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URL: https://willitrunai.com/can-run/hf-bartowski--yi-1-5-6b-chat-gguf-on-m4-pro-24gb

⇱ Yi 1.5 6B Chat on MacBook Pro M4 Pro 24GB? YES

Can Yi 1.5 6B Chat run on MacBook Pro M4 Pro 24GB?

YES — Runs Great

C50Usable
▲Estimated — low-sample bucket· few comparable runs

Yi 1.5 6B Chat needs ~7.9 GB VRAM. MacBook Pro M4 Pro 24GB has 17.3 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) — 7.9 GB, 52.8 tok/s, Runs well
7.9 GB required17.3 GB available
46% VRAM used

Fit status

Runs well

Decode

52.8 tok/s

TTFT

3664 ms

Safe context

230K

Memory

7.9 GB / 17.3 GB

Memory breakdown

Weights3.7 GB
KV Cache0.7 GB
Runtime0.9 GB
Headroom2.6 GB

See how fast it feels

See how fast it feelsYi 1.5 6B Chat on MacBook Pro M4 Pro 24GB
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 ms230K
CodingCRuns well52.8 tok/s3664 ms230K
Agentic CodingCRuns well52.8 tok/s5329 ms230K
ReasoningCRuns well52.8 tok/s4330 ms230K
RAGCRuns well52.8 tok/s6662 ms230K

Quantization options

How Yi 1.5 6B Chat (6B params) fits at each quantization level on MacBook Pro M4 Pro 24GB (17.3 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
2.3 GB
LowC46
Q3_K_S
3
2.9 GB
LowC46
NVFP4
4
3.4 GB
MediumC47
Q4_K_M
4
3.7 GB
MediumC47
Q5_K_M
5
4.3 GB
HighC47
Q6_K
6
4.9 GB
HighC48
Q8_0
8
6.4 GB
Very HighC49
F16Best for your GPU
16
12.3 GB
MaximumC50

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

Frequently asked questions

See all results for MacBook Pro M4 Pro 24GBSee all hardware for Yi 1.5 6B Chat