Can Yi 34B Chat run on MacBook Pro M3 Max 64GB?

Q: Is unified memory on MacBook Pro M3 Max 64GB as fast as VRAM for Yi 34B Chat?

Not always. MacBook Pro M3 Max 64GB 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.

YES — Runs Great

C52Usable

Estimated from fit model

Yi 34B Chat needs ~32.2 GB VRAM. MacBook Pro M3 Max 64GB has 46.1 GB. With Q4_K_M quantization, expect ~13 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) — 32.2 GB, 12.6 tok/s, Runs well

32.2 GB required46.1 GB available

70% VRAM used

Fit status

Runs well

Decode

12.6 tok/s

TTFT

15409 ms

Safe context

77K

Memory

32.2 GB / 46.1 GB

Memory breakdown

Weights20.7 GB

KV Cache3.7 GB

Runtime0.9 GB

Headroom6.9 GB

See how fast it feels

See how fast it feelsYi 34B Chat on MacBook Pro M3 Max 64GB
1st promptCold start — includes initialization
>What is local AI inference?
Local AI inference means running an AI language model directly on your own hardware — your laptop, desktop, or server — instead of sending requests to a remote cloud API.

When you run inference locally the model weights are loaded into your GPU or unified memory. Each token you generate requires reading those weights from memory, so memory bandwidth is the main bottleneck for decode speed.

Key benefits of running locally:
  - Full privacy: your prompts never leave your machine
  - No per-token cost or rate limits
  - Works offline once the model is downloaded
  - Latency depends only on your hardware
2nd promptWarm — model ready, no init overhead
>How much VRAM do I need?
It depends on the model size and quantization level. A rough rule of thumb:

  Model size     Q4 (4-bit)    Q8 (8-bit)    FP16
  7B params      ~4.3 GB       ~7.5 GB       ~14 GB
  13B params     ~7.9 GB       ~13.9 GB      ~26 GB
  70B params     ~42.7 GB      ~74.9 GB      ~140 GB

Most people use 4-bit quantization (Q4_K_M) which gives 90-95% of full quality at a fraction of the memory. A 24 GB GPU can comfortably run most 7B-13B models.
Estimated: 12.6 tok/s decode · 15.4s TTFT (warm) · 31 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	C	Runs well	11.6 tok/s	9126 ms	77K
Coding	C	Runs well	12.6 tok/s	15409 ms	77K
Agentic Coding	C	Runs well	12.6 tok/s	22414 ms	77K
Reasoning	C	Runs well	12.6 tok/s	18211 ms	77K
RAG	C	Runs well	12.6 tok/s	28017 ms	77K

Quantization options

How Yi 34B Chat (34B params) fits at each quantization level on MacBook Pro M3 Max 64GB (46.1 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	13.3 GB	Low	C46
Q3_K_S	3	16.7 GB	Low	C47
NVFP4	4

Get started

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

Run

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

Upgrade options

Hardware that runs Yi 34B Chat well

Radeon Pro W7900 48GBBudget pick

864 GB/s (+464)

Raises estimated decode speed by about 112%.26.7 tok/s decode

Raises estimated decode speed by about 112%.

~$3,999 MSRP

Radeon PRO W7900 DS 48GBBest value

864 GB/s (+464)

Raises estimated decode speed by about 112%.26.7 tok/s decode

Raises estimated decode speed by about 112%.

~$3,999 MSRP

Frequently asked questions

Can MacBook Pro M3 Max 64GB run Yi 34B Chat?

Yes, MacBook Pro M3 Max 64GB can run Yi 34B Chat with a C grade (Runs well). Expected decode speed: 12.6 tok/s.

How much VRAM does Yi 34B Chat need?

Yi 34B Chat (34B parameters) requires approximately 32.2 GB of memory with Q4_K_M quantization.

What is the best quantization for Yi 34B Chat?

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

What speed will Yi 34B Chat run at on MacBook Pro M3 Max 64GB?

On MacBook Pro M3 Max 64GB, Yi 34B Chat achieves approximately 12.6 tokens per second decode speed with a time-to-first-token of 15409ms using Q4_K_M quantization.

Can MacBook Pro M3 Max 64GB run Yi 34B Chat for coding?

For coding workloads, Yi 34B Chat on MacBook Pro M3 Max 64GB receives a C grade with 12.6 tok/s and 77K context.

What context window can Yi 34B Chat use on MacBook Pro M3 Max 64GB?

On MacBook Pro M3 Max 64GB, Yi 34B Chat can safely use up to 77K tokens of context. The model's official context limit is 200K, but available memory constrains the safe maximum.

Is unified memory on MacBook Pro M3 Max 64GB as fast as VRAM for Yi 34B Chat?

See all results for MacBook Pro M3 Max 64GB See all hardware for Yi 34B Chat

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