AMD Instinct MI350X 288GBコスパ重視
288 GB VRAM (+32)8000 GB/s (+2000)
C
Raises estimated decode speed by about 34%.40.6 tok/sデコード
Raises estimated decode speed by about 34%.
〜$8,000 MSRP
Can K EXAONE 236B A23B run on AMD Instinct MI325X 256GB?
YES — Runs Great
C53Usable
Estimated from fit model
K EXAONE 236B A23B needs ~198.1 GB VRAM. AMD Instinct MI325X 256GB has 256.0 GB. With Q4_K_M quantization, expect ~30 tok/s.
Runtime: llama.cppCapacity: RoomyBandwidth: 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) — 198.1 GB, 30.4 tok/s, Runs well
198.1 GB required256.0 GB available
77% VRAM used
Fit status
Runs well
Decode
30.4 tok/s
TTFT
6363 ms
Safe context
49K
Memory
198.1 GB / 256.0 GB
Memory breakdown
Weights144.0 GB
KV Cache27.7 GB
Runtime0.9 GB
Headroom25.6 GB
See how fast it feels
See how fast it feelsK EXAONE 236B A23B on AMD Instinct MI325X 256GB
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: 30.4 tok/s decode · 6.4s TTFT (warm) · 76 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 | 30.4 tok/s | 3471 ms | 49K |
| Coding | C | Runs well | 30.4 tok/s | 6363 ms | 49K |
| Agentic Coding | C | Tight fit | 30.4 tok/s | 9256 ms | 49K |
| Reasoning | C | Runs well | 30.4 tok/s | 7520 ms | 49K |
| RAG | C | Tight fit | 30.4 tok/s | 11569 ms | 49K |
Quantization options
How K EXAONE 236B A23B (236B params) fits at each quantization level on AMD Instinct MI325X 256GB (256.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 92.0 GB | Low | C44 |
Q3_K_S | 3 | 115.6 GB | Low | C46 |
NVFP4 | 4 | 132.2 GB | Medium | C47 |
Q4_K_M | 4 | 144.0 GB | Medium | C48 |
Q5_K_M | 5 | 169.9 GB | High | C48 |
Q6_KBest for your GPU | 6 | 193.5 GB | High | C48 |
Q8_0 | 8 | 252.5 GB | Very High | F0 |
F16 | 16 | 483.8 GB | Maximum | F0 |
Get started
Copy-paste commands to run K EXAONE 236B A23B on your machine.
Run
lms load hf-lgai-exaone--k-exaone-236b-a23b-gguf && lms server startアップグレードオプション
K EXAONE 236B A23Bを快適に動かすハードウェア
Frequently asked questions
Can AMD Instinct MI325X 256GB run K EXAONE 236B A23B?
Yes, AMD Instinct MI325X 256GB can run K EXAONE 236B A23B with a C grade (Runs well). Expected decode speed: 30.4 tok/s.
How much VRAM does K EXAONE 236B A23B need?
K EXAONE 236B A23B (236B parameters) requires approximately 198.1 GB of memory with Q4_K_M quantization.
What is the best quantization for K EXAONE 236B A23B?
The recommended quantization for K EXAONE 236B A23B is Q4_K_M, which balances quality and memory efficiency.
What speed will K EXAONE 236B A23B run at on AMD Instinct MI325X 256GB?
On AMD Instinct MI325X 256GB, K EXAONE 236B A23B achieves approximately 30.4 tokens per second decode speed with a time-to-first-token of 6363ms using Q4_K_M quantization.
Can AMD Instinct MI325X 256GB run K EXAONE 236B A23B for coding?
For coding workloads, K EXAONE 236B A23B on AMD Instinct MI325X 256GB receives a C grade with 30.4 tok/s and 49K context.
What context window can K EXAONE 236B A23B use on AMD Instinct MI325X 256GB?
On AMD Instinct MI325X 256GB, K EXAONE 236B A23B can safely use up to 49K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
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