MacBook Pro M4 Pro 64GBコスパ重視
64 GB Unified (+32)
C
Adds memory headroom for longer context windows and future model growth.19.4 tok/sデコード
Adds memory headroom for longer context windows and future model growth.
〜$1,599 MSRP
Can EXAONE 4.0 32B run on AMD Instinct MI100 32GB?
YES — Tight Fit
C51Usable
Estimated from fit model
EXAONE 4.0 32B needs ~27.4 GB VRAM. AMD Instinct MI100 32GB has 32.0 GB. With Q4_K_M quantization, expect ~41 tok/s.
Runtime: llama.cppCapacity: TightBandwidth: HighStack: StandardBottleneck: 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) — 27.4 GB, 40.9 tok/s, Tight fit
27.4 GB required32.0 GB available
86% VRAM used
Fit status
Tight fit
Decode
40.9 tok/s
TTFT
4734 ms
Safe context
36K
Memory
27.4 GB / 32.0 GB
Memory breakdown
Weights19.5 GB
KV Cache3.8 GB
Runtime0.9 GB
Headroom3.2 GB
See how fast it feels
See how fast it feelsEXAONE 4.0 32B on AMD Instinct MI100 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: 40.9 tok/s decode · 4.7s TTFT (warm) · 102 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 | 40.9 tok/s | 2582 ms | 36K |
| Coding | C | Tight fit | 40.9 tok/s | 4734 ms | 36K |
| Agentic Coding | C | Runs with offload | 40.9 tok/s | 6887 ms | 36K |
| Reasoning | C | Tight fit | 40.9 tok/s | 5595 ms | 36K |
| RAG | C | Runs with offload | 40.9 tok/s | 8608 ms | 36K |
Quantization options
How EXAONE 4.0 32B (32B params) fits at each quantization level on AMD Instinct MI100 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 AMD Instinct MI100 32GB run EXAONE 4.0 32B?
Yes, AMD Instinct MI100 32GB can run EXAONE 4.0 32B with a C grade (Tight fit). Expected decode speed: 40.9 tok/s.
How much VRAM does EXAONE 4.0 32B need?
EXAONE 4.0 32B (32B parameters) requires approximately 27.4 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 AMD Instinct MI100 32GB?
On AMD Instinct MI100 32GB, EXAONE 4.0 32B achieves approximately 40.9 tokens per second decode speed with a time-to-first-token of 4734ms using Q4_K_M quantization.
Can AMD Instinct MI100 32GB run EXAONE 4.0 32B for coding?
For coding workloads, EXAONE 4.0 32B on AMD Instinct MI100 32GB receives a C grade with 40.9 tok/s and 36K context.
What context window can EXAONE 4.0 32B use on AMD Instinct MI100 32GB?
On AMD Instinct MI100 32GB, EXAONE 4.0 32B can safely use up to 36K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
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<iframe src="https://willitrunai.com/embed/hf-lgai-exaone--exaone-4-0-32b-gguf-on-instinct-mi100-32gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
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