RTX PRO 6000 Blackwell Workstation Edition 96GBGünstige Wahl1792 GB/s (+1246)
C
Raises estimated decode speed by about 150%.77.1 tok/s Dekodierung
Raises estimated decode speed by about 150%.
ca. $9,999 MSRP
Can EXAONE 4.0 32B run on MacBook Pro M4 Max 128GB?
YES — Runs Great
C45Usable
Estimated from fit model
EXAONE 4.0 32B needs ~38.0 GB VRAM. MacBook Pro M4 Max 128GB has 92.2 GB. With Q4_K_M quantization, expect ~18 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.
Select quantization to explore
Q4_K_M (Medium quality) — 38.0 GB, 30.8 tok/s, Runs well
38.0 GB required92.2 GB available
41% VRAM used
Fit status
Runs well
Decode
30.8 tok/s
TTFT
6292 ms
Safe context
247K
Memory
38.0 GB / 92.2 GB
Memory breakdown
Weights19.5 GB
KV Cache3.8 GB
Runtime0.9 GB
Headroom13.8 GB
See how fast it feels
See how fast it feelsEXAONE 4.0 32B on MacBook Pro M4 Max 128GB
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: 30.8 tok/s decode · 6.3s TTFT (warm) · 77 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 | 30.8 tok/s | 3432 ms | 247K |
| Coding | C | Runs well | 17.6 tok/s | 10986 ms | 247K |
| Agentic Coding | C | Runs well | 30.8 tok/s | 9152 ms | 247K |
| Reasoning | C | Runs well | 30.8 tok/s | 7436 ms | 247K |
| RAG | C | Runs well | 30.8 tok/s | 11441 ms | 247K |
Quantization options
How EXAONE 4.0 32B (32B params) fits at each quantization level on MacBook Pro M4 Max 128GB (92.2 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 12.5 GB | Low | D40 |
Q3_K_S | 3 | 15.7 GB | Low | C40 |
NVFP4 | 4 | 17.9 GB | Medium | C40 |
Q4_K_M | 4 | 19.5 GB | Medium | C41 |
Q5_K_M | 5 | 23.0 GB | High | C41 |
Q6_K | 6 | 26.2 GB | High | C42 |
Q8_0 | 8 | 34.2 GB | Very High | C43 |
F16Best for your GPU | 16 | 65.6 GB | Maximum | C48 |
Get started
Copy-paste commands to run EXAONE 4.0 32B on your machine.
Run
lms load hf-lgai-exaone--exaone-4-0-32b-gguf && lms server startUpgrade-Optionen
Hardware, die EXAONE 4.0 32B gut ausführt
Frequently asked questions
Can MacBook Pro M4 Max 128GB run EXAONE 4.0 32B?
Yes, MacBook Pro M4 Max 128GB can run EXAONE 4.0 32B with a C grade (Runs well). Expected decode speed: 17.6 tok/s.
How much VRAM does EXAONE 4.0 32B need?
EXAONE 4.0 32B (32B parameters) requires approximately 38.0 GB of memory with Q4_K_M quantization.
What is the best quantization for EXAONE 4.0 32B?
The recommended quantization for EXAONE 4.0 32B is Q4_K_M, which balances quality and memory efficiency.
What speed will EXAONE 4.0 32B run at on MacBook Pro M4 Max 128GB?
On MacBook Pro M4 Max 128GB, EXAONE 4.0 32B achieves approximately 17.6 tokens per second decode speed with a time-to-first-token of 10986ms using Q4_K_M quantization.
Can MacBook Pro M4 Max 128GB run EXAONE 4.0 32B for coding?
For coding workloads, EXAONE 4.0 32B on MacBook Pro M4 Max 128GB receives a C grade with 17.6 tok/s and 247K context.
What context window can EXAONE 4.0 32B use on MacBook Pro M4 Max 128GB?
On MacBook Pro M4 Max 128GB, EXAONE 4.0 32B can safely use up to 247K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
Is unified memory on MacBook Pro M4 Max 128GB as fast as VRAM for EXAONE 4.0 32B?
Not always. MacBook Pro M4 Max 128GB 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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