MacBook Pro M3 Pro 18GBGünstige Wahl
18 GB Unified (+6)
C
This setup is broadly balanced for this model.33.6 tok/s Dekodierung
ca. $1,999 MSRP
Can EXAONE 3.5 2.4B Instruct run on RTX A2000 12GB?
YES — Runs Great
C46Usable
Estimated from fit model
EXAONE 3.5 2.4B Instruct needs ~4.1 GB VRAM. RTX A2000 12GB has 12.0 GB. With Q4_K_M quantization, expect ~34 tok/s.
Runtime: OllamaCapacity: RoomyBandwidth: LowStack: BasicBottleneck: 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) — 4.1 GB, 33.6 tok/s, Runs well
4.1 GB required12.0 GB available
34% VRAM used
Fit status
Runs well
Decode
33.6 tok/s
TTFT
5762 ms
Safe context
463K
Memory
4.1 GB / 12.0 GB
Memory breakdown
Weights1.5 GB
KV Cache0.3 GB
Runtime1.2 GB
Headroom1.2 GB
See how fast it feels
See how fast it feelsEXAONE 3.5 2.4B Instruct on RTX A2000 12GB
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
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.
Best improvement path
Performance by workload
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | C | Runs well | 33.6 tok/s | 3143 ms | 463K |
| Coding | C | Runs well | 33.6 tok/s | 5762 ms | 463K |
| Agentic Coding | C | Runs well | 33.6 tok/s | 8381 ms | 463K |
| Reasoning | C | Runs well | 33.6 tok/s | 6810 ms | 463K |
| RAG | C | Runs well | 33.6 tok/s | 10476 ms | 463K |
Inference speed
EXAONE 3.5 2.4B Instruct inference speed — tokens per second by GPU & Mac
Estimated decode speed (tokens/sec) for EXAONE 3.5 2.4B Instruct at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~46 tok/s. Speed is memory-bandwidth bound, so cards that fit the whole model in VRAM run far faster than ones that offload to system RAM.
| GPU / Mac | Memory | Quant | Speed (tok/s) | Fits? |
|---|---|---|---|---|
 RTX 5090 32GB | 32 GB | Q4_K_M | 45.6 | Fits |
| RTX 4090 24GB | 24 GB | Q4_K_M | 38.4 | Fits |
| RTX 4080 Super 16GB | 16 GB | Q4_K_M | 38.4 | Fits |
| RTX 3090 24GB | 24 GB | Q4_K_M | 33.6 | Fits |
| RTX 4070 12GB | 12 GB | Q4_K_M | 33.6 | Fits |
| RTX 3060 12GB | 12 GB | Q4_K_M | 33.6 | Fits |
| RTX 4060 8GB | 8 GB | Q4_K_M | 33.6 | Fits |
RX 7900 XTX 24GB | 24 GB | Q4_K_M | 33.6 | Fits |
MacBook Pro M4 Max 128GB | 128 GB | Q4_K_M | 33.6 | Fits |
Mac Studio M3 Ultra 256GB | 256 GB | Q4_K_M | 33.6 | Fits |
Mac Studio M2 Ultra 128GB | 128 GB | Q4_K_M | 33.6 | Fits |
Mac Studio M1 Ultra 128GB | 128 GB | Q4_K_M | 33.6 | Fits |
MacBook Pro M4 Max 64GB | 64 GB | Q4_K_M | 33.6 | Fits |
MacBook Pro M3 Max 64GB | 64 GB | Q4_K_M | 33.6 | Fits |
MacBook Pro M1 Max 64GB | 64 GB | Q4_K_M | 33.6 | Fits |
MacBook Pro M4 Pro 48GB | 48 GB | Q4_K_M | 33.6 | Fits |
Estimates for single-stream decoding at Q4_K_M; real tokens/sec varies with prompt length, context, batch size, and runtime build. Prompt processing (prefill) is faster than the decode figures shown here.
Quantization options
How EXAONE 3.5 2.4B Instruct (2.4000000953674316B params) fits at each quantization level on RTX A2000 12GB (12.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 0.9 GB | Low | C47 |
Q3_K_S | 3 | 1.2 GB | Low | C47 |
NVFP4 | 4 | 1.3 GB | Medium | C47 |
Q4_K_M | 4 | 1.5 GB | Medium | C47 |
Q5_K_M | 5 | 1.7 GB | High | C48 |
Q6_K | 6 | 2.0 GB | High | C48 |
Q8_0 | 8 | 2.6 GB | Very High | C49 |
F16Best for your GPU | 16 | 4.9 GB | Maximum | C52 |
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 startUpgrade-Optionen
Hardware, die EXAONE 3.5 2.4B Instruct gut ausführt
Frequently asked questions
Can RTX A2000 12GB run EXAONE 3.5 2.4B Instruct?
Yes, RTX A2000 12GB 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 4.1 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 RTX A2000 12GB?
On RTX A2000 12GB, 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 RTX A2000 12GB run EXAONE 3.5 2.4B Instruct for coding?
For coding workloads, EXAONE 3.5 2.4B Instruct on RTX A2000 12GB receives a C grade with 33.6 tok/s and 463K context.
What context window can EXAONE 3.5 2.4B Instruct use on RTX A2000 12GB?
On RTX A2000 12GB, EXAONE 3.5 2.4B Instruct can safely use up to 463K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
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