Yes — with 35.6 GB to spare

Llama 3.3 70B Instruct at Q4_K_M fits your H100 SXM entirely on the GPU at 8K context, at an estimated 51 tokens per second. Past 121K the KV cache pushes it over — quantise the cache to q8_0, or step down a quantisation, to go longer.

Fully on GPU 8K context Q4_K_M · 39.7 GB Llama 3.3 Community Released Dec 2024

Still the creative-writing favourite: consistent voice, takes direction. Needs 48 GB to sit comfortably on GPU at Q4.

What hardware do I need for Llama 3.3 70B Instruct? →

The VRAM budget

weights 39.7 GB

Weights 39.7 GB KV cache @ 8K 2.50 GB Runtime overhead 0.6 GB Free 35.6 GB of 78.4 GB

Quantisation ladder

Quant	Weights	Total @ 8K	Max context	Tok/s	Quality	Fit
Q8_0	69.9 GB	73.0 GB	25K	29	−0.1% ppl	Long context
Q6_K	53.9 GB	57.0 GB	76K	38	−0.4% ppl	Long context
Q5_K_M	46.6 GB	49.7 GB	99K	44	−0.8% ppl	Long context
Q4_K_M	39.7 GB	42.8 GB	121K	51	−1.9% ppl	Recommended
Q3_K_M	32.1 GB	35.2 GB	128K	63	−5.4% ppl	Long context
Q2_K	27.5 GB	30.6 GB	128K	74	−15% ppl	Long context

Quality is the published perplexity delta against f16 weights. Max context assumes an f16 KV cache; q8_0 roughly doubles it.

How to run it

Ollama llama.cpp LM Studio vLLM

terminal

$ ollama pull llama3.3:70b
$ OLLAMA_CONTEXT_LENGTH=8192 \
    ollama run llama3.3:70b

The default. One binary, a model registry, an OpenAI-compatible port. More on Ollama.

01Download is 39.7 GB. Keep it on an SSD — a first load off a spinning disk takes minutes.

02Close anything else holding VRAM. A browser with hardware acceleration can sit on 1–2 GB.

03There is room to go to 121K context on this card.

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