Falcon 3 10B Instruct at FP16 needs 20.0 GB for its weights and 30.7 GB in total at 8K context with 8 concurrent users. That means it fits on a single TPU v6e Trillium (32GB). 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× TPU | 32 GB | 30.7 GB | 26.62 | 319.4 | 424.5 ms | $0.6957 | ✓ |
| 2× TPU | 64 GB | 30.7 GB | 45.25 | 1086 | 230.6 ms | $0.4093 | ✓ |
| 4× TPU | 128 GB | 30.7 GB | 90.50 | 4344 | 130.3 ms | $0.2046 | ✓ |
| 8× TPU | 256 GB | 30.7 GB | 181.0 | 17376 | 80.1 ms | $0.1023 | ✓ |
| Context | Concurrent users | KV per request | Fits? |
|---|---|---|---|
| 4K | 17 | 687.2 MB | ✓ fits |
| 8K | 8 | 1.34 GB | ✓ fits |
| 33K | 2 | 5.37 GB | ✓ fits |
| 66K | 1 | 10.74 GB | ✓ fits |
| 131K | 0 | 21.47 GB | ✗ does not fit |
| GPU | GPUs | Aggregate tok/s | Cost / 1M tok |
|---|---|---|---|
| RTX 5090 32GB | 1× | 698.9 | $0.1987 |
| AMD MI300X 192GB | 1× | 2067 | $0.3360 |
| V100 SXM2 32GB | 1× | 351.0 | $0.3957 |
| A100 40GB SXM4 | 1× | 604.5 | $0.4136 |
| AMD MI325X 288GB | 1× | 2340 | $0.4155 |
| A100 80GB SXM4 | 1× | 780.0 | $0.4274 |
Yes. Falcon 3 10B Instruct at FP16 needs 30.7 GB including the KV cache, and TPU v6e Trillium (32GB) has 32 GB — so it fits on a single GPU.
26.62 tokens/sec per stream and 319.4 tok/s aggregate with continuous batching at 8 concurrent users. Time to first token is 424.5 ms, and inter-token latency is 37.6 ms.
$0.6957 per 1M output tokens at 1× TPU v6e Trillium (32GB) ($0.8000/hr). That is the marginal hardware cost — add idle capacity, ops, and egress for the real figure.
20.0 GB for the weights at FP16, plus 10.7 GB of KV cache for 8 concurrent requests at 8K context — 30.7 GB total. Every extra concurrent user at long context costs more, so leave headroom.
It works. Compare $0.6957 per 1M tokens against the alternatives below — the cheapest option that fits the same workload is 4× RTX 3080 10GB at $0.0551, and the fastest is 1× Cerebras CS-3 (WSE-3) at 7800 tok/s.
Same formulas, your context length, concurrency, and quantisation.