Will It Run AI

Can BaichuanMed OCR 72B i1 run on MacBook Pro M4 Max 96GB?

YES — Tight Fit

C47Usable
Estimated from fit model

BaichuanMed OCR 72B i1 needs ~63.6 GB VRAM. MacBook Pro M4 Max 96GB has 69.1 GB. With Q4_K_M quantization, expect ~14 tok/s.

Runtime: llama.cppCapacity: TightBandwidth: MediumStack: 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) — 63.6 GB, 13.7 tok/s, Tight fit
63.6 GB required69.1 GB available
92% VRAM used

Fit status

Tight fit

Decode

13.7 tok/s

TTFT

14158 ms

Safe context

26K

Memory

63.6 GB / 69.1 GB

Memory breakdown

Weights43.9 GB
KV Cache8.4 GB
Runtime0.9 GB
Headroom10.4 GB

See how fast it feels

See how fast it feelsBaichuanMed OCR 72B i1 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: 13.7 tok/s decode · 14.2s TTFT (warm) · 34 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

Very little memory headroom

You can run the model, but there is not much room left for longer context, bigger batches, extra apps, or future model updates.

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

Buy headroom, not only minimum fit

A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.

Performance by workload

WorkloadGradeFitDecodeTTFTContext
ChatCTight fit13.7 tok/s7722 ms26K
CodingCTight fit13.7 tok/s14158 ms26K
Agentic CodingCRuns with offload (needs ~1.8 GB host RAM)12.6 tok/s22321 ms26K
ReasoningCTight fit13.7 tok/s16732 ms26K
RAGCRuns with offload (needs ~1.8 GB host RAM)12.6 tok/s27901 ms26K

Quantization options

How BaichuanMed OCR 72B i1 (72B params) fits at each quantization level on MacBook Pro M4 Max 96GB (69.1 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
28.1 GB
LowC45
Q3_K_S
3
35.3 GB
LowC47
NVFP4
4
40.3 GB
MediumC47
Q4_K_M
4
43.9 GB
MediumC47
Q5_K_MBest for your GPU
5
51.8 GB
HighC47
Q6_K
6
59.0 GB
HighF0
Q8_0
8
77.0 GB
Very HighF0
F16
16
147.6 GB
MaximumF0

Get started

Copy-paste commands to run BaichuanMed OCR 72B i1 on your machine.

Run

lms load hf-mradermacher--baichuanmed-ocr-72b-i1-gguf && lms server start

Opciones de mejora

Hardware que ejecuta bien BaichuanMed OCR 72B i1

MacBook Pro M3 Max 128GBOpción económica
128 GB Unified (+32)
C
Añade margen de memoria para más contexto y para que el modelo envejezca mejor.5.5 tok/s decodificación

Añade margen de memoria para más contexto y para que el modelo envejezca mejor.

~$2,499 MSRP

Mac Studio M2 Ultra 128GBMejor relación calidad-precio
128 GB Unified (+32)800 GB/s (+254)
C
Añade margen de memoria para más contexto y para que el modelo envejezca mejor.10.6 tok/s decodificación

Añade margen de memoria para más contexto y para que el modelo envejezca mejor.

~$3,999 MSRP

Mac Studio M1 Ultra 128GBMejora Apple
128 GB Unified (+32)800 GB/s (+254)
C
Añade margen de memoria para más contexto y para que el modelo envejezca mejor.10 tok/s decodificación

Añade margen de memoria para más contexto y para que el modelo envejezca mejor.

~$3,999 MSRP

NVIDIANVIDIA H100 80GBMayor salto
3350 GB/s (+2804)
B
Sube la velocidad estimada de decodificación alrededor de un 368%.64.1 tok/s decodificación

Sube la velocidad estimada de decodificación alrededor de un 368%.

~$40,000 MSRP

Frequently asked questions

Can MacBook Pro M4 Max 96GB run BaichuanMed OCR 72B i1?

Yes, MacBook Pro M4 Max 96GB can run BaichuanMed OCR 72B i1 with a C grade (Tight fit). Expected decode speed: 13.7 tok/s.

How much VRAM does BaichuanMed OCR 72B i1 need?

BaichuanMed OCR 72B i1 (72B parameters) requires approximately 63.6 GB of memory with Q4_K_M quantization.

What is the best quantization for BaichuanMed OCR 72B i1?

The recommended quantization for BaichuanMed OCR 72B i1 is Q4_K_M, which balances quality and memory efficiency.

What speed will BaichuanMed OCR 72B i1 run at on MacBook Pro M4 Max 96GB?

On MacBook Pro M4 Max 96GB, BaichuanMed OCR 72B i1 achieves approximately 13.7 tokens per second decode speed with a time-to-first-token of 14158ms using Q4_K_M quantization.

Can MacBook Pro M4 Max 96GB run BaichuanMed OCR 72B i1 for coding?

For coding workloads, BaichuanMed OCR 72B i1 on MacBook Pro M4 Max 96GB receives a C grade with 13.7 tok/s and 26K context.

What context window can BaichuanMed OCR 72B i1 use on MacBook Pro M4 Max 96GB?

On MacBook Pro M4 Max 96GB, BaichuanMed OCR 72B i1 can safely use up to 26K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

What should I upgrade first if BaichuanMed OCR 72B i1 feels slow on MacBook Pro M4 Max 96GB?

Buy headroom, not only minimum fit. A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.

Is unified memory on MacBook Pro M4 Max 96GB as fast as VRAM for BaichuanMed OCR 72B i1?

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 96GBSee all hardware for BaichuanMed OCR 72B i1
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