Mac Studio M3 Ultra 256GBコスパ重視
256 GB Unified (+128)
C
Adds memory headroom for longer context windows and future model growth.109.2 tok/sデコード
Adds memory headroom for longer context windows and future model growth.
〜$6,999 MSRP
Can exaone 3.0 7.8b it run on Gaudi 3 128GB?
YES — Runs Great
C45Usable
Estimated from fit model
exaone 3.0 7.8b it needs ~19.4 GB VRAM. Gaudi 3 128GB has 128.0 GB. With Q4_K_M quantization, expect ~109 tok/s.
Runtime: llama.cppCapacity: RoomyBandwidth: HighStack: 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) — 19.4 GB, 109.2 tok/s, Runs well
19.4 GB required128.0 GB available
15% VRAM used
Fit status
Runs well
Decode
109.2 tok/s
TTFT
1773 ms
Safe context
1.9M
Memory
19.4 GB / 128.0 GB
Memory breakdown
Weights4.8 GB
KV Cache0.9 GB
Runtime0.9 GB
Headroom12.8 GB
See how fast it feels
See how fast it feelsexaone 3.0 7.8b it on Gaudi 3 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: 109.2 tok/s decode · 1.8s TTFT (warm) · 273 tok/s prefill
What limits this setup
The raw memory story may look fine, but the software ecosystem is still a constraint here.
Runtime ecosystem is narrower than CUDA
Intel GPUs can look attractive on memory per dollar, but local AI tooling, kernels, and model coverage are still broader and easier on CUDA today.
Best improvement path
Prefer CUDA if you want the path of least resistance
If your goal is maximum runtime coverage, easier troubleshooting, and better support for new local AI releases, CUDA is usually still the safer upgrade path.
Performance by workload
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | C | Runs well | 109.2 tok/s | 967 ms | 1.9M |
| Coding | C | Runs well | 109.2 tok/s | 1773 ms | 1.9M |
| Agentic Coding | C | Runs well | 109.2 tok/s | 2579 ms | 1.9M |
| Reasoning | C | Runs well | 109.2 tok/s | 2095 ms | 1.9M |
| RAG | C | Runs well | 109.2 tok/s | 3223 ms | 1.9M |
Quantization options
How exaone 3.0 7.8b it (7.800000190734863B params) fits at each quantization level on Gaudi 3 128GB (128.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 3.0 GB | Low | D38 |
Q3_K_S | 3 | 3.8 GB | Low | D38 |
NVFP4 | 4 | 4.4 GB | Medium | D38 |
Q4_K_M | 4 | 4.8 GB | Medium | D38 |
Q5_K_M | 5 | 5.6 GB | High | D38 |
Q6_K | 6 | 6.4 GB | High | D38 |
Q8_0 | 8 | 8.3 GB | Very High | D38 |
F16Best for your GPU | 16 | 16.0 GB | Maximum | D38 |
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 startアップグレードオプション
exaone 3.0 7.8b itを快適に動かすハードウェア
Frequently asked questions
Can Gaudi 3 128GB run exaone 3.0 7.8b it?
Yes, Gaudi 3 128GB can run exaone 3.0 7.8b it with a C grade (Runs well). Expected decode speed: 109.2 tok/s.
How much VRAM does exaone 3.0 7.8b it need?
exaone 3.0 7.8b it (7.800000190734863B parameters) requires approximately 19.4 GB of memory with Q4_K_M quantization.
What is the best quantization for exaone 3.0 7.8b it?
The recommended quantization for exaone 3.0 7.8b it is Q4_K_M, which balances quality and memory efficiency.
What speed will exaone 3.0 7.8b it run at on Gaudi 3 128GB?
On Gaudi 3 128GB, exaone 3.0 7.8b it achieves approximately 109.2 tokens per second decode speed with a time-to-first-token of 1773ms using Q4_K_M quantization.
Can Gaudi 3 128GB run exaone 3.0 7.8b it for coding?
For coding workloads, exaone 3.0 7.8b it on Gaudi 3 128GB receives a C grade with 109.2 tok/s and 1.9M context.
What context window can exaone 3.0 7.8b it use on Gaudi 3 128GB?
On Gaudi 3 128GB, exaone 3.0 7.8b it can safely use up to 1.9M tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
What should I upgrade first if exaone 3.0 7.8b it feels slow on Gaudi 3 128GB?
Prefer CUDA if you want the path of least resistance. If your goal is maximum runtime coverage, easier troubleshooting, and better support for new local AI releases, CUDA is usually still the safer upgrade path.
Would CUDA be a better path than Gaudi 3 128GB for exaone 3.0 7.8b it?
Often yes, if your goal is the easiest setup and the widest runtime support. Intel can offer attractive memory capacity, but CUDA still tends to win on tooling maturity, guides, kernels, and model coverage for local AI.
Embed this result▼
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/hf-bingsu--exaone-3-0-7-8b-it-on-gaudi-3-128gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview: