Can Yi 1.5 6B Chat run on MacBook Pro M4 Pro 48GB?

YES — Runs Great

C46Usable
Estimated — low-sample bucket· few comparable runs

Yi 1.5 6B Chat needs ~10.4 GB VRAM. MacBook Pro M4 Pro 48GB has 34.6 GB. With Q4_K_M quantization, expect ~53 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: LowStack: 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.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) 10.4 GB, 52.8 tok/s, Runs well
10.4 GB required34.6 GB available
30% VRAM used

Fit status

Runs well

Decode

52.8 tok/s

TTFT

3664 ms

Safe context

565K

Memory

10.4 GB / 34.6 GB

Memory breakdown

Weights3.7 GB
KV Cache0.7 GB
Runtime0.9 GB
Headroom5.2 GB

See how fast it feels

See how fast it feelsYi 1.5 6B Chat on MacBook Pro M4 Pro 48GB
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: 52.8 tok/s decode · 3.7s TTFT (warm) · 132 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

WorkloadGradeFitDecodeTTFTContext
ChatCRuns well52.8 tok/s1998 ms565K
CodingCRuns well52.8 tok/s3664 ms565K
Agentic CodingCRuns well52.8 tok/s5329 ms565K
ReasoningCRuns well52.8 tok/s4330 ms565K
RAGCRuns well52.8 tok/s6662 ms565K

Quantization options

How Yi 1.5 6B Chat (6B params) fits at each quantization level on MacBook Pro M4 Pro 48GB (34.6 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
2.3 GB
LowC42
Q3_K_S
3
2.9 GB
LowC43
NVFP4
4
3.4 GB
MediumC43
Q4_K_M
4
3.7 GB
MediumC43
Q5_K_M
5
4.3 GB
HighC43
Q6_K
6
4.9 GB
HighC43
Q8_0
8
6.4 GB
Very HighC44
F16Best for your GPU
16
12.3 GB
MaximumC46

Get started

Copy-paste commands to run Yi 1.5 6B Chat on your machine.

Run

lms load hf-bartowski--yi-1-5-6b-chat-gguf && lms server start

Upgrade-Optionen

Hardware, die Yi 1.5 6B Chat gut ausführt

Frequently asked questions

Can MacBook Pro M4 Pro 48GB run Yi 1.5 6B Chat?

Yes, MacBook Pro M4 Pro 48GB can run Yi 1.5 6B Chat with a C grade (Runs well). Expected decode speed: 52.8 tok/s.

How much VRAM does Yi 1.5 6B Chat need?

Yi 1.5 6B Chat (6B parameters) requires approximately 10.4 GB of memory with Q4_K_M quantization.

What is the best quantization for Yi 1.5 6B Chat?

The recommended quantization for Yi 1.5 6B Chat is Q4_K_M, which balances quality and memory efficiency.

What speed will Yi 1.5 6B Chat run at on MacBook Pro M4 Pro 48GB?

On MacBook Pro M4 Pro 48GB, Yi 1.5 6B Chat achieves approximately 52.8 tokens per second decode speed with a time-to-first-token of 3664ms using Q4_K_M quantization.

Can MacBook Pro M4 Pro 48GB run Yi 1.5 6B Chat for coding?

For coding workloads, Yi 1.5 6B Chat on MacBook Pro M4 Pro 48GB receives a C grade with 52.8 tok/s and 565K context.

What context window can Yi 1.5 6B Chat use on MacBook Pro M4 Pro 48GB?

On MacBook Pro M4 Pro 48GB, Yi 1.5 6B Chat can safely use up to 565K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

Is unified memory on MacBook Pro M4 Pro 48GB as fast as VRAM for Yi 1.5 6B Chat?

Not always. MacBook Pro M4 Pro 48GB 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 Pro 48GBSee all hardware for Yi 1.5 6B Chat
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