Mac Studio M3 Ultra 256GBOpção econômica
256 GB Unified (+128)
C
Adds memory headroom for longer context windows and future model growth.33.6 tok/s decodificação
Adds memory headroom for longer context windows and future model growth.
~$6,999 MSRP
Can EXAONE 3.5 2.4B Instruct run on Intel Data Center GPU Max 1550 128GB?
YES — Runs Great
C41Usable
Estimated from fit model
EXAONE 3.5 2.4B Instruct needs ~15.4 GB VRAM. Intel Data Center GPU Max 1550 128GB has 128.0 GB. With Q4_K_M quantization, expect ~34 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) — 15.4 GB, 33.6 tok/s, Runs well
15.4 GB required128.0 GB available
12% VRAM used
Fit status
Runs well
Decode
33.6 tok/s
TTFT
5762 ms
Safe context
6.4M
Memory
15.4 GB / 128.0 GB
Memory breakdown
Weights1.5 GB
KV Cache0.3 GB
Runtime0.9 GB
Headroom12.8 GB
See how fast it feels
See how fast it feelsEXAONE 3.5 2.4B Instruct on Intel Data Center GPU Max 1550 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: 33.6 tok/s decode · 5.8s TTFT (warm) · 84 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 | 33.6 tok/s | 3143 ms | 6.4M |
| Coding | C | Runs well | 33.6 tok/s | 5762 ms | 6.4M |
| Agentic Coding | C | Runs well | 33.6 tok/s | 8381 ms | 6.4M |
| Reasoning | C | Runs well | 33.6 tok/s | 6810 ms | 6.4M |
| RAG | C | Runs well | 33.6 tok/s | 10476 ms | 6.4M |
Quantization options
How EXAONE 3.5 2.4B Instruct (2.4000000953674316B params) fits at each quantization level on Intel Data Center GPU Max 1550 128GB (128.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 0.9 GB | Low | D38 |
Q3_K_S | 3 | 1.2 GB | Low | D38 |
NVFP4 | 4 | 1.3 GB | Medium | D38 |
Q4_K_M | 4 | 1.5 GB | Medium | D38 |
Q5_K_M | 5 | 1.7 GB | High | D38 |
Q6_K | 6 | 2.0 GB | High | D38 |
Q8_0 | 8 | 2.6 GB | Very High | D38 |
F16Best for your GPU | 16 | 4.9 GB | Maximum | D38 |
Get started
Copy-paste commands to run EXAONE 3.5 2.4B Instruct on your machine.
Run
lms load hf-lmstudio-community--exaone-3-5-2-4b-instruct-gguf && lms server startOpções de upgrade
Hardware que roda bem EXAONE 3.5 2.4B Instruct
Frequently asked questions
Can Intel Data Center GPU Max 1550 128GB run EXAONE 3.5 2.4B Instruct?
Yes, Intel Data Center GPU Max 1550 128GB can run EXAONE 3.5 2.4B Instruct with a C grade (Runs well). Expected decode speed: 33.6 tok/s.
How much VRAM does EXAONE 3.5 2.4B Instruct need?
EXAONE 3.5 2.4B Instruct (2.4000000953674316B parameters) requires approximately 15.4 GB of memory with Q4_K_M quantization.
What is the best quantization for EXAONE 3.5 2.4B Instruct?
The recommended quantization for EXAONE 3.5 2.4B Instruct is Q4_K_M, which balances quality and memory efficiency.
What speed will EXAONE 3.5 2.4B Instruct run at on Intel Data Center GPU Max 1550 128GB?
On Intel Data Center GPU Max 1550 128GB, EXAONE 3.5 2.4B Instruct achieves approximately 33.6 tokens per second decode speed with a time-to-first-token of 5762ms using Q4_K_M quantization.
Can Intel Data Center GPU Max 1550 128GB run EXAONE 3.5 2.4B Instruct for coding?
For coding workloads, EXAONE 3.5 2.4B Instruct on Intel Data Center GPU Max 1550 128GB receives a C grade with 33.6 tok/s and 6.4M context.
What context window can EXAONE 3.5 2.4B Instruct use on Intel Data Center GPU Max 1550 128GB?
On Intel Data Center GPU Max 1550 128GB, EXAONE 3.5 2.4B Instruct can safely use up to 6.4M tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
What should I upgrade first if EXAONE 3.5 2.4B Instruct feels slow on Intel Data Center GPU Max 1550 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 Intel Data Center GPU Max 1550 128GB for EXAONE 3.5 2.4B Instruct?
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.
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