Llama 2 7B at FP16 needs 13.5 GB for its weights and 47.8 GB in total at 8K context with 8 concurrent users. That means it fits on a single AMD MI300X 192GB. Every figure is computed by tokcalc's open-source capacity model.
| GPUs | Total VRAM | VRAM needed | Decode tok/s | Aggregate tok/s | TTFT | Cost / 1M tok | Fits? |
|---|---|---|---|---|---|---|---|
| 1× AMD | 192 GB | 47.8 GB | 255.6 | 3067 | 202.9 ms | $0.2264 | ✓ |
| 2× AMD | 384 GB | 47.8 GB | 434.5 | 10427 | 116.8 ms | $0.1332 | ✓ |
| 4× AMD | 768 GB | 47.8 GB | 868.9 | 41708 | 73.4 ms | $0.0666 | ✓ |
| 8× AMD | 1536 GB | 47.8 GB | 1738 | 166832 | 51.7 ms | $0.0333 | ✓ |
| Context | Concurrent users | KV per request | Fits? |
|---|---|---|---|
| 4K | 83 | 2.15 GB | ✓ fits |
| 8K | 41 | 4.29 GB | ✓ fits |
| 33K | 10 | 17.18 GB | ✓ fits |
| 66K | 5 | 34.36 GB | ✓ fits |
| 131K | 2 | 68.72 GB | ✓ fits |
| GPU | GPUs | Aggregate tok/s | Cost / 1M tok |
|---|---|---|---|
| RTX 5090 32GB | 2× | 3526 | $0.0788 |
| AMD MI325X 288GB | 1× | 3472 | $0.2800 |
| A100 80GB SXM4 | 1× | 1157 | $0.2880 |
| Intel Gaudi 3 128GB | 1× | 1909 | $0.2909 |
| H100 SXM5 80GB | 1× | 1938 | $0.3583 |
| B200 SXM 192GB | 1× | 4629 | $0.3600 |
Yes. Llama 2 7B at FP16 needs 47.8 GB including the KV cache, and AMD MI300X 192GB has 192 GB — so it fits on a single GPU.
255.6 tokens/sec per stream and 3067 tok/s aggregate with continuous batching at 8 concurrent users. Time to first token is 202.9 ms, and inter-token latency is 3.9 ms.
$0.2264 per 1M output tokens at 1× AMD MI300X 192GB ($2.50/hr). That is the marginal hardware cost — add idle capacity, ops, and egress for the real figure.
13.5 GB for the weights at FP16, plus 34.4 GB of KV cache for 8 concurrent requests at 8K context — 47.8 GB total. Every extra concurrent user at long context costs more, so leave headroom.
It works. Compare $0.2264 per 1M tokens against the alternatives below — the cheapest option that fits the same workload is 8× RTX 3080 10GB at $0.0186, and the fastest is 2× Cerebras CS-3 (WSE-3) at 39347 tok/s.
Same formulas, your context length, concurrency, and quantisation.