Can Yi 1.5 34B run on MacBook Pro M2 Max 96GB?

YES — Runs Great

B59Good

Estimated from fit model

Yi 1.5 34B needs ~35.7 GB VRAM. MacBook Pro M2 Max 96GB has 69.1 GB. With Q4_K_M quantization, expect ~12 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: LowStack: StandardBottleneck: 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

Q4_K_M (Medium quality) — 35.7 GB, 12.1 tok/s, Runs well

35.7 GB required69.1 GB available

52% VRAM used

Fit status

Runs well

Decode

12.1 tok/s

TTFT

15941 ms

Safe context

Memory

35.7 GB / 69.1 GB

Memory breakdown

Weights20.7 GB

KV Cache3.7 GB

Runtime0.9 GB

Headroom10.4 GB

See how fast it feels

See how fast it feelsYi 1.5 34B on MacBook Pro M2 Max 96GB
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: 12.1 tok/s decode · 15.9s TTFT (warm) · 30 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	Runs well	12.1 tok/s	8695 ms	4K
Coding	B	Runs well	12.1 tok/s	15941 ms	4K
Agentic Coding	B	Runs well	12.1 tok/s	23187 ms	4K
Reasoning	B	Runs well	12.1 tok/s	18839 ms	4K
RAG	B	Runs well	12.1 tok/s	28983 ms	4K

Quantization options

How Yi 1.5 34B (34B params) fits at each quantization level on MacBook Pro M2 Max 96GB (69.1 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	13.3 GB	Low	C54
Q3_K_S	3	16.7 GB	Low	C55
NVFP4	4	19.0 GB	Medium	B55
Q4_K_M	4	20.7 GB	Medium	B56
Q5_K_M	5	24.5 GB	High	B57
Q6_K	6	27.9 GB	High	B57
Q8_0Best for your GPU	8	36.4 GB	Very High	B60
F16	16	69.7 GB	Maximum	F0

Get started

Copy-paste commands to run Yi 1.5 34B on your machine.

Run

lms load Yi-1.5-34B-Chat && lms server start

Upgrade options

Hardware that runs Yi 1.5 34B well

Mac Studio M2 Ultra 128GBBudget pick

128 GB Unified (+32)800 GB/s (+400)

Raises estimated decode speed by about 101%.24.3 tok/s decode

Raises estimated decode speed by about 101%.

Adds memory headroom for longer context windows and future model growth.

~$3,999 MSRP

Mac Studio M1 Ultra 128GBBest value

128 GB Unified (+32)800 GB/s (+400)

Raises estimated decode speed by about 90%.23 tok/s decode

Raises estimated decode speed by about 90%.

Adds memory headroom for longer context windows and future model growth.

~$3,999 MSRP

MacBook Pro M4 Max 128GBApple upgrade

128 GB Unified (+32)546 GB/s (+146)

Raises estimated decode speed by about 160%.31.4 tok/s decode

Raises estimated decode speed by about 160%.

Adds memory headroom for longer context windows and future model growth.

~$4,999 MSRP

Frequently asked questions

Can MacBook Pro M2 Max 96GB run Yi 1.5 34B?

Yes, MacBook Pro M2 Max 96GB can run Yi 1.5 34B with a B grade (Runs well). Expected decode speed: 12.1 tok/s.

How much VRAM does Yi 1.5 34B need?

Yi 1.5 34B (34B parameters) requires approximately 35.7 GB of memory with Q4_K_M quantization.

What is the best quantization for Yi 1.5 34B?

The recommended quantization for Yi 1.5 34B is Q4_K_M, which balances quality and memory efficiency.

What speed will Yi 1.5 34B run at on MacBook Pro M2 Max 96GB?

On MacBook Pro M2 Max 96GB, Yi 1.5 34B achieves approximately 12.1 tokens per second decode speed with a time-to-first-token of 15941ms using Q4_K_M quantization.

Can MacBook Pro M2 Max 96GB run Yi 1.5 34B for coding?

For coding workloads, Yi 1.5 34B on MacBook Pro M2 Max 96GB receives a B grade with 12.1 tok/s and 4K context.

What context window can Yi 1.5 34B use on MacBook Pro M2 Max 96GB?

On MacBook Pro M2 Max 96GB, Yi 1.5 34B can safely use up to 4K tokens of context. The model's official context limit is 4K, but available memory constrains the safe maximum.

Is unified memory on MacBook Pro M2 Max 96GB as fast as VRAM for Yi 1.5 34B?

Not always. MacBook Pro M2 Max 96GB 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 MacBook Pro M2 Max 96GB See all hardware for Yi 1.5 34B

Embed this result▼

Paste this snippet into any page to show a live fit card.

<iframe src="https://willitrunai.com/embed/yi-1.5-34b-on-m2-max-96gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>

Preview:

Workload

Grade

Fit

Decode

TTFT

Context

Chat

Runs well

12.1 tok/s

8695 ms

Coding

Runs well

12.1 tok/s

15941 ms

Agentic Coding

Runs well

12.1 tok/s

23187 ms

Reasoning

Runs well

12.1 tok/s

18839 ms

RAG

Runs well

12.1 tok/s

28983 ms

Quantization options

How Yi 1.5 34B (34B params) fits at each quantization level on MacBook Pro M2 Max 96GB (69.1 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	13.3 GB	Low	C54
Q3_K_S	3	16.7 GB	Low	C55
NVFP4	4	19.0 GB	Medium	B55
Q4_K_M	4	20.7 GB	Medium	B56
Q5_K_M	5	24.5 GB	High	B57
Q6_K	6	27.9 GB	High	B57
Q8_0Best for your GPU	8	36.4 GB	Very High	B60
F16	16	69.7 GB	Maximum	F0

Frequently asked questions

Can MacBook Pro M2 Max 96GB run Yi 1.5 34B?

Yes, MacBook Pro M2 Max 96GB can run Yi 1.5 34B with a B grade (Runs well). Expected decode speed: 12.1 tok/s.

How much VRAM does Yi 1.5 34B need?

Yi 1.5 34B (34B parameters) requires approximately 35.7 GB of memory with Q4_K_M quantization.

What is the best quantization for Yi 1.5 34B?

The recommended quantization for Yi 1.5 34B is Q4_K_M, which balances quality and memory efficiency.

What speed will Yi 1.5 34B run at on MacBook Pro M2 Max 96GB?

On MacBook Pro M2 Max 96GB, Yi 1.5 34B achieves approximately 12.1 tokens per second decode speed with a time-to-first-token of 15941ms using Q4_K_M quantization.

Can MacBook Pro M2 Max 96GB run Yi 1.5 34B for coding?

For coding workloads, Yi 1.5 34B on MacBook Pro M2 Max 96GB receives a B grade with 12.1 tok/s and 4K context.

What context window can Yi 1.5 34B use on MacBook Pro M2 Max 96GB?

On MacBook Pro M2 Max 96GB, Yi 1.5 34B can safely use up to 4K tokens of context. The model's official context limit is 4K, but available memory constrains the safe maximum.

Is unified memory on MacBook Pro M2 Max 96GB as fast as VRAM for Yi 1.5 34B?