RTX A6000 48GBBudget pick48 GB VRAM (+16)
C
Adds memory headroom for longer context windows and future model growth.29.9 tok/s decode
Adds memory headroom for longer context windows and future model growth.
~$4,650 MSRP
EXAONE 4.0 32B needs ~27.7 GB VRAM. NVIDIA V100 32GB has 32.0 GB. With Q4_K_M quantization, expect ~31 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
Tight fit
Decode
30.9 tok/s
TTFT
6267 ms
Safe context
34K
Memory
27.7 GB / 32.0 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 | 30.9 tok/s | 3418 ms | 34K |
| Coding | C | Tight fit | 30.9 tok/s | 6267 ms | 34K |
| Agentic Coding | C | Runs with offload | 30.9 tok/s | 9116 ms | 34K |
| Reasoning | C | Tight fit | 30.9 tok/s | 7407 ms | 34K |
| RAG | C | Runs with offload | 30.9 tok/s | 11395 ms | 34K |
Inference speed
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.
How EXAONE 4.0 32B (32B params) fits at each quantization level on NVIDIA V100 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 |
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 startUpgrade options
RTX A6000 48GBBudget pickAdds memory headroom for longer context windows and future model growth.
~$4,650 MSRP
RTX PRO 5000 Blackwell 48GBBest valueRaises estimated decode speed by about 87%.
Adds memory headroom for longer context windows and future model growth.
~$4,999 MSRP
NVIDIA L40 48GBNVIDIA upgradeAdds memory headroom for longer context windows and future model growth.
~$5,500 MSRP
Yes, NVIDIA V100 32GB can run EXAONE 4.0 32B with a C grade (Tight fit). Expected decode speed: 30.9 tok/s.
EXAONE 4.0 32B (32B parameters) requires approximately 27.7 GB of memory with Q4_K_M quantization.
The recommended quantization for EXAONE 4.0 32B is Q4_K_M, which balances quality and memory efficiency.
On NVIDIA V100 32GB, EXAONE 4.0 32B achieves approximately 30.9 tokens per second decode speed with a time-to-first-token of 6267ms using Q4_K_M quantization.
For coding workloads, EXAONE 4.0 32B on NVIDIA V100 32GB receives a C grade with 30.9 tok/s and 34K context.
On NVIDIA V100 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.
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/hf-lgai-exaone--exaone-4-0-32b-gguf-on-v100-32gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview: