AMD192 GB VRAM5,300 GB/s$2.5/hr

SmolLM2 1.7B Instruct on AMD MI300X 192GB: Does It Fit, How Fast, What It Costs

SmolLM2 1.7B Instruct at FP16 needs 3.4 GB for its weights and 16.3 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.

Fits?
Yes, 1 GPU
16.3 GB needed / 192 GB available
Decode speed
1013
tok/s per stream
Aggregate
12159
tok/s @ 8 concurrent
Cost / 1M tokens
$0.0571
1× AMD MI300X 192GB
What changes as you add AMD MI300X 192GBs
Tensor-parallel degree vs VRAM, throughput, and cost. Cost scales linearly with GPU count; decode throughput scales near-linearly too, so cost per token stays roughly flat.
GPUsTotal VRAMVRAM neededDecode tok/sAggregate tok/sTTFTCost / 1M tokFits?
1× AMD192 GB16.3 GB10131215973.6 ms$0.0571✓
2× AMD384 GB16.3 GB17234134051.9 ms$0.0336✓
4× AMD768 GB16.3 GB344516536040.9 ms$0.0168✓
8× AMD1536 GB16.3 GB689066144035.5 ms$0.008399✓
Concurrency vs context length
On 1× AMD MI300X 192GB at FP16. KV cache scales linearly with context, so this is the number of users your GPUs can actually serve.
ContextConcurrent usersKV per requestFits?
4K234824.6 MB✓ fits
8K1171.61 GB✓ fits
33K296.44 GB✓ fits
66K1412.88 GB✓ fits
131K725.77 GB✓ fits
Other GPUs that run SmolLM2 1.7B Instruct
Same workload, same quantization — ranked by value (tokens per dollar-hour).
GPUGPUsAggregate tok/sCost / 1M tok
RTX 3090 24GB1×2147$0.0323
RTX 5090 32GB1×4111$0.0338
RTX 4090 24GB1×2312$0.0480
V100 SXM2 32GB1×2065$0.0673
A100 40GB SXM41×3556$0.0703
AMD MI325X 288GB1×13765$0.0706
Frequently asked questions

Can SmolLM2 1.7B Instruct run on AMD MI300X 192GB?

Yes. SmolLM2 1.7B Instruct at FP16 needs 16.3 GB including the KV cache, and AMD MI300X 192GB has 192 GB — so it fits on a single GPU.

How fast is SmolLM2 1.7B Instruct on AMD MI300X 192GB?

1013 tokens/sec per stream and 12159 tok/s aggregate with continuous batching at 8 concurrent users. Time to first token is 73.6 ms, and inter-token latency is 986.9 µs.

What does SmolLM2 1.7B Instruct on AMD MI300X 192GB cost per million tokens?

$0.0571 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.

How much VRAM does SmolLM2 1.7B Instruct need on AMD MI300X 192GB?

3.4 GB for the weights at FP16, plus 12.9 GB of KV cache for 8 concurrent requests at 8K context — 16.3 GB total. Every extra concurrent user at long context costs more, so leave headroom.

Is AMD MI300X 192GB the right choice for SmolLM2 1.7B Instruct?

It works. Compare $0.0571 per 1M tokens against the alternatives below — the cheapest option that fits the same workload is 2× RTX 3080 10GB at $0.0187, and the fastest is 1× Cerebras CS-3 (WSE-3) at 45882 tok/s.

Adjust the workload yourself

Same formulas, your context length, concurrency, and quantisation.

Open in calculator