Llama 3 8B at FP16 needs 16.1 GB for its weights and 24.6 GB in total at 8K context with 8 concurrent users. That means it fits on a single AMD MI300X 192GB. 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 | 192 GB | 24.6 GB | 214.5 | 2574 | 236.0 ms | $0.2698 | ✓ |
| 2× AMD | 384 GB | 24.6 GB | 364.7 | 8752 | 133.4 ms | $0.1587 | ✓ |
| 4× AMD | 768 GB | 24.6 GB | 729.3 | 35008 | 81.7 ms | $0.0793 | ✓ |
| 8× AMD | 1536 GB | 24.6 GB | 1459 | 140031 | 55.9 ms | $0.0397 | ✓ |
| Context | Concurrent users | KV per request | Fits? |
|---|---|---|---|
| 4K | 327 | 549.8 MB | ✓ fits |
| 8K | 163 | 1.07 GB | ✓ fits |
| 33K | 40 | 4.29 GB | ✓ fits |
| 66K | 20 | 8.59 GB | ✓ fits |
| 131K | 10 | 17.18 GB | ✓ fits |
| GPU | GPUs | Aggregate tok/s | Cost / 1M tok |
|---|---|---|---|
| RTX 5090 32GB | 1× | 870.3 | $0.1596 |
| V100 SXM2 32GB | 1× | 437.1 | $0.3177 |
| A100 40GB SXM4 | 1× | 752.8 | $0.3321 |
| AMD MI325X 288GB | 1× | 2914 | $0.3336 |
| A100 80GB SXM4 | 1× | 971.4 | $0.3432 |
| Intel Gaudi 3 128GB | 1× | 1603 | $0.3466 |
Yes. Llama 3 8B at FP16 needs 24.6 GB including the KV cache, and AMD MI300X 192GB has 192 GB — so it fits on a single GPU.
214.5 tokens/sec per stream and 2574 tok/s aggregate with continuous batching at 8 concurrent users. Time to first token is 236.0 ms, and inter-token latency is 4.7 ms.
$0.2698 per 1M output tokens at 1× AMD MI300X 192GB ($2.50/hr). That is the marginal hardware cost — add idle capacity, ops, and egress for the real figure.
16.1 GB for the weights at FP16, plus 8.6 GB of KV cache for 8 concurrent requests at 8K context — 24.6 GB total. Every extra concurrent user at long context costs more, so leave headroom.
It works. Compare $0.2698 per 1M tokens against the alternatives below — the cheapest option that fits the same workload is 4× RTX 3080 10GB at $0.0443, and the fastest is 1× Cerebras CS-3 (WSE-3) at 9714 tok/s.
Same formulas, your context length, concurrency, and quantisation.