Can Falcon 40B Instruct run on MacBook Pro M4 Pro 64GB?

YES — Tight Fit

B69Good

○Estimated from fit model

Falcon 40B Instruct needs ~38.7 GB VRAM. MacBook Pro M4 Pro 64GB has 46.1 GB. With Q5_K_M quantization, expect ~15 tok/s.

Runtime: OllamaCapacity: TightBandwidth: LowStack: BasicBottleneck: Balanced

Operating mode

Choose the run profile you care about

Interactive favors responsiveness, while light API and scale-out lean harder on serving readiness. The fit stays the same, but the recommendation lens changes.

Current mode

Balanced

Balanced for general local use. Keeps the ranking neutral across personal and serving workflows.

Capabilities:

Select quantization to explore

Q5_K_M (High quality) — 38.7 GB, 14.6 tok/s, Tight fit

38.7 GB required46.1 GB available

84% VRAM used

Fit status

Tight fit

Decode

14.6 tok/s

TTFT

13284 ms

Safe context

Memory

38.7 GB / 46.1 GB

Memory breakdown

Weights28.8 GB

KV Cache1.8 GB

Runtime1.2 GB

Headroom6.9 GB

See how fast it feels

See how fast it feelsFalcon 40B Instruct on MacBook Pro M4 Pro 64GB
1st promptCold start — includes initialization
>Solve: If a train travels at 60 mph for 2.5 hours, how far does it go?
Let me work through this step by step.

Given information:
 - Speed = 60 mph (miles per hour)
 - Time = 2.5 hours

Formula: Distance = Speed x Time

Calculation:
 Distance = 60 mph x 2.5 h
 = 150 miles

The train travels 150 miles.

Double-check: at 60 mph the train covers 60 miles each hour. In half an hour it covers 30 miles. So in 2.5 hours: 60 + 60 + 30 = 150 miles. Confirmed.
2nd promptWarm — model ready, no init overhead
>What if the train stops for 20 minutes halfway?
The stop doesn't change the total distance covered while moving, but it affects total trip time.

Breakdown:
 - First half: 1.25 hours at 60 mph = 75 miles
 - Stop: 20 minutes (no distance)
 - Second half: 1.25 hours at 60 mph = 75 miles

Total distance: 150 miles (same)
Total time: 2.5 h + 0.33 h = 2.83 hours
Average speed: 150 / 2.83 = 53 mph

The distance stays the same but average speed drops to 53 mph because of the stop.
Estimated: 14.6 tok/s decode · 13.3s TTFT (warm) · 36 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

Shared-memory contention still exists

The OS, browser, and inference runtime all compete for the same physical memory pool, so real-world headroom is less forgiving than raw capacity suggests.

Best improvement path

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	B	Tight fit	14.6 tok/s	7246 ms	8K
Coding	B	Tight fit	14.6 tok/s	13284 ms	8K
Agentic Coding	B	Tight fit	14.6 tok/s	19323 ms	8K
Reasoning	B	Tight fit	14.6 tok/s	15700 ms	8K
RAG	B	Tight fit	14.6 tok/s	24153 ms	8K

Quantization options

How Falcon 40B Instruct (40B params) fits at each quantization level on MacBook Pro M4 Pro 64GB (46.1 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	15.6 GB	Low	B66
Q3_K_S	3	19.6 GB	Low	B67
NVFP4	4	22.4 GB	Medium	B68
Q4_K_M	4	24.4 GB	Medium	B69
Q5_K_M	5	28.8 GB	High	B69
Q6_KBest for your GPU	6	32.8 GB	High	B68
Q8_0	8	42.8 GB	Very High	F0
F16	16	82.0 GB	Maximum	F0

Get started

Copy-paste commands to run Falcon 40B Instruct on your machine.

Run

docker run --rm -it ghcr.io/ggerganov/llama.cpp:full \
 --hf-repo "tiiuae/falcon-40b-instruct" \
 --hf-file "falcon-40b-instruct-Q5_K_M.gguf" \
 -c 4096 -ngl 99

Upgrade options