RX 7900 XT 20GBBudget pick
800 GB/s (+527)
C
Raises estimated decode speed by about 149%.100.9 tok/s decode
Raises estimated decode speed by about 149%.
~$899 MSRP
Can exaone 3.0 7.8b it run on MacBook Pro M4 Pro 24GB?
YES — Runs Great
C50Usable
▲Estimated — low-sample bucket· few comparable runs
exaone 3.0 7.8b it needs ~9.2 GB VRAM. MacBook Pro M4 Pro 24GB has 17.3 GB. With Q4_K_M quantization, expect ~41 tok/s.
Runtime: llama.cppCapacity: RoomyBandwidth: LowStack: StandardBottleneck: Balanced
Share:
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) — 9.2 GB, 40.6 tok/s, Runs well
9.2 GB required17.3 GB available
53% VRAM used
Fit status
Runs well
Decode
40.6 tok/s
TTFT
4763 ms
Safe context
158K
Memory
9.2 GB / 17.3 GB
Memory breakdown
Weights4.8 GB
KV Cache0.9 GB
Runtime0.9 GB
Headroom2.6 GB
See how fast it feels
See how fast it feelsexaone 3.0 7.8b it 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: 40.6 tok/s decode · 4.8s TTFT (warm) · 102 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 | 40.6 tok/s | 2598 ms | 158K |
| Coding | C | Runs well | 40.6 tok/s | 4763 ms | 158K |
| Agentic Coding | C | Runs well | 40.6 tok/s | 6928 ms | 158K |
| Reasoning | C | Runs well | 40.6 tok/s | 5629 ms | 158K |
| RAG | C | Runs well | 40.6 tok/s | 8660 ms | 158K |
Quantization options
How exaone 3.0 7.8b it (7.800000190734863B params) fits at each quantization level on MacBook Pro M4 Pro 24GB (17.3 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 3.0 GB | Low | C46 |
Q3_K_S | 3 | 3.8 GB | Low | C47 |
NVFP4 | 4 |
Get started
Copy-paste commands to run exaone 3.0 7.8b it on your machine.
Run
lms load hf-bingsu--exaone-3-0-7-8b-it && lms server startUpgrade options
