GPT-OSS 20B (MoE) at FP8 (E4M3) needs 20.9 GB for its weights and 22.6 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 | 22.6 GB | 1625 | 19500 | 120.9 ms | $0.0499 | ✓ |
| 2× AMD | 576 GB | 22.6 GB | 2763 | 66300 | 75.5 ms | $0.0293 | ✓ |
| 4× AMD | 1152 GB | 22.6 GB | 5525 | 265200 | 52.7 ms | $0.0147 | ✓ |
| 8× AMD | 2304 GB | 22.6 GB | 11050 | 1060800 | 41.4 ms | $0.007332 | ✓ |
| Context | Concurrent users | KV per request | Fits? |
|---|---|---|---|
| 4K | 2572 | 206.2 MB | ✓ fits |
| 8K | 1306 | 412.3 MB | ✓ fits |
| 33K | 330 | 1.61 GB | ✓ fits |
| 66K | 165 | 3.22 GB | ✓ fits |
| 131K | 82 | 6.44 GB | ✓ fits |
| GPU | GPUs | Aggregate tok/s | Cost / 1M tok |
|---|---|---|---|
| RTX 5090 32GB | 1× | 5824 | $0.0238 |
| RTX 3090 24GB | 1× | 2472 | $0.0281 |
| RTX 4090 24GB | 1× | 3276 | $0.0339 |
| AMD MI300X 192GB | 1× | 17225 | $0.0403 |
| V100 SXM2 32GB | 1× | 2925 | $0.0475 |
| A100 40GB SXM4 | 1× | 5038 | $0.0496 |
Yes. GPT-OSS 20B (MoE) at FP8 (E4M3) needs 22.6 GB including the KV cache, and AMD MI325X 288GB has 288 GB — so it fits on a single GPU.
1625 tokens/sec per stream and 19500 tok/s aggregate with continuous batching at 8 concurrent users. Time to first token is 120.9 ms, and inter-token latency is 615.4 µs.
$0.0499 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.
20.9 GB for the weights at FP8 (E4M3), plus 1.6 GB of KV cache for 8 concurrent requests at 8K context — 22.6 GB total. Every extra concurrent user at long context costs more, so leave headroom.
It works. Compare $0.0499 per 1M tokens against the alternatives below — the cheapest option that fits the same workload is 4× RTX 3080 10GB at $0.006615, and the fastest is 1× Cerebras CS-3 (WSE-3) at 65000 tok/s.
Same formulas, your context length, concurrency, and quantisation.