How to Run Llama 5 Locally

Meta released Llama 5 on April 8, 2026. Here’s how to run it on your own hardware.

Last verified: April 10, 2026

Pick Your Variant First

Llama 5 ships in several sizes:

Variant	Parameters	Min VRAM (Q4)	Min VRAM (FP16)
Llama 5 8B	8B dense	6 GB	16 GB
Llama 5 70B	70B dense	40 GB	140 GB
Llama 5 Scout	109B MoE (17B active)	60 GB	220 GB
Llama 5 Maverick	400B MoE (40B active)	220 GB	800 GB
Llama 5 Behemoth	600B+ MoE flagship	380 GB+	1.2 TB+

Most users want Llama 5 70B or Scout. They’re the sweet spot between quality and local feasibility.

Option 1: Ollama (Easiest)

# Install Ollama
curl -fsSL https://ollama.com/install.sh | sh

# Pull Llama 5 70B quantized to Q4
ollama pull llama5:70b-instruct-q4_K_M

# Run it
ollama run llama5:70b-instruct-q4_K_M

# Or serve as API
ollama serve

Ollama added Llama 5 variants within hours of the Meta announcement. The 8B variant runs on a MacBook Pro or a single RTX 4090. The 70B variant needs a beefy rig (M4 Max 128GB or 2x RTX 4090).

Option 2: LM Studio (GUI)

1. Download LM Studio from lmstudio.ai
2. In the search tab, filter for “llama-5”
3. Pick a GGUF variant (Q4_K_M is a good default)
4. Download and hit “Load Model”
5. Start chatting or enable the OpenAI-compatible local server

LM Studio is the fastest path for Windows and Mac users who want a full chat UI without the terminal.

Option 3: vLLM (Production-Grade)

For multi-GPU serving with batching:

pip install vllm

# Serve Llama 5 70B on 2x H100 with tensor parallelism
vllm serve meta-llama/Llama-5-70B-Instruct \
  --tensor-parallel-size 2 \
  --max-model-len 128000 \
  --dtype bfloat16

Then call it via the OpenAI-compatible API at http://localhost:8000/v1.

Option 4: MLX for Apple Silicon

Native Apple Silicon inference:

pip install mlx-lm

mlx_lm.generate \
  --model mlx-community/Llama-5-70B-Instruct-4bit \
  --prompt "Explain recursion"

MLX is optimized for M-series chips and often beats Ollama on throughput for larger models on Macs.

Hardware Recommendations

Budget	Setup	Best Variant
$0 extra	MacBook Pro M3/M4	Llama 5 8B Q4
$2,000	RTX 4090 workstation	Llama 5 8B FP16 or 70B Q4
$6,000	M4 Max 128GB	Llama 5 70B Q4-Q8
$20,000	2x H100 80GB	Llama 5 70B FP16 or Scout
$100,000+	8x H100 / 4x B200	Llama 5 Maverick
$250,000+	16x H100 / 8x B200	Llama 5 Behemoth (full)

Context Window

Llama 5 supports up to 5 million tokens of context, but using the full window needs huge VRAM for the KV cache. Most local deployments should cap context at 32K–128K tokens unless you specifically need long-context workloads.

Common Pitfalls

• Out of memory on first run? Reduce --max-model-len and try a lower quantization (Q3 or Q2)
• Slow on Mac? Use MLX instead of Ollama for large models
• License acceptance? You must accept Meta’s community license on Hugging Face before downloading weights
• Missing tokenizer? Use the Llama 5 tokenizer from Meta’s HF repo, not the Llama 4 one

Should You Self-Host?

Run Llama 5 locally if:

• ✅ You have sensitive data that can’t leave your infrastructure
• ✅ You have steady high-volume workloads where API costs exceed hardware costs
• ✅ You want to fine-tune on domain data
• ✅ You need air-gapped deployment

Use a hosted API (Together, Fireworks, Groq, Bedrock) if:

• ❌ Volume is low or bursty
• ❌ You don’t have ops capacity
• ❌ You need the flagship 600B variant without buying a cluster

Last verified: April 10, 2026