RTX 4000 Ada 20GBコスパ重視20 GB VRAM (+4)360 GB/s (+160)
C
Raises estimated decode speed by about 80%.59 tok/sデコード
Raises estimated decode speed by about 80%.
Adds memory headroom for longer context windows and future model growth.
〜$1,250 MSRP
Can EXAONE 3.5 7.8B Instruct run on NVIDIA A2 16GB?
YES — Runs Great
C50Usable
Estimated from fit model
EXAONE 3.5 7.8B Instruct needs ~8.5 GB VRAM. NVIDIA A2 16GB has 16.0 GB. With Q4_K_M quantization, expect ~33 tok/s.
Runtime: OllamaCapacity: RoomyBandwidth: Very lowStack: BasicBottleneck: Memory bandwidth
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) — 8.5 GB, 32.8 tok/s, Runs well
8.5 GB required16.0 GB available
53% VRAM used
Fit status
Runs well
Decode
32.8 tok/s
TTFT
5905 ms
Safe context
148K
Memory
8.5 GB / 16.0 GB
Memory breakdown
Weights4.8 GB
KV Cache0.9 GB
Runtime1.2 GB
Headroom1.6 GB
See how fast it feels
See how fast it feelsEXAONE 3.5 7.8B Instruct on NVIDIA A2 16GB
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: 32.8 tok/s decode · 5.9s TTFT (warm) · 82 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 | 32.8 tok/s | 3221 ms | 148K |
| Coding | C | Runs well | 32.8 tok/s | 5905 ms | 148K |
| Agentic Coding | C | Runs well | 32.8 tok/s | 8589 ms | 148K |
| Reasoning | C | Runs well | 32.8 tok/s | 6978 ms | 148K |
| RAG | C | Runs well | 32.8 tok/s | 10736 ms | 148K |
Quantization options
How EXAONE 3.5 7.8B Instruct (7.800000190734863B params) fits at each quantization level on NVIDIA A2 16GB (16.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 3.0 GB | Low | C47 |
Q3_K_S | 3 | 3.8 GB | Low | C47 |
NVFP4 | 4 | 4.4 GB | Medium | C48 |
Q4_K_M | 4 | 4.8 GB | Medium | C48 |
Q5_K_M | 5 | 5.6 GB | High | C49 |
Q6_K | 6 | 6.4 GB | High | C50 |
Q8_0Best for your GPU | 8 | 8.3 GB | Very High | C51 |
F16 | 16 | 16.0 GB | Maximum | F0 |
Get started
Copy-paste commands to run EXAONE 3.5 7.8B Instruct on your machine.
Run
lms load hf-lmstudio-community--exaone-3-5-7-8b-instruct-gguf && lms server startアップグレードオプション
EXAONE 3.5 7.8B Instructを快適に動かすハードウェア
RTX 4000 Ada 20GBコスパ重視RTX 3090 24GBコスパ最良24 GB VRAM (+8)936 GB/s (+736)
C
Raises estimated decode speed by about 233%.109.2 tok/sデコード
Raises estimated decode speed by about 233%.
Adds memory headroom for longer context windows and future model growth.
〜$1,499 MSRP
RTX PRO 4000 Blackwell 24GBNVIDIAアップグレード24 GB VRAM (+8)672 GB/s (+472)
C
Raises estimated decode speed by about 233%.109.2 tok/sデコード
Raises estimated decode speed by about 233%.
Adds memory headroom for longer context windows and future model growth.
〜$1,599 MSRP
Frequently asked questions
Can NVIDIA A2 16GB run EXAONE 3.5 7.8B Instruct?
Yes, NVIDIA A2 16GB can run EXAONE 3.5 7.8B Instruct with a C grade (Runs well). Expected decode speed: 32.8 tok/s.
How much VRAM does EXAONE 3.5 7.8B Instruct need?
EXAONE 3.5 7.8B Instruct (7.800000190734863B parameters) requires approximately 8.5 GB of memory with Q4_K_M quantization.
What is the best quantization for EXAONE 3.5 7.8B Instruct?
The recommended quantization for EXAONE 3.5 7.8B Instruct is Q4_K_M, which balances quality and memory efficiency.
What speed will EXAONE 3.5 7.8B Instruct run at on NVIDIA A2 16GB?
On NVIDIA A2 16GB, EXAONE 3.5 7.8B Instruct achieves approximately 32.8 tokens per second decode speed with a time-to-first-token of 5905ms using Q4_K_M quantization.
Can NVIDIA A2 16GB run EXAONE 3.5 7.8B Instruct for coding?
For coding workloads, EXAONE 3.5 7.8B Instruct on NVIDIA A2 16GB receives a C grade with 32.8 tok/s and 148K context.
What context window can EXAONE 3.5 7.8B Instruct use on NVIDIA A2 16GB?
On NVIDIA A2 16GB, EXAONE 3.5 7.8B Instruct can safely use up to 148K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
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