Open weights no longer means runs locally — four releases in 72 hours proved it

On the evening of October 18 a poster on r/LocalLLaMA put four open-weight releases on one chart and drew a horizontal line at 24 GB. The thread is titled "72 hours, four big open weights drops. The only one that fits in 24GB came from NVIDIA, of all companies." It carried 812 points and 267 comments when the archive snapshot was taken, and the argument beneath it was not about benchmarks. It was about whether open weights still describes something a person can run.

All four releases are real, and all four are open in the ordinary sense: the tensors download without asking anyone. What shifted in those 72 hours is the distance between that fact and the assumption riding along with it. The four checkpoints total roughly 3.2 terabytes in their largest official form. One of them fits the card in the machine you are reading this on.

Four releases, one download that fits

The sizes below are every file in each repository, in decimal gigabytes, read from the Hugging Face API on October 20. They measure download size, not parameter count, and the two have stopped moving together.

Release	Total / active parameters	Official download	Fits 24 GB
DeepSeek-V4.1-Flash-1017	763B, mixed 8-bit	510.3 GB, 48 shards	No
Muse Spark Open 816B-A42B	816B / 42B	1,644 GB bf16; 533 GB NVFP4	No
Beam 501B-A23B	501B / 23B	1,003 GB bf16; 293.7 GB NVFP4	No
Nemotron 3.5 Bolt 74B-A9B	74B / 9B	24.1 GB, 90 shards	Yes, barely

Beam's smallest official checkpoint is the NVFP4 repository: 293.7 GB across 53 shards, the thread's 294 GB, against 1,003 GB for the bf16 tree it came from. Meta publishes no small Muse Spark Open at all — 1,644 GB bf16 across 65 shards, 533 GB for NVFP4 across 127 — so the figure near 490 GB that circulated with the release is not a download Meta offers. DeepSeek's 510 GB is the whole repository, shipped at 8-bit rather than bf16, which is why it is the one people are running.

Sparse models shrink the compute, not the download

A mixture of experts routes each token through a slice of its parameters, and that slice sets what a token costs in arithmetic — the number in the model name: 42B active, 23B active, 9B active. It does not set memory, because a router cannot select an expert whose weights were never loaded. All 501B of Beam stay resident so 23B can be read on any given step.

Sparsity bought speed and nothing in download size. Beam is a 501B model on disk and a 23B model only in the sense that it bills like one per token.

DeepSeek's index makes the point from the other side: 763 billion parameters, 557 billion stored as int8 and 204 billion as FP8, which is why they occupy 510 GB rather than the 1.5 TB a bf16 copy would need. Precision, not architecture, did that compressing.

Nemotron shows how badly a parameter count can be read off a file. The repository's index reports 22.4 billion parameters for a model NVIDIA calls 74B, because 20.9 billion of those entries are U8 tensors each packing four 2-bit weights into a byte. The architecture is 74B; the index is counting containers.

How a 24 GB card became a product category

NVIDIA, which spent the same week signing seven PC makers up for Windows machines on its RTX Spark platform and shipping DGX Spark boxes with as much as 128 GB of unified memory, still ended up the only vendor of the four building for the machine most people own. Bolt's 2-bit weights came out of quantization-aware training rather than a post-hoc conversion, which is the difference between a model that survives 2-bit and one that is merely smaller. Its bf16 sibling is 149.8 GB in 63 shards, a 6.2x reduction, and NVIDIA's card states the target was 24 GB rather than best quality at any size.

That is a hardware company optimising for the memory tier its customers bought, and a hardware company that benefits when the alternative to its 24 GB card is four 256 GB machines. Both readings are available. The first is the one with a file listing behind it.

The category matters more than the motive. A 24 GB card used to be a price tier: the same models, fewer at once, slower. It is now a separate catalogue, because three of this week's four releases cannot be made to fit by any quantisation their publishers offer. The line on the chart is not a budget. It is a compatibility boundary.

The arithmetic for 24, 48 and 128 GB

At four bits a parameter costs half a byte, so usable memory divided by 0.5 gives a rough ceiling in parameters. Reserve a gigabyte for the driver and CUDA context, another for the compositor.

Memory	Rough 4-bit capacity	Of these four, fits
24 GiB	about 45B parameters	Nemotron Bolt only
48 GiB	about 100B parameters	none
128 GiB	about 270B parameters	none

Beam's NVFP4 file, the smallest of the three big releases, is 293.7 GB — 273 GiB, more than one 128 GiB card holds before its cache is allocated. DeepSeek needs four such cards for weights alone.

Context is the line item that ambushes people, and it varies enormously here. Beam's published configuration has 52 layers with 8 key-value heads of width 128, which at fp16 is roughly 213 KB of cache per token: about 7 GB at 32K tokens, and roughly 223 GB at the million-token window the card advertises — more memory than the weights. Bolt goes the other way. Its configuration has 88 blocks, only 8 of them attention and 40 Mamba-2 state-space layers, with 2 key-value heads, so the cache is about 8 KB per token and a 128K context costs around a gigabyte.

Which is why the 24 GB fit is real but thin. The thread's 23.7 GB is 22.1 GiB, and a 24 GB card is 24 GiB, so about 1.9 GiB remains for the CUDA context, the driver and whatever the desktop already claimed. Bolt's cheap cache is what makes that survivable; a model with Beam's cache geometry at that weight size would have died at the first long prompt. The 24 GB claim is a claim about weights plus a short context.

What to check before you hit download

Four things, in this order. File size in GiB, not the parameter count in the name, because a 24 GB download is 22.4 GiB on a 24 GiB card and a fit can turn on less than that. Which repository you are pulling, since the gap between a project's bf16 and NVFP4 trees here runs from 1,003 GB down to 294 GB. The context budget, as bytes per token times the window you intend to use, because a model that fits at 8K and one that fits at 128K are different products. And the licence, which this week separated cleanly: Beam is Apache 2.0, DeepSeek MIT, Bolt OpenMDW 1.1, and Meta's Muse Model License asks for an attribution line and a separate agreement past a revenue threshold.

None of that is a reason to skip open weights. It is a reason to read the file listing instead of the announcement. The download button looks identical in all four cases, and it is the only part of the page that never exaggerates.

The DeepSeek release was the cheapest to run and the second largest to store; Meta's was the largest and the most restricted; NVIDIA's was the smallest and the only one engineered backwards from a memory budget. Expect more of the same: models sized for the datacenter, with a 24 GB derivative arriving later, from whoever sells the 24 GB card.