✅Runs easilyfp16 · 4K ctx · batch 1
Run Llama 3.1 8B on H100
At fp16 precision, Llama 3.1 8B (8.0 B parameters) needs roughly 19.5 GB of VRAM. H100 has 80 GB, leaving 60.5 GB of headroom — plenty of room for longer contexts and larger batch sizes. Expected throughput ≈ 128 tokens/sec on a single card.
24%
VRAM
Runs easily
Estimated VRAM (fp16)
19.5 GB
of 80 GB on H100· 60.5 GB free
Tok/sec
128
TFTT
521ms
Power
267W
Rent on GPUniq marketplace
H100
Live prices from verified providers · billed hourly, no commitment.
Memory breakdown (fp16)
Model Weights
16.00 GBKV Cache
0.50 GBActivations
2.00 GBFramework Overhead
1.04 GBQuantization comparison
Lower precision = less VRAM with a small quality trade-off. Quality order: FP16 > INT8 > INT4.
| Precision | VRAM | Utilisation | Fits on H100? |
|---|---|---|---|
| FP16 (full precision) | 19.5 GB | 24% | ✅Runs easily |
| INT8 (8-bit) | 10.5 GB | 13% | ✅Runs easily |
| INT4 / Q4 (4-bit) | 6.0 GB | 8% | ✅Runs easily |
Frequently asked
- Can H100 run Llama 3.1 8B?
- Yes. Llama 3.1 8B needs ≈ 19.5 GB VRAM at fp16 and H100 provides 80 GB. Expected throughput is 128 tokens/sec per GPU.
- How much VRAM does Llama 3.1 8B use?
- About 19.5 GB at fp16, 6.0 GB at INT4 (for a 4K context, batch size 1). Longer contexts add to KV cache size; larger batches increase activations.
- What's the fastest way to run Llama 3.1 8B on H100?
- Use a production inference engine like vLLM, SGLang, or TensorRT-LLM with paged attention — they cut KV cache 2–3× vs the naive estimate and batch multiple requests efficiently. For H100, enable Flash Attention 2.
- Where can I rent a H100?
- GPUniq aggregates live H100 offers from verified providers. You can deploy an instance in about a minute and pay hourly with no commitment.
Other GPUs for Llama 3.1 8B
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