EXAONE 3.5 2.4B Instruct needs ~19.5 GB VRAM. NVIDIA DGX Spark 128GB has 0 MB. With F16 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
5.3M
Memory
16.0 GB / 108.8 GB
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.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | F | Too heavy | 20.1 tok/s | 5243 ms | 4K |
| Coding | F | Too heavy | 20.1 tok/s | 9613 ms | 4K |
| Agentic Coding | F | Too heavy | 20.1 tok/s | 13983 ms | 4K |
| Reasoning | F | Too heavy | 20.1 tok/s | 11361 ms | 4K |
| RAG | F | Too heavy | 20.1 tok/s | 17478 ms | 4K |
How EXAONE 3.5 2.4B Instruct (2.4000000953674316B params) fits at each quantization level on NVIDIA DGX Spark 128GB (92.2 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 | 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 |
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-Optionen
Yes, NVIDIA DGX Spark 128GB can run EXAONE 3.5 2.4B Instruct at F16 quantization (Runs well). The recommended Q4_K_M requires 2.9 GB which exceeds available memory, but at F16 it needs only 19.5 GB. Expected decode speed: 33.6 tok/s.
EXAONE 3.5 2.4B Instruct (2.4000000953674316B parameters) requires approximately 2.9 GB at Q4_K_M quantization. On NVIDIA DGX Spark 128GB, it fits at F16 using 19.5 GB.
The recommended quantization is Q4_K_M, but on NVIDIA DGX Spark 128GB the best fitting quantization is F16, which uses 19.5 GB.
On NVIDIA DGX Spark 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 F16 quantization.
For coding workloads, EXAONE 3.5 2.4B Instruct on NVIDIA DGX Spark 128GB receives a F grade with 20.1 tok/s and 4K context.
On NVIDIA DGX Spark 128GB, EXAONE 3.5 2.4B Instruct can safely use up to 5.1M tokens of context at F16 quantization. The model's official context limit is —, but available memory constrains the safe maximum.
Not always. NVIDIA DGX Spark 128GB 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.
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-dgx-spark-128gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview: