Mac mini M4 64GBコスパ重視
64 GB Unified (+16)
C
Adds memory headroom for longer context windows and future model growth.8.1 tok/sデコード
Adds memory headroom for longer context windows and future model growth.
〜$1,099 MSRP
Can Yi 34B Chat run on MacBook Pro M3 Max 48GB?
YES — Tight Fit
C49Usable
Estimated from fit model
Yi 34B Chat needs ~30.5 GB VRAM. MacBook Pro M3 Max 48GB has 34.6 GB. With Q4_K_M quantization, expect ~13 tok/s.
Runtime: llama.cppCapacity: TightBandwidth: 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.
Select quantization to explore
Q4_K_M (Medium quality) — 30.5 GB, 12.6 tok/s, Tight fit
30.5 GB required34.6 GB available
88% VRAM used
Fit status
Tight fit
Decode
12.6 tok/s
TTFT
15409 ms
Safe context
34K
Memory
30.5 GB / 34.6 GB
Memory breakdown
Weights20.7 GB
KV Cache3.7 GB
Runtime0.9 GB
Headroom5.2 GB
See how fast it feels
See how fast it feelsYi 34B Chat on MacBook Pro M3 Max 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: 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 | Tight fit | 12.6 tok/s | 8405 ms | 34K |
| Coding | C | Tight fit | 12.6 tok/s | 15409 ms | 34K |
| Agentic Coding | C | Runs with offload | 12.6 tok/s | 22414 ms | 34K |
| Reasoning | C | Tight fit | 12.6 tok/s | 18211 ms | 34K |
| RAG | C | Runs with offload | 12.6 tok/s | 28017 ms | 34K |
Quantization options
How Yi 34B Chat (34B params) fits at each quantization level on MacBook Pro M3 Max 48GB (34.6 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 13.3 GB | Low | C49 |
Q3_K_S | 3 | 16.7 GB | Low | C50 |
NVFP4 | 4 | 19.0 GB | Medium | C51 |
Q4_K_M | 4 | 20.7 GB | Medium | C50 |
Q5_K_M | 5 | 24.5 GB | High | C50 |
Q6_KBest for your GPU | 6 | 27.9 GB | High | C50 |
Q8_0 | 8 | 36.4 GB | Very High | F0 |
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アップグレードオプション
Yi 34B Chatを快適に動かすハードウェア
MacBook Pro M4 Pro 64GBコスパ最良
64 GB Unified (+16)
C
Raises estimated decode speed by about 57%.19.8 tok/sデコード
Raises estimated decode speed by about 57%.
Adds memory headroom for longer context windows and future model growth.
〜$1,599 MSRP
MacBook Pro M3 Max 64GBAppleアップグレード
64 GB Unified (+16)
C
Adds memory headroom for longer context windows and future model growth.12.6 tok/sデコード
Adds memory headroom for longer context windows and future model growth.
〜$2,499 MSRP
Frequently asked questions
Can MacBook Pro M3 Max 48GB run Yi 34B Chat?
Yes, MacBook Pro M3 Max 48GB can run Yi 34B Chat with a C grade (Tight fit). Expected decode speed: 12.6 tok/s.
How much VRAM does Yi 34B Chat need?
Yi 34B Chat (34B parameters) requires approximately 30.5 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 48GB?
On MacBook Pro M3 Max 48GB, 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 48GB run Yi 34B Chat for coding?
For coding workloads, Yi 34B Chat on MacBook Pro M3 Max 48GB receives a C grade with 12.6 tok/s and 34K context.
What context window can Yi 34B Chat use on MacBook Pro M3 Max 48GB?
On MacBook Pro M3 Max 48GB, Yi 34B Chat can safely use up to 34K 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 48GB as fast as VRAM for Yi 34B Chat?
Not always. MacBook Pro M3 Max 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.
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