 12GB. All numbers are planning estimates, verified 2026-07-27.
VRAM by quantization (16K context)
| Quantization | Weights | Total needed | Minimum hardware |
|---|---|---|---|
| FP16 / BF16 | 14.9 GB | 18.4 GB | RTX 3090 / 4090 24GB |
| FP8 | 7.5 GB | 11.0 GB | RTX 3060 12GB |
| Q8_0 (GGUF) | 7.9 GB | 11.4 GB | RTX 4060 Ti 16GB |
| Q6_K (GGUF) | 6.1 GB | 9.6 GB | RTX 3060 12GB |
| Q5_K_M (GGUF) | 5.3 GB | 8.8 GB | RTX 3060 12GB |
| MXFP4 | 3.9 GB | 7.4 GB | RTX 3060 12GB |
| Q4_K_M (GGUF)recommended | 4.5 GB | 8.0 GB | RTX 3060 12GB |
| Q3_K_M (GGUF) | 3.5 GB | 7.0 GB | RTX 3060 12GB |
| Q2_K (GGUF) | 2.6 GB | 6.1 GB | RTX 3060 12GB |
How context length changes memory (Q4_K_M (GGUF))
| Context | KV cache | Total needed |
|---|---|---|
| 4K | 0.5 GB | 6.5 GB |
| 16K | 2.0 GB | 8.0 GB |
| 64K | 8.0 GB | 14.0 GB |
| 128K | 16.0 GB | 22.0 GB |
KV cache grows linearly with context; FP8 KV cache halves it again (try it in the calculator below).
Customize the estimate
LLM VRAM Calculator — Can I Run It?
Estimate how much GPU memory (VRAM) you need to run open-weight LLMs like Kimi K3, DeepSeek R1, Qwen3, or Llama locally. Pick a model, quantization, and context length — the calculator adds up weights, KV cache, and runtime overhead, then shows which GPUs or Macs can fit it. Runs entirely in your browser: no upload, no signup.
8B total params · max 128K context
Recommended balance · ~0.61 bytes/param
Estimated memory needed
22.0 GB
Will it fit?
Assumes ~92% of device memory is usable. Multi-GPU counts are for tensor/pipeline parallel serving (vLLM, SGLang); Apple Silicon uses unified memory via llama.cpp or MLX.
FAQ
How much VRAM does Llama 3.1 8B need?
About 8.0 GB at the recommended Q4_K_M (GGUF) quantization with 16K context: 4.5 GB for weights, 2.0 GB for KV cache, plus runtime overhead. Longer context grows the KV cache.
What is the minimum hardware for Llama 3.1 8B?
Roughly RTX 3060 12GB, assuming 92% of device memory is usable. Lower-end setups can try more aggressive quantization (Q3/Q2) at a noticeable quality cost.
Can I run Llama 3.1 8B on a Mac?
Yes. At Q4_K_M (GGUF) with 16K context it needs about 8.0 GB, which fits the unified memory of a Mac M3 Ultra 512GB via llama.cpp or MLX.
Which quantization should I use for Llama 3.1 8B?
Q4_K_M is the recommended balance of size and quality; with headroom, Q6_K or Q8_0 reduce quality loss further.