RTX PRO 5000 Blackwell 48GBコスパ重視48 GB VRAM (+16)
C
Adds memory headroom for longer context windows and future model growth.57.8 tok/sデコード
Adds memory headroom for longer context windows and future model growth.
〜$4,999 MSRP
Can EXAONE 4.0 32B run on RTX 5090 32GB?
YES — Tight Fit
C52Usable
Estimated from fit model
EXAONE 4.0 32B needs ~27.7 GB VRAM. RTX 5090 32GB has 32.0 GB. With Q4_K_M quantization, expect ~62 tok/s.
Runtime: OllamaCapacity: TightBandwidth: HighStack: 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) — 27.7 GB, 61.5 tok/s, Tight fit
27.7 GB required32.0 GB available
87% VRAM used
Fit status
Tight fit
Decode
61.5 tok/s
TTFT
3148 ms
Safe context
34K
Memory
27.7 GB / 32.0 GB
Memory breakdown
Weights19.5 GB
KV Cache3.8 GB
Runtime1.2 GB
Headroom3.2 GB
See how fast it feels
See how fast it feelsEXAONE 4.0 32B on RTX 5090 32GB
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: 61.5 tok/s decode · 3.1s TTFT (warm) · 154 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 | B | Runs well | 61.5 tok/s | 1717 ms | 34K |
| Coding | C | Tight fit | 61.5 tok/s | 3148 ms | 34K |
| Agentic Coding | C | Runs with offload | 61.5 tok/s | 4578 ms | 34K |
| Reasoning | C | Tight fit | 61.5 tok/s | 3720 ms | 34K |
| RAG | C | Runs with offload | 61.5 tok/s | 5723 ms | 34K |
Inference speed
EXAONE 4.0 32B inference speed — tokens per second by GPU & Mac
Estimated decode speed (tokens/sec) for EXAONE 4.0 32B at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~62 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 | 61.5 | Tight |
MacBook Pro M4 Max 128GB | 128 GB | Q4_K_M | 30.8 | Fits |
MacBook Pro M4 Max 64GB | 64 GB | Q4_K_M | 30.8 | Fits |
Mac Studio M3 Ultra 256GB | 256 GB | Q4_K_M | 28.5 | Fits |
Mac Studio M2 Ultra 128GB | 128 GB | Q4_K_M | 23.8 | Fits |
| RTX 4090 24GB | 24 GB | Q4_K_M | 23.2 | Heavy offload |
Mac Studio M1 Ultra 128GB | 128 GB | Q4_K_M | 22.5 | Fits |
RX 7900 XTX 24GB | 24 GB | Q4_K_M | 21.4 | Heavy offload |
| RTX 3090 24GB | 24 GB | Q4_K_M | 19.8 | Heavy offload |
MacBook Pro M4 Pro 48GB | 48 GB | Q4_K_M | 19.4 | Tight |
MacBook Pro M3 Max 64GB | 64 GB | Q4_K_M | 12.3 | Fits |
MacBook Pro M1 Max 64GB | 64 GB | Q4_K_M | 11.3 | Fits |
| RTX 4080 Super 16GB | 16 GB | Q4_K_M | 8.4 | Too big |
| RTX 4070 12GB | 12 GB | Q4_K_M | 2.9 | Too big |
| RTX 3060 12GB | 12 GB | Q4_K_M | 2.0 | Too big |
| RTX 4060 8GB | 8 GB | Q4_K_M | 2.0 | Too big |
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 4.0 32B (32B params) fits at each quantization level on RTX 5090 32GB (32.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 12.5 GB | Low | C47 |
Q3_K_S | 3 | 15.7 GB | Low | C49 |
NVFP4 | 4 | 17.9 GB | Medium | C49 |
Q4_K_M | 4 | 19.5 GB | Medium | C49 |
Q5_K_MBest for your GPU | 5 | 23.0 GB | High | C48 |
Q6_K | 6 | 26.2 GB | High | F0 |
Q8_0 | 8 | 34.2 GB | Very High | F0 |
F16 | 16 | 65.6 GB | Maximum | F0 |
Get started
Copy-paste commands to run EXAONE 4.0 32B on your machine.
Run
lms load hf-lgai-exaone--exaone-4-0-32b-gguf && lms server startアップグレードオプション
EXAONE 4.0 32Bを快適に動かすハードウェア
Frequently asked questions
Can RTX 5090 32GB run EXAONE 4.0 32B?
Yes, RTX 5090 32GB can run EXAONE 4.0 32B with a C grade (Tight fit). Expected decode speed: 61.5 tok/s.
How much VRAM does EXAONE 4.0 32B need?
EXAONE 4.0 32B (32B parameters) requires approximately 27.7 GB of memory with Q4_K_M quantization.
What is the best quantization for EXAONE 4.0 32B?
The recommended quantization for EXAONE 4.0 32B is Q4_K_M, which balances quality and memory efficiency.
What speed will EXAONE 4.0 32B run at on RTX 5090 32GB?
On RTX 5090 32GB, EXAONE 4.0 32B achieves approximately 61.5 tokens per second decode speed with a time-to-first-token of 3148ms using Q4_K_M quantization.
Can RTX 5090 32GB run EXAONE 4.0 32B for coding?
For coding workloads, EXAONE 4.0 32B on RTX 5090 32GB receives a C grade with 61.5 tok/s and 34K context.
What context window can EXAONE 4.0 32B use on RTX 5090 32GB?
On RTX 5090 32GB, EXAONE 4.0 32B can safely use up to 34K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
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