Gemma 2 9B at FP16 needs 18.0 GB for its weights and 40.5 GB in total at 8K context with 8 concurrent users. That means it fits on a single AMD MI325X 288GB. 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 | 288 GB | 40.5 GB | 216.7 | 2600 | 260.3 ms | $0.3739 | ✓ |
| 2× AMD | 576 GB | 40.5 GB | 368.3 | 8840 | 145.5 ms | $0.2200 | ✓ |
| 4× AMD | 1152 GB | 40.5 GB | 736.7 | 35360 | 87.8 ms | $0.1100 | ✓ |
| 8× AMD | 2304 GB | 40.5 GB | 1473 | 141440 | 58.9 ms | $0.0550 | ✓ |
| Context | Concurrent users | KV per request | Fits? |
|---|---|---|---|
| 4K | 191 | 1.41 GB | ✓ fits |
| 8K | 95 | 2.82 GB | ✓ fits |
| 33K | 23 | 11.27 GB | ✓ fits |
| 66K | 11 | 22.55 GB | ✓ fits |
| 131K | 5 | 45.10 GB | ✓ fits |
| GPU | GPUs | Aggregate tok/s | Cost / 1M tok |
|---|---|---|---|
| RTX 5090 32GB | 2× | 2640 | $0.1052 |
| AMD MI300X 192GB | 1× | 2297 | $0.3024 |
| A100 80GB SXM4 | 1× | 866.7 | $0.3846 |
| Intel Gaudi 3 128GB | 1× | 1430 | $0.3885 |
| H100 SXM5 80GB | 1× | 1452 | $0.4784 |
| B200 SXM 192GB | 1× | 3467 | $0.4808 |
Yes. Gemma 2 9B at FP16 needs 40.5 GB including the KV cache, and AMD MI325X 288GB has 288 GB — so it fits on a single GPU.
216.7 tokens/sec per stream and 2600 tok/s aggregate with continuous batching at 8 concurrent users. Time to first token is 260.3 ms, and inter-token latency is 4.6 ms.
$0.3739 per 1M output tokens at 1× AMD MI325X 288GB ($3.50/hr). That is the marginal hardware cost — add idle capacity, ops, and egress for the real figure.
18.0 GB for the weights at FP16, plus 22.5 GB of KV cache for 8 concurrent requests at 8K context — 40.5 GB total. Every extra concurrent user at long context costs more, so leave headroom.
It works. Compare $0.3739 per 1M tokens against the alternatives below — the cheapest option that fits the same workload is 8× RTX 3080 10GB at $0.0248, and the fastest is 8× RTX 3080 10GB at 17916 tok/s.
Same formulas, your context length, concurrency, and quantisation.