Qwen 3 4B at FP16 needs 8.0 GB for its weights and 17.7 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 | 17.7 GB | 487.5 | 5850 | 132.3 ms | $0.1662 | ✓ |
| 2× AMD | 576 GB | 17.7 GB | 828.8 | 19890 | 81.3 ms | $0.0978 | ✓ |
| 4× AMD | 1152 GB | 17.7 GB | 1658 | 79560 | 55.7 ms | $0.0489 | ✓ |
| 8× AMD | 2304 GB | 17.7 GB | 3315 | 318240 | 42.8 ms | $0.0244 | ✓ |
| Context | Concurrent users | KV per request | Fits? |
|---|---|---|---|
| 4K | 463 | 618.5 MB | ✓ fits |
| 8K | 231 | 1.21 GB | ✓ fits |
| 33K | 57 | 4.83 GB | ✓ fits |
| 66K | 28 | 9.66 GB | ✓ fits |
| 131K | 14 | 19.33 GB | ✓ fits |
| GPU | GPUs | Aggregate tok/s | Cost / 1M tok |
|---|---|---|---|
| RTX 3090 24GB | 1× | 912.6 | $0.0761 |
| RTX 5090 32GB | 1× | 1747 | $0.0795 |
| RTX 4090 24GB | 1× | 982.8 | $0.1131 |
| AMD MI300X 192GB | 1× | 5168 | $0.1344 |
| V100 SXM2 32GB | 1× | 877.5 | $0.1583 |
| A100 40GB SXM4 | 1× | 1511 | $0.1654 |
Yes. Qwen 3 4B at FP16 needs 17.7 GB including the KV cache, and AMD MI325X 288GB has 288 GB — so it fits on a single GPU.
487.5 tokens/sec per stream and 5850 tok/s aggregate with continuous batching at 8 concurrent users. Time to first token is 132.3 ms, and inter-token latency is 2.1 ms.
$0.1662 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.
8.0 GB for the weights at FP16, plus 9.7 GB of KV cache for 8 concurrent requests at 8K context — 17.7 GB total. Every extra concurrent user at long context costs more, so leave headroom.
It works. Compare $0.1662 per 1M tokens against the alternatives below — the cheapest option that fits the same workload is 2× RTX 3080 10GB at $0.0441, and the fastest is 1× Cerebras CS-3 (WSE-3) at 19500 tok/s.
Same formulas, your context length, concurrency, and quantisation.