MacBook Pro M4 Max 96GBBudget pick
96 GB Unified (+48)
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.
~$2,499 MSRP
EXAONE 3.5 2.4B Instruct needs ~7.4 GB VRAM. NVIDIA L20 48GB has 48.0 GB. With Q4_K_M quantization, expect ~38 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
38.4 tok/s
TTFT
5042 ms
Safe context
2.3M
Memory
7.4 GB / 48.0 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 | 38.4 tok/s | 2750 ms | 2.3M |
| Coding | C | Runs well | 38.4 tok/s | 5042 ms | 2.3M |
| Agentic Coding | C | Runs well | 38.4 tok/s | 7333 ms | 2.3M |
| Reasoning | C | Runs well | 38.4 tok/s | 5958 ms | 2.3M |
| RAG | C | Runs well | 38.4 tok/s | 9167 ms | 2.3M |
How EXAONE 3.5 2.4B Instruct (2.4000000953674316B params) fits at each quantization level on NVIDIA L20 48GB (48.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 0.9 GB | Low | C41 |
Q3_K_S | 3 | 1.2 GB | Low | C41 |
NVFP4 | 4 | 1.3 GB | Medium | C41 |
Q4_K_M | 4 | 1.5 GB | Medium | C41 |
Q5_K_M | 5 | 1.7 GB | High | C41 |
Q6_K | 6 | 2.0 GB | High | C41 |
Q8_0 | 8 | 2.6 GB | Very High | C41 |
F16Best for your GPU | 16 | 4.9 GB | Maximum | C41 |
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.
~$2,499 MSRP
Adds memory headroom for longer context windows and future model growth.
~$2,499 MSRP
Yes, NVIDIA L20 48GB can run EXAONE 3.5 2.4B Instruct with a C grade (Runs well). Expected decode speed: 38.4 tok/s.
EXAONE 3.5 2.4B Instruct (2.4000000953674316B parameters) requires approximately 7.4 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 NVIDIA L20 48GB, EXAONE 3.5 2.4B Instruct achieves approximately 38.4 tokens per second decode speed with a time-to-first-token of 5042ms using Q4_K_M quantization.
For coding workloads, EXAONE 3.5 2.4B Instruct on NVIDIA L20 48GB receives a C grade with 38.4 tok/s and 2.3M context.
On NVIDIA L20 48GB, EXAONE 3.5 2.4B Instruct can safely use up to 2.3M tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
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-l20-48gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview: