Radeon Pro W7900 48GBOpção econômica
864 GB/s (+464)
C
Raises estimated decode speed by about 131%.26.1 tok/s decodificação
Raises estimated decode speed by about 131%.
~$3,999 MSRP
Can EXAONE 4.0 32B run on MacBook Pro M1 Max 64GB?
YES — Runs Great
C49Usable
Estimated from fit model
EXAONE 4.0 32B needs ~31.1 GB VRAM. MacBook Pro M1 Max 64GB has 46.1 GB. With Q4_K_M quantization, expect ~11 tok/s.
Runtime: llama.cppCapacity: RoomyBandwidth: 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) — 31.1 GB, 11.3 tok/s, Runs well
31.1 GB required46.1 GB available
67% VRAM used
Fit status
Runs well
Decode
11.3 tok/s
TTFT
17178 ms
Safe context
80K
Memory
31.1 GB / 46.1 GB
Memory breakdown
Weights19.5 GB
KV Cache3.8 GB
Runtime0.9 GB
Headroom6.9 GB
See how fast it feels
See how fast it feelsEXAONE 4.0 32B on MacBook Pro M1 Max 64GB
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: 11.3 tok/s decode · 17.2s TTFT (warm) · 28 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 | 11.3 tok/s | 9370 ms | 80K |
| Coding | C | Runs well | 11.3 tok/s | 17178 ms | 80K |
| Agentic Coding | C | Runs well | 11.3 tok/s | 24986 ms | 80K |
| Reasoning | C | Runs well | 11.3 tok/s | 20301 ms | 80K |
| RAG | C | Runs well | 11.3 tok/s | 31232 ms | 80K |
Quantization options
How EXAONE 4.0 32B (32B params) fits at each quantization level on MacBook Pro M1 Max 64GB (46.1 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 12.5 GB | Low | C44 |
Q3_K_S | 3 | 15.7 GB | Low | C45 |
NVFP4 | 4 | 17.9 GB | Medium | C46 |
Q4_K_M | 4 | 19.5 GB | Medium | C46 |
Q5_K_M | 5 | 23.0 GB | High | C48 |
Q6_K | 6 | 26.2 GB | High | C48 |
Q8_0Best for your GPU | 8 | 34.2 GB | Very High | C48 |
F16 | 16 | 65.6 GB | Maximum | F0 |
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 startOpções de upgrade
Hardware que roda bem EXAONE 4.0 32B
Frequently asked questions
Can MacBook Pro M1 Max 64GB run EXAONE 4.0 32B?
Yes, MacBook Pro M1 Max 64GB can run EXAONE 4.0 32B with a C grade (Runs well). Expected decode speed: 11.3 tok/s.
How much VRAM does EXAONE 4.0 32B need?
EXAONE 4.0 32B (32B parameters) requires approximately 31.1 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 M1 Max 64GB?
On MacBook Pro M1 Max 64GB, EXAONE 4.0 32B achieves approximately 11.3 tokens per second decode speed with a time-to-first-token of 17178ms using Q4_K_M quantization.
Can MacBook Pro M1 Max 64GB run EXAONE 4.0 32B for coding?
For coding workloads, EXAONE 4.0 32B on MacBook Pro M1 Max 64GB receives a C grade with 11.3 tok/s and 80K context.
What context window can EXAONE 4.0 32B use on MacBook Pro M1 Max 64GB?
On MacBook Pro M1 Max 64GB, EXAONE 4.0 32B can safely use up to 80K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
Is unified memory on MacBook Pro M1 Max 64GB as fast as VRAM for EXAONE 4.0 32B?
Not always. MacBook Pro M1 Max 64GB 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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