Can Yi 34B Chat run on MacBook Pro M4 Max 96GB?

YES — Runs Great

C51Usable

Estimated from fit model

Yi 34B Chat needs ~35.7 GB VRAM. MacBook Pro M4 Max 96GB has 69.1 GB. With Q4_K_M quantization, expect ~31 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: MediumStack: 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, 31.4 tok/s, Runs well

35.7 GB required69.1 GB available

52% VRAM used

Fit status

Runs well

Decode

31.4 tok/s

TTFT

6158 ms

Safe context

162K

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 34B Chat on MacBook Pro M4 Max 96GB
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: 31.4 tok/s decode · 6.2s TTFT (warm) · 79 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	31.4 tok/s	3359 ms	162K
Coding	C	Runs well	31.4 tok/s	6158 ms	162K
Agentic Coding	C	Runs well	31.4 tok/s	8957 ms	162K
Reasoning	C	Runs well	31.4 tok/s	7277 ms	162K
RAG	C	Runs well	31.4 tok/s	11196 ms	162K

Quantization options

How Yi 34B Chat (34B params) fits at each quantization level on MacBook Pro M4 Max 96GB (69.1 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	13.3 GB	Low	C43
Q3_K_S	3	16.7 GB	Low	C44
NVFP4	4	19.0 GB	Medium	C44
Q4_K_M	4	20.7 GB	Medium	C45
Q5_K_M	5	24.5 GB	High	C45
Q6_K	6	27.9 GB	High	C46
Q8_0Best for your GPU	8	36.4 GB	Very High	C49
F16	16	69.7 GB	Maximum	F0

Get started

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

Run

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

Upgrade-Optionen

Hardware, die Yi 34B Chat gut ausführt

NVIDIA H20 96GBGünstige Wahl

4000 GB/s (+3454)

Raises estimated decode speed by about 440%.169.6 tok/s Dekodierung

Raises estimated decode speed by about 440%.

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

ca. $12,000 MSRP

NVIDIA A100 80GBBestes Preis-Leistungs-Verhältnis

2039 GB/s (+1493)

Raises estimated decode speed by about 186%.89.7 tok/s Dekodierung

Raises estimated decode speed by about 186%.

ca. $15,000 MSRP

Frequently asked questions

Can MacBook Pro M4 Max 96GB run Yi 34B Chat?

Yes, MacBook Pro M4 Max 96GB can run Yi 34B Chat with a C grade (Runs well). Expected decode speed: 31.4 tok/s.

How much VRAM does Yi 34B Chat need?

Yi 34B Chat (34B parameters) requires approximately 35.7 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 M4 Max 96GB?

On MacBook Pro M4 Max 96GB, Yi 34B Chat achieves approximately 31.4 tokens per second decode speed with a time-to-first-token of 6158ms using Q4_K_M quantization.

Can MacBook Pro M4 Max 96GB run Yi 34B Chat for coding?

For coding workloads, Yi 34B Chat on MacBook Pro M4 Max 96GB receives a C grade with 31.4 tok/s and 162K context.

What context window can Yi 34B Chat use on MacBook Pro M4 Max 96GB?

On MacBook Pro M4 Max 96GB, Yi 34B Chat can safely use up to 162K tokens of context. The model's official context limit is 200K, but available memory constrains the safe maximum.

Is unified memory on MacBook Pro M4 Max 96GB as fast as VRAM for Yi 34B Chat?

Not always. MacBook Pro M4 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 M4 Max 96GB See all hardware for Yi 34B Chat

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