Will It Run AI

Can Llama 4 Maverick 17B 128E run on Mac Studio M3 Ultra 256GB?

YES — With Q2_K

A82Great
Estimated from fit model

Llama 4 Maverick 17B 128E needs ~187.5 GB VRAM. Mac Studio M3 Ultra 256GB has 184.3 GB. With Q2_K quantization, expect ~15 tok/s.

Runtime: llama.cppCapacity: OffloadBandwidth: HighStack: StandardBottleneck: Balanced
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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.

Llama 4 Maverick 17B 128E at Q4_K_M needs 275.5 GB — too much for Mac Studio M3 Ultra 256GB (184.3 GB). Runs at Q2_K (187.5 GB) with low quality.
Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 275.5 GB, exceeds 184.3 GB available
275.5 GB required184.3 GB available
149% VRAM needed

91.2 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

6.7 tok/s

TTFT

28822 ms

Safe context

4K

Memory

275.5 GB / 184.3 GB

Offload

30%

Memory breakdown

Weights244.0 GB
KV Cache2.9 GB
Runtime0.9 GB
Headroom27.6 GB

See how fast it feels

With memory offload — actual speed may be lower
See how fast it feelsLlama 4 Maverick 17B 128E on Mac Studio M3 Ultra 256GB
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: 6.7 tok/s decode · 28.8s TTFT (warm) · 17 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

Very little memory headroom

You can run the model, but there is not much room left for longer context, bigger batches, extra apps, or future model updates.

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

Buy headroom, not only minimum fit

A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.

Performance by workload

WorkloadGradeFitDecodeTTFTContext
ChatFToo heavy6.8 tok/s15626 ms4K
CodingFToo heavy6.7 tok/s28822 ms4K
Agentic CodingFToo heavy6.6 tok/s42430 ms4K
ReasoningFToo heavy6.7 tok/s34062 ms4K
RAGFToo heavy6.6 tok/s53037 ms4K

Quantization options

How Llama 4 Maverick 17B 128E (400B params) fits at each quantization level on Mac Studio M3 Ultra 256GB (184.3 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
156.0 GB
LowF0
Q3_K_S
3
196.0 GB
LowF0
NVFP4
4
224.0 GB
MediumF0
Q4_K_M
4
244.0 GB
MediumF0
Q5_K_M
5
288.0 GB
HighF0
Q6_K
6
328.0 GB
HighF0
Q8_0
8
428.0 GB
Very HighF0
F16
16
820.0 GB
MaximumF0

Get started

Copy-paste commands to run Llama 4 Maverick 17B 128E on your machine.

Run

lms load Llama-4-Maverick-17B-128E-Instruct && lms server start

Upgrade-Optionen

Hardware, die Llama 4 Maverick 17B 128E gut ausführt

AMD Instinct MI350X 288GBGünstige Wahl
288 GB VRAM (+32)8000 GB/s (+7181)
S
Makes the model fit on the accelerator instead of staying completely out of reach.77.4 tok/s Dekodierung

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

ca. $8,000 MSRP

AMD Instinct MI325X 256GBBestes Preis-Leistungs-Verhältnis
6000 GB/s (+5181)
A
Makes the model fit on the accelerator instead of staying completely out of reach.37.9 tok/s Dekodierung

Makes the model fit on the accelerator instead of staying completely out of reach.

Raises estimated decode speed by about 466%.

ca. $20,000 MSRP

Frequently asked questions

Can Mac Studio M3 Ultra 256GB run Llama 4 Maverick 17B 128E?

Yes, Mac Studio M3 Ultra 256GB can run Llama 4 Maverick 17B 128E at Q2_K quantization (Runs with offload (needs ~2.6 GB host RAM)). The recommended Q4_K_M requires 275.5 GB which exceeds available memory, but at Q2_K it needs only 187.5 GB. Expected decode speed: 14.6 tok/s.

How much VRAM does Llama 4 Maverick 17B 128E need?

Llama 4 Maverick 17B 128E (400B parameters) requires approximately 275.5 GB at Q4_K_M quantization. On Mac Studio M3 Ultra 256GB, it fits at Q2_K using 187.5 GB.

What is the best quantization for Llama 4 Maverick 17B 128E?

The recommended quantization is Q4_K_M, but on Mac Studio M3 Ultra 256GB the best fitting quantization is Q2_K, which uses 187.5 GB.

What speed will Llama 4 Maverick 17B 128E run at on Mac Studio M3 Ultra 256GB?

On Mac Studio M3 Ultra 256GB, Llama 4 Maverick 17B 128E achieves approximately 14.6 tokens per second decode speed with a time-to-first-token of 13234ms using Q2_K quantization.

Can Mac Studio M3 Ultra 256GB run Llama 4 Maverick 17B 128E for coding?

For coding workloads, Llama 4 Maverick 17B 128E on Mac Studio M3 Ultra 256GB receives a F grade with 6.7 tok/s and 4K context.

What context window can Llama 4 Maverick 17B 128E use on Mac Studio M3 Ultra 256GB?

On Mac Studio M3 Ultra 256GB, Llama 4 Maverick 17B 128E can safely use up to 4K tokens of context at Q2_K quantization. The model's official context limit is 1.0M, but available memory constrains the safe maximum.

What should I upgrade first if Llama 4 Maverick 17B 128E feels slow on Mac Studio M3 Ultra 256GB?

Buy headroom, not only minimum fit. A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.

Is unified memory on Mac Studio M3 Ultra 256GB as fast as VRAM for Llama 4 Maverick 17B 128E?

Not always. Mac Studio M3 Ultra 256GB can often fit larger models thanks to unified memory, but a discrete GPU with dedicated high-bandwidth VRAM may still decode faster once the model fits. For this combination, the important distinction is capacity versus sustained throughput.

See all results for Mac Studio M3 Ultra 256GBSee all hardware for Llama 4 Maverick 17B 128E
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