How much memory does Llama 3.3 70B Instruct need at Q4?

The Q4 figure used here is 42.5 GB. GGUF file size from the linked Hub repo. KV cache was not added, because this model's attention shape is not in the catalog.

What is the cheapest catalog device that runs Llama 3.3 70B Instruct at Q4?

Framework Desktop 64GB, list price $1,959.

How fast is Llama 3.3 70B Instruct on the GeForce RTX 4080 Super, as a bandwidth estimate?

About 17.3 tok/s at Q4. That is device bandwidth divided by active weight bytes, not a measured run.

No. Llama 3.3 70B Instruct does not fit on the GeForce RTX 4080 Super at Q4. The estimate is 42.5 GB and usable memory is 15.2 GB.

Can the GeForce RTX 4080 Super run Llama 3.3 70B Instruct?

Data updated 16 October 2026. Last reviewed the same day, when this catalog snapshot was written.

Usable memory on this 16 GB VRAM config is 15.2 GB (95% of the sticker figure). A result under 2 GB of headroom is labeled tight.

Quant ladder

Estimated memory for Llama 3.3 70B Instruct on GeForce RTX 4080 Super
Quant	Estimate	Headroom	Est. tok/s	Verdict
Q4	42.5 GB	-27.3 GB	17.3 tok/s	No
Q8	75.0 GB	-59.8 GB	9.82 tok/s	No
FP16	169 GB	-154.1 GB	5.22 tok/s	No

GGUF file size from the linked Hub repo. KV cache was not added, because this model's attention shape is not in the catalog.
Formula estimate: parameter count × bytes per weight × 1.20. No GGUF file size is stored for this quant.
Tokens/sec is a bandwidth roofline: GB/s divided by active weight GB. It is not a measured generation speed.

KV cache at 8K and 32K

No KV-cache estimate. The public config for this model was gated, missing, or not a GQA or MLA shape this catalog knows how to read.

Offload

About 27.3 GB of the Q4 weights sit outside usable VRAM. A partial offload would need at least that much free system RAM. This catalog does not record system RAM, so offload is not counted as a fit.

Ollama

This command pulls the Q4_K_M file from unsloth/Llama-3.3-70B-Instruct-GGUF, the Hub repo whose file size is used above. It is not an official Ollama library tag.

ollama run hf.co/unsloth/Llama-3.3-70B-Instruct-GGUF:Q4_K_M

What fits instead

Cheapest catalog device at Q4: Framework Desktop 64GB
Closest catalog model that still fits at Q4: gpt-oss-20b.

All hardware for Llama 3.3 70B Instruct · All models for the GeForce RTX 4080 Super

Questions

How much memory does Llama 3.3 70B Instruct need at Q4?: The Q4 figure used here is 42.5 GB. GGUF file size from the linked Hub repo. KV cache was not added, because this model's attention shape is not in the catalog.
Can the GeForce RTX 4080 Super run Llama 3.3 70B Instruct?: No. Llama 3.3 70B Instruct does not fit on the GeForce RTX 4080 Super at Q4. The estimate is 42.5 GB and usable memory is 15.2 GB.
What is the cheapest catalog device that runs Llama 3.3 70B Instruct at Q4?: Framework Desktop 64GB, list price $1,959.
How fast is Llama 3.3 70B Instruct on the GeForce RTX 4080 Super, as a bandwidth estimate?: About 17.3 tok/s at Q4. That is device bandwidth divided by active weight bytes, not a measured run.