❌Won't fitfp16 · 4K ctx · batch 1
Does RTX 6000 Ada run Llama 3.1 70B? No at fp16.
At fp16 precision, Llama 3.1 70B (70 B parameters) needs roughly 153.8 GB of VRAM. RTX 6000 Ada only has 48 GB, short by 105.8 GB. INT4 quantization brings it down to 41.1 GB — that fits.
321%
VRAM
Won't fit
Estimated VRAM (fp16)
153.8 GB
of 48 GB on RTX 6000 Ada· 105.8 GB short
Tok/sec
35
TFTT
1883ms
Power
189W
Rent on GPUniq marketplace
RTX 6000 Ada
Live prices from verified providers · billed hourly, no commitment.
Memory breakdown (fp16)
Model Weights
140.00 GBKV Cache
2.50 GBActivations
10.00 GBFramework Overhead
1.35 GBQuantization comparison
Lower precision = less VRAM with a small quality trade-off. Quality order: FP16 > INT8 > INT4.
| Precision | VRAM | Utilisation | Fits on RTX 6000 Ada? |
|---|---|---|---|
| FP16 (full precision) | 153.8 GB | 321% | ❌Won't fit |
| INT8 (8-bit) | 78.7 GB | 164% | ❌Won't fit |
| INT4 / Q4 (4-bit) | 41.1 GB | 86% | ⚠️Tight fit |
Frequently asked
- Can RTX 6000 Ada run Llama 3.1 70B?
- Not at fp16 — Llama 3.1 70B needs about 153.8 GB while RTX 6000 Ada has 48 GB. It fits at INT4 quantization (41.1 GB) with some accuracy trade-off.
- How much VRAM does Llama 3.1 70B use?
- About 153.8 GB at fp16, 41.1 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 70B on RTX 6000 Ada?
- 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 RTX 6000 Ada, enable Flash Attention 2.
- Where can I rent a RTX 6000 Ada?
- GPUniq aggregates live RTX 6000 Ada 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 70B
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