Can You Run a 70B Model on a 4GB GPU? AirLLM Says Yes

AirLLM's pitch is one sentence: "Run 70B model inference on a single 4GB GPU, without quantization, distillation or pruning." A Dev.to project write-up walks through how the library gets there — and what it does not measure.

The mechanism

The write-up frames the standard alternative bluntly: to run a 70B model you buy an 80 GB A100, or at minimum pair up two 40 GB cards. Otherwise you quantize and trade accuracy, or distill and trade capability.

AirLLM's claimed insight is that the GPU never needs the whole model resident — only the layer currently executing. Per the write-up, the model is split into per-layer shards on disk, each layer is streamed to the GPU at inference time, released immediately, and the next one loaded. Required VRAM drops from total model size to single-layer size, which the author puts at "tens to a few hundred megabytes." The library does this by inserting what the write-up calls a "layer scheduler" between the GPU and the model files.

The claim that makes it interesting to anyone sizing hardware: nothing about the model changes. No structural change, no weight quantization, no knowledge distillation — only how the model is loaded.

The numbers the project claims

Model	Claimed VRAM
Llama 3.x 70B (full precision)	4 GB
Llama 3.1 405B	8 GB
DeepSeek-V3 671B	12 GB
Kimi K3 2.8T	3.72 GB

Note the ordering: a 2.8T-parameter model listed below a 70B one. The write-up attributes that to sparse expert loading for MoE architectures, naming DeepSeek-V3 and Kimi K3 specifically. It does not publish the per-model math behind those two figures, so treat them as the project's claims rather than derived numbers you can check.

Two other optimizations get named. Prefetching overlaps disk I/O with GPU computation for higher throughput. And block-wise quantization, per the write-up, adds up to a 3× speed boost without much accuracy cost — worth noting because it is optional, and using it means the "no quantization" framing no longer applies to your setup.

Project facts

AirLLM is a Python library by independent developer Gavin Li (lyogavin), described as specializing in LLM inference optimization. The write-up lists 33,500+ GitHub stars, 3,500+ forks, Apache 2.0, and installation via pip install airllm.

What the write-up does not tell you

This is the gap to close before you plan around it. The piece publishes no tokens-per-second figures, no time-to-first-token, and no disk-throughput requirement — the three numbers that decide whether layer streaming is usable or merely possible. Streaming every layer from disk on every forward pass moves the bottleneck from VRAM to storage bandwidth, and nothing in the write-up quantifies where that lands on consumer hardware.

That is not a knock on the mechanism, which is straightforward. It does mean the VRAM table above answers "will it load" rather than "will it be fast enough," and those are different questions for anyone choosing between this and a rented GPU hour.

Who should test it

If your workload is batch and latency-tolerant — offline evaluation, document processing, synthetic data generation on a gaming card you already own — the VRAM claims are worth verifying against your own storage setup. If you are serving interactive requests, benchmark tokens per second on your actual disk before committing, because that is the number the write-up leaves open.

Either way, measure on your hardware rather than trusting the table. The claims here come from a project write-up, not an independent benchmark.