Southeast Asia GPU cost intelligence
Track cloud GPU prices across Southeast Asia in seconds.
Compare GPU rates across major providers, zoom into SEA regions fast, and spot the best launch options without opening 10 tabs.
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| Provider | GPU Model | Region | Instance Type | Price (USD/h) | Type | Confidence | Source | Updated |
|---|
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LLM model size ↔ GPU memory guide
Quick sizing reference for inference planning. Actual needs vary by context window, batching, and runtime stack.
| Model family | Size | Precision / quant | Approx VRAM needed | Single-GPU fit | Recommended setup |
|---|---|---|---|---|---|
| Llama / Qwen / Mistral class | 7B–8B | 4-bit | 6–8 GB | ✅ Yes | RTX 4060 Ti 16GB, RTX 3090, A10 |
| Llama / Qwen / Mistral class | 7B–8B | FP16 | 14–18 GB | ✅ Yes | RTX 4090, A5000, L4 |
| 13B–14B models | 13B–14B | 4-bit | 10–14 GB | ✅ Yes | RTX 3090/4090, A10, L40S |
| 13B–14B models | 13B–14B | FP16 | 26–32 GB | ⚠️ Depends | A40, A100 40GB, multi-GPU consumer rigs |
| Reasoning / coding mid-tier | 32B | 4-bit | 20–26 GB | ✅ Yes (24GB+) | RTX 4090, A5000/A6000, A100 40GB |
| Reasoning / coding mid-tier | 32B | FP16 | 60–70 GB | ❌ No | H100 80GB or 2×A100 40GB |
| Frontier open models | 70B | 4-bit | 40–48 GB | ⚠️ Tight | A100 80GB, H100 80GB, 2×24GB+ with tensor parallel |
| Frontier open models | 70B | FP16 | 140–160 GB | ❌ No | 2×H100 80GB or larger multi-GPU cluster |
| Mixture-of-Experts (MoE) | 8x7B / 8x22B | 4-bit | Varies widely (24–80+ GB) | ⚠️ Depends | Size by active params + KV cache, usually multi-GPU for production |
Rule of thumb: FP16 memory ≈ params × 2 bytes. 4-bit quant often cuts weight memory ~60–75%, but KV cache and serving overhead can dominate at long context.
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