Mac Studio M3 Ultra 256GBBudget pick
256 GB Unified (+160)
C
Adds memory headroom for longer context windows and future model growth.33.6 tok/s decode
Adds memory headroom for longer context windows and future model growth.
~$6,999 MSRP
EXAONE 3.5 2.4B Instruct needs ~12.5 GB VRAM. RTX PRO 6000 Blackwell Workstation Edition 96GB has 96.0 GB. With Q4_K_M quantization, expect ~34 tok/s.
Operating mode
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
Fit status
Runs well
Decode
33.6 tok/s
TTFT
5762 ms
Safe context
4.8M
Memory
12.5 GB / 96.0 GB
This setup is broadly balanced for this model.
No major red flags
This recommendation has enough memory headroom and acceptable estimated speed for the selected workload.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | C | Runs well | 33.6 tok/s | 3143 ms | 4.8M |
| Coding | C | Runs well | 33.6 tok/s | 5762 ms | 4.8M |
| Agentic Coding | C | Runs well | 33.6 tok/s | 8381 ms | 4.8M |
| Reasoning | C | Runs well | 33.6 tok/s | 6810 ms | 4.8M |
| RAG | C | Runs well | 33.6 tok/s | 10476 ms | 4.8M |
How EXAONE 3.5 2.4B Instruct (2.4000000953674316B params) fits at each quantization level on RTX PRO 6000 Blackwell Workstation Edition 96GB (96.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 0.9 GB | Low | D39 |
Q3_K_S | 3 | 1.2 GB | Low | D39 |
NVFP4 | 4 |
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 startUpgrade options
Adds memory headroom for longer context windows and future model growth.
~$6,999 MSRP
NVIDIA DGX Spark 128GBNVIDIA upgrade
Yes, RTX PRO 6000 Blackwell Workstation Edition 96GB can run EXAONE 3.5 2.4B Instruct with a C grade (Runs well). Expected decode speed: 33.6 tok/s.
EXAONE 3.5 2.4B Instruct (2.4000000953674316B parameters) requires approximately 12.5 GB of memory with Q4_K_M quantization.
The recommended quantization for EXAONE 3.5 2.4B Instruct is Q4_K_M, which balances quality and memory efficiency.
On RTX PRO 6000 Blackwell Workstation Edition 96GB, 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.
For coding workloads, EXAONE 3.5 2.4B Instruct on RTX PRO 6000 Blackwell Workstation Edition 96GB receives a C grade with 33.6 tok/s and 4.8M context.
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/hf-lmstudio-community--exaone-3-5-2-4b-instruct-gguf-on-rtx-pro-6000-blackwell-96gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
1.3 GB |
| Medium |
| D39 |
Q4_K_M | 4 | 1.5 GB | Medium | D39 |
Q5_K_M | 5 | 1.7 GB | High | D39 |
Q6_K | 6 | 2.0 GB | High | D39 |
Q8_0 | 8 | 2.6 GB | Very High | D39 |
F16Best for your GPU | 16 | 4.9 GB | Maximum | D39 |
On RTX PRO 6000 Blackwell Workstation Edition 96GB, EXAONE 3.5 2.4B Instruct can safely use up to 4.8M tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.