The direct answer
Yes, you can run Llama 3 on 8GB — but only the 8B parameter version at Q4 quantization. Llama 3.1 8B at Q4_K_M uses approximately 5.5 GB of VRAM with 4K context, leaving 2.5 GB for your system. This runs on any 8 GB GPU (RTX 3070, RTX 4060, RTX 2080) and on Apple Silicon Macs with 8 GB unified memory.
No, you cannot run Llama 3.3 70B on 8GB. The 70B model at minimum quantization (Q2) needs ~18 GB. At Q4 it needs ~35 GB. Even with aggressive CPU offloading, generation speed drops to <1 tok/s, which is unusable.
Here is the full breakdown of what fits and what does not.
What fits on 8GB?
These models run comfortably on 8 GB VRAM or unified memory at the specified quantization levels:
| Model | Quantization | VRAM usage | Context | tok/s (GPU) |
|---|---|---|---|---|
| Llama 3.1 8B | Q4_K_M | ~5.5 GB | 4K | ~20-25 |
| Llama 3.2 3B | Q4_K_M | ~2.5 GB | 8K | ~30-40 |
| Llama 3.2 1B | Q4_K_M | ~1 GB | 8K | ~40-60 |
| Qwen 2.5 7B | Q4_K_M | ~5 GB | 8K | ~20-28 |
| Mistral 7B | Q4_K_M | ~4.5 GB | 8K | ~25-30 |
| Phi-3 3.8B | Q4_K_M | ~2.8 GB | 4K | ~30-40 |
Notice that Llama 3.1 8B at Q4 is the largest Llama model you can fit on 8 GB. The smaller Llama 3.2 models (1B and 3B) are faster but less capable.
What does NOT fit on 8GB?
| Model | Minimum VRAM | Why it does not fit |
|---|---|---|
| Llama 3.3 70B Q4 | ~35 GB | Weights alone are 35 GB + KV cache. Needs 48 GB total. |
| Llama 3.3 70B Q2 | ~18 GB | Even the most aggressive quantization does not fit on 8 GB. |
| Qwen 2.5 32B Q4 | ~18 GB | ~18 GB weights + context. Needs 24 GB GPU. |
| Mixtral 8x7B Q4 | ~20 GB | ~20 GB weights + context. Needs 24 GB GPU. |
| Llama 3.1 8B Q8 | ~8.5 GB | Only if context is minimal (1K). At 4K context it hits ~10 GB. |
The Llama 3.3 70B family requires 48 GB+ of VRAM — out of reach for any single consumer GPU under $2000. You need dual RTX 3090s (48 GB total) or a Mac with 64 GB+ unified memory.
Performance on 8GB
Your experience depends on whether you are using a GPU, Apple Silicon, or CPU-only:
| Hardware | Model | tok/s | Usability |
|---|---|---|---|
| RTX 3070 8 GB | Llama 3.1 8B Q4 | ~20-25 | Great — fast enough for interactive chat |
| RTX 4060 8 GB | Llama 3.1 8B Q4 | ~18-22 | Great — slightly slower than 3070 |
| M1 MacBook Air 8 GB | Llama 3.1 8B Q4 | ~10-15 | Good — usable, fans stay off for short sessions |
| CPU-only 8 GB RAM | Llama 3.1 8B Q4 | ~2-5 | Slow — usable for batch processing, not chat |
| CPU-only 8 GB RAM | Llama 3.2 3B Q4 | ~5-8 | OK — slow but usable for simple tasks |
The threshold for "usable" interactive chat is ~10 tok/s. Below that, the delay between pressing Enter and seeing the response feels sluggish. Above 20 tok/s, text appears smoothly as if you are reading a fast typist.
How to set up on 8GB hardware
Follow these steps to run Llama 3.1 8B on your 8 GB machine:
Step 1: Check your VRAM
First, confirm you have enough free VRAM. Close other GPU-using applications (browser with hardware acceleration, design tools, games).
# NVIDIA GPUs nvidia-smi --query-gpu=memory.free --format=csv,noheader # Apple Silicon (unified memory) memory_pressure # General (system RAM) free -h
Step 2: Install Ollama
curl -fsSL https://ollama.com/install.sh | sh
Step 3: Run Llama 3.1 8B
# Default (Q4, 8K context) ollama run llama3.1:8b # If you hit memory limits, reduce context: ollama run llama3.1:8b --context-size 4096 # For the tiny Llama 3.2 3B (faster, less capable): ollama run llama3.2:3b
Ollama automatically selects the best quantized version for your hardware. For llama.cpp, use:
./main -m llama-3.1-8b-q4_k_m.gguf -n 512 -ngl 99 -c 4096
The -ngl 99 flag offloads all layers to GPU. If you run out of memory, lower -ngl to offload fewer layers (e.g., -ngl 20 offloads ~80% to GPU, rest to CPU).
Quantized vs full quality comparison
How much quality do you lose by running Llama 3 at Q4 instead of FP16?
| Quantization | Size vs FP16 | Quality retention | Use case |
|---|---|---|---|
| FP16 (full) | 100% | 100% (baseline) | Reference, benchmarking |
| Q8 | 50% | ~99.5% | Maximum quality, plenty of VRAM |
| Q6 | 37.5% | ~98.5% | Good balance for larger models |
| Q4 | 25% | ~95-97% | Standard for consumer GPUs |
| Q3 | 18.75% | ~90-93% | Fitting very large models |
| Q2 | 12.5% | ~80-85% | Compressed models, quality loss visible |
In blind A/B tests, most users cannot consistently distinguish Q4 from FP16 on chat, summarisation, and Q&A tasks. The difference is noticeable on sensitive tasks like translation of idioms, creative writing nuance, and exact mathematical reasoning. For 95% of everyday use, Q4 is indistinguishable from full precision.
Experiment with our quantization calculator to compare quality vs VRAM trade-offs for any model.
How to check your hardware
Before downloading models, verify your hardware specs:
- GPU VRAM: Run
nvidia-smi(NVIDIA) or check About This Mac → System Report → Graphics/Displays (Apple Silicon). - System RAM: Run
free -h(Linux), check Activity Monitor (Mac), or Task Manager (Windows). - GPU compute capability: Ensure your GPU supports CUDA (NVIDIA cards from GTX 900 series and up work).
- Available disk space: GGUF model files are 3-8 GB. Ensure you have at least 20 GB free for a few models.
Use our VRAM calculator to check exactly which model + quantization combinations fit before you download.
Alternatives to running on 8GB hardware
If 8 GB is too tight for your needs, you have options:
- Upgrade your GPU: A used RTX 3060 12 GB costs ~$200 and doubles your VRAM. An RTX 3090 24 GB is ~$700 used.
- Use CPU offloading: llama.cpp can split layers between GPU and system RAM. Expect ~5-10 tok/s with 50% offloading.
- Use a cloud API: If you need Llama 3 70B or GPT-4-class models, skip the hardware entirely. Plugsky gives you 30+ models including all Llama versions with no hardware requirements.
Cannot fit Llama 70B on your hardware?
Plugsky gives you 30+ models including Llama 3.3 70B, Qwen 2.5 72B, and more. No hardware, no setup, no rate limits.
Start Free → VRAM calculatorFrequently asked questions
Can I run Llama 3 on 8GB RAM?
Yes. Llama 3.1 8B at Q4 quantization uses ~5.5GB of VRAM with 4K context. It runs comfortably on any 8GB GPU (RTX 3070, RTX 4060, etc.) and on Apple Silicon with 8GB unified memory at ~10-15 tok/s.
Can I run Llama 3 70B on 8GB?
No. Llama 3.3 70B at minimum quantization (Q2) still needs ~18GB VRAM. At Q4 it needs ~35GB. The 70B model is completely out of reach for 8GB hardware. You need 48GB+ (dual GPU or Mac with 64GB+).
How fast will Llama 3 run on 8GB?
On an 8GB GPU (e.g., RTX 3070): ~20-25 tok/s for Llama 3.1 8B Q4. On 8GB Apple Silicon (e.g., M1 Air): ~10-15 tok/s. On 8GB system RAM with CPU only: ~2-5 tok/s. Only GPU inference provides usable speeds.
Is quantized Llama 3 as good as the full version?
Q4 quantization retains ~95-97% of the original model quality. In blind tests, most users cannot tell the difference between Q4 and FP16 for chat and summarisation. Q8 retains ~99.5%. The quality loss is minimal and well worth the 4x memory savings.
How do I run Llama 3 on 8GB hardware?
Install Ollama (curl -fsSL https://ollama.com/install.sh | sh), then run 'ollama run llama3.1:8b'. Ollama automatically downloads the optimal Q4 quantized version. For more control, use llama.cpp with the --ngl flag to offload layers to GPU.