text model · Falcon · macOS

Can I run Falcon-H1-34B-Instruct on Apple M5 Pro (48GB)?

Compatibility verdict VRAM threshold engine

Yes, it runs usable speed ~13 tok/s est.

Yes. Falcon-H1-34B-Instruct runs on Apple M5 Pro (48GB) at Q4_K_M (~21.1 GB of ~32 GB usable).

Needs ~21.1 GB Device usable ~32 GB

Runs at Q4_K_M using ~21.1 GB of ~32 GB usable.

That figure is at a 4k context and moves about ±15% as context length changes. Apple M5 Pro (48GB) leaves ~10.9 GB of headroom.

Q4_K_M needed: ~21.1 GB
Usable on device: ~32 GB
Device memory: 48 GB
Best quant: Q4_K_M

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Which quant fits

Quant ladder vs ~32 GB usable

Q2_K

~16.4 GB

Q3_K_M

~18.8 GB

Q4_K_M

~21.1 GB

Q5_K_M

~26.4 GB

Q6_K

~30.1 GB

Q8_0

~35.5 GB

FP16

~69.5 GB

The line marks Apple M5 Pro (48GB)'s ~32 GB budget; rungs past it are too large.

Run it

Install commands macOS

Pick your tool. All 2 load the same Q4_K_M weights.

llama.cpp

$ llama-cli -hf tiiuae/Falcon-H1-34B-Instruct-GGUF:Q4_K_M

LM Studio

$ lms get tiiuae/Falcon-H1-34B-Instruct-GGUF

How to run it

On macOS use LM Studio (Polished GUI, ships MLX on Apple Silicon, one-click model downloads.).

Model Falcon

Parameters: 34B
Q4_K_M size: 18.94 GB
Q8_0 size: 33.31 GB
Context: 256k

Full Falcon-H1-34B-Instruct requirements →

Device macOS

Memory: 48 GB unified
Usable for weights: ~32 GB
Best runtime: MLX direct / Ollama (MLX backend)

Best models for Apple M5 Pro (48GB) →

You could also run

Run Falcon-H1-34B-Instruct on other hardware

Nvidia GeForce RTX 4090 (24GB)Nvidia GeForce RTX 3090 (24GB)AMD Radeon RX 7900 XTX (24GB)Nvidia GeForce RTX 5090 (32GB)32GB RAM Laptop (CPU/iGPU only)Apple M4 Pro (48GB)

FAQ

Can Apple M5 Pro (48GB) run Falcon-H1-34B-Instruct?

Yes. Falcon-H1-34B-Instruct runs on Apple M5 Pro (48GB) at Q4_K_M (~21.1 GB of ~32 GB usable).

How much memory does Falcon-H1-34B-Instruct need?

Apple M5 Pro (48GB) has room to spare. At Q4_K_M the weights are ~18.94 GB; with KV cache and runtime overhead, budget ~21.1 GB at a 4k context.

What is the best tool to run Falcon-H1-34B-Instruct on macOS?

LM Studio for a simple setup; mlx-lm for the most speed. vLLM is NOT a Mac tool, it is a CUDA/Linux serving engine. Unified memory is not a fixed VRAM slice; ~70% is usable for weights.

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Markdown

[![Falcon-H1-34B-Instruct on Apple M5 Pro (48GB)](https://localmodel.run/badge/falcon-h1-34b/apple-m5-pro-48gb.svg)](https://localmodel.run/can-i-run/falcon-h1-34b/apple-m5-pro-48gb)

Sources

Weights are measured from GGUF files; KV cache and overhead are computed, so totals can vary ~15% with context and runtime. Any tok/s is a bandwidth estimate. See methodology.