AMD Instinct MI300X
The AMD Instinct MI300X is an enterprise-class GPU built on the CDNA 3 architecture, featuring 192 GB of memory with 5,200 GB/s of memory bandwidth and a 750 W TDP. It delivers 163.4 TFLOPS of FP32 performance and 1,307 TFLOPS of FP16 tensor performance. Currently available from 3 providers starting at $1.71/GPU/hour, with a market median of $2.80/GPU/hour across 6 configurations.
Hardware specifications
Same across all providers
Median price across all providers
3 offerings from 3 providers
Sorted by price ascending. Compare configurations side-by-side.
| Provider | Count | vCPU | RAM | Region | Per GPU hour | Total/hr | Action |
|---|---|---|---|---|---|---|---|
| ×1 | 24 | 283 GB | $2.390 | $2.39 | Launch | ||
TensorWaveCheapest | ×1 | — | — | $1.710 | $1.71 | Launch | |
Up to$500credit | ×1 | 24 | 283 GB | $2.390 | $2.39 | Launch | |
Up to$1credit | ×8 | 128 | 1.5 TB | From$2.781 +0.2% 30d | From$22.25 | Launch |
LLMs that fit on the AMD Instinct MI300X
Estimated VRAM for popular open-weight LLMs at 16-bit (FP16/BF16), 8-bit (FP8/INT8) and 4-bit (INT4/MXFP4) precision, against 192 GB per GPU. Showing the 12 largest that fit.
| Model | Parameters | 16-bit | 8-bit | 4-bit |
|---|---|---|---|---|
| DeepSeek V4 Pro DeepSeek | 1.6T 49B active | — Not released | — Not released | 1,038 GB 8× · 498 GB free |
| Kimi K2.5 Moonshot AI | 1T 32B active | — Not released | — Not released | 714 GB 4× · 54 GB free |
| GLM-5.2 Z.ai | 753B | 1,808 GB Tight on 8× · 1% spare | 994 GB 8× · 542 GB free | 579 GB 4× · 189 GB free |
| DeepSeek R1 DeepSeek | 671B 37B active | — Not released | 826 GB 8× · 710 GB free or 4× · 11% spare | 481 GB 4× · 287 GB free |
| DeepSeek V4 Flash DeepSeek | 284B | — Not released | — Not released | 192 GB Barely fits on 1× |
| Solar Open2 250B Upstage | 250B 15B active | 601 GB 4× · 167 GB free | 330 GB 2× · 54 GB free | 192 GB 2× · 192 GB free or 1× · 19% spare |
| Qwen3 235B-A22B Alibaba | 235B 22B active | 564 GB 4× · 204 GB free | 310 GB 2× · 74 GB free | 181 GB 1× · 11 GB free |
| Laguna-S 2.1 Poolside | 118B 8B active | 282 GB 2× · 102 GB free | 155 GB 1× · 37 GB free | 90 GB 1× · 102 GB free |
| gpt-oss-120b OpenAI | 117B 5.1B active | — Not released | — Not released | 78 GB 1× · 114 GB free |
| Qwen3.6 35B-A3B Alibaba | 35B 3B active | 86 GB 1× · 106 GB free | 47 GB 1× · 145 GB free | 28 GB 1× · 164 GB free |
| Qwen3 32B Alibaba | 32.8B | 79 GB 1× · 113 GB free | 43 GB 1× · 149 GB free | 25 GB 1× · 167 GB free |
| Gemma 4 31B Google | 31.3B | 75 GB 1× · 117 GB free | 41 GB 1× · 151 GB free | 24 GB 1× · 168 GB free |
Estimates: model weights plus 20% for KV cache and runtime overhead, at short context lengths. Long contexts and large batches need more. Rows marked “tight” hold the weights but not that full margin. A ×N figure is the total VRAM across N of these GPUs and makes no claim about interconnect throughput. How we estimate this · Start from a model instead
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