Qwen 3 30B-A3B (MoE) at FP8 (E4M3) needs 30.0 GB for its weights and 36.4 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 | 36.4 GB | 582.4 | 6989 | 818.1 ms | $0.0596 | ✓ |
| 2× RTX | 192 GB | 36.4 GB | 990.1 | 23762 | 424.2 ms | $0.0351 | ✓ |
| 4× RTX | 384 GB | 36.4 GB | 1980 | 95048 | 227.1 ms | $0.0175 | ✓ |
| 8× RTX | 768 GB | 36.4 GB | 3960 | 380191 | 128.6 ms | $0.008768 | ✓ |
| Context | Concurrent users | KV per request | Fits? |
|---|---|---|---|
| 4K | 163 | 412.3 MB | ✓ fits |
| 8K | 81 | 824.6 MB | ✓ fits |
| 33K | 20 | 3.22 GB | ✓ fits |
| 66K | 10 | 6.44 GB | ✓ fits |
| 131K | 5 | 12.88 GB | ✓ fits |
| GPU | GPUs | Aggregate tok/s | Cost / 1M tok |
|---|---|---|---|
| RTX 5090 32GB | 2× | 23762 | $0.0117 |
| AMD MI300X 192GB | 1× | 20670 | $0.0336 |
| A100 40GB SXM4 | 1× | 6045 | $0.0414 |
| AMD MI325X 288GB | 1× | 23400 | $0.0415 |
| A100 80GB SXM4 | 1× | 7800 | $0.0427 |
| Intel Gaudi 3 128GB | 1× | 12870 | $0.0432 |
Yes. Qwen 3 30B-A3B (MoE) at FP8 (E4M3) needs 36.4 GB including the KV cache, and RTX PRO 6000 Blackwell 96GB has 96 GB — so it fits on a single GPU.
582.4 tokens/sec per stream and 6989 tok/s aggregate with continuous batching at 8 concurrent users. Time to first token is 818.1 ms, and inter-token latency is 1.7 ms.
$0.0596 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.
30.0 GB for the weights at FP8 (E4M3), plus 6.4 GB of KV cache for 8 concurrent requests at 8K context — 36.4 GB total. Every extra concurrent user at long context costs more, so leave headroom.
It works. Compare $0.0596 per 1M tokens against the alternatives below — the cheapest option that fits the same workload is 4× RTX 3080 10GB at $0.005513, and the fastest is 1× Cerebras CS-3 (WSE-3) at 78000 tok/s.
Same formulas, your context length, concurrency, and quantisation.