Gemma 2 27B at FP8 (E4M3) needs 27.0 GB for its weights and 51.7 GB in total at 8K context with 8 concurrent users. That means it fits on a single RTX PRO 6000 Blackwell 96GB. 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× RTX | 96 GB | 51.7 GB | 64.71 | 776.5 | 7.12 s | $0.5366 | ✓ |
| 2× RTX | 192 GB | 51.7 GB | 110.0 | 2640 | 3.58 s | $0.3156 | ✓ |
| 4× RTX | 384 GB | 51.7 GB | 220.0 | 10561 | 1.80 s | $0.1578 | ✓ |
| 8× RTX | 768 GB | 51.7 GB | 440.0 | 42243 | 917.0 ms | $0.0789 | ✓ |
| Context | Concurrent users | KV per request | Fits? |
|---|---|---|---|
| 4K | 44 | 1.54 GB | ✓ fits |
| 8K | 22 | 3.09 GB | ✓ fits |
| 33K | 5 | 12.35 GB | ✓ fits |
| 66K | 2 | 24.70 GB | ✓ fits |
| 131K | 1 | 49.39 GB | ✓ fits |
| GPU | GPUs | Aggregate tok/s | Cost / 1M tok |
|---|---|---|---|
| RTX 5090 32GB | 2× | 2640 | $0.1052 |
| AMD MI300X 192GB | 1× | 2297 | $0.3024 |
| L40S 48GB | 2× | 1273 | $0.3055 |
| AMD MI325X 288GB | 1× | 2600 | $0.3739 |
| A100 80GB SXM4 | 1× | 866.7 | $0.3846 |
| Intel Gaudi 3 128GB | 1× | 1430 | $0.3885 |
Yes. Gemma 2 27B at FP8 (E4M3) needs 51.7 GB including the KV cache, and RTX PRO 6000 Blackwell 96GB has 96 GB — so it fits on a single GPU.
64.71 tokens/sec per stream and 776.5 tok/s aggregate with continuous batching at 8 concurrent users. Time to first token is 7.12 s, and inter-token latency is 15.5 ms.
$0.5366 per 1M output tokens at 1× RTX PRO 6000 Blackwell 96GB ($1.50/hr). That is the marginal hardware cost — add idle capacity, ops, and egress for the real figure.
27.0 GB for the weights at FP8 (E4M3), plus 24.7 GB of KV cache for 8 concurrent requests at 8K context — 51.7 GB total. Every extra concurrent user at long context costs more, so leave headroom.
It works. Compare $0.5366 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 2× Cerebras CS-3 (WSE-3) at 29467 tok/s.
Same formulas, your context length, concurrency, and quantisation.