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Issue 001 · Fact-check ledger

Will it fit? The sources

Every claim in issue 001, with its source, the exact quote and the recomputed numbers.

This ledger lists every factual claim in Exploded View 001, “Will it fit?”. Each claim is tied to a source, a primary one wherever it exists, with the exact quote or data that supports it. Every number we calculated is recomputed step by step in §2. A second pass (§3) then read the issue as a skeptical editor and tried to disprove each claim. Estimates are labelled as estimates, and the corrections made before publishing are listed in §4.

Checked October 17, 2026 (US Eastern time). All sources were fetched that day.

Status key:

  • verified: an exact quote or primary data supports it.
  • verified (derived): arithmetic on verified inputs; shown in §2.
  • estimate: our own model, labelled as an estimate in the issue.
  • corrected: fixed before publishing; see §4.
  • removed: cut because it could not be supported.

Claim numbers skip #42–#47, which covered photos cut before publishing.

Sources (P = primary):

ID Source URL How it was read
S1 (P) Meta research blog https://research.meta.ai/blog/muse-spark-open Read in full
S2 (P) HF model card, Muse Spark Open NVFP4 https://huggingface.co/meta-models/Muse-Spark-Open-816B-A42B-NVFP4 Read in full
S3 (P) HF API file tree, both Muse repos (byte sizes) https://huggingface.co/api/models/meta-models/<repo>/tree/main API file list, sizes summed
S4 (P) Meta guide, "Muse Spark Open on Mac" https://dev.meta.ai/docs/cookbook/muse-spark-open-mac Read in full
S5 (P) Meta hardware platforms page https://dev.meta.ai/docs/muse-spark-open/hardware-platforms Read in full
S6 PyTorch blog https://pytorch.org/blog/muse-spark-open/ Read in full
S7 Axios, Oct 15, 2026 axios.com (Muse Spark Open story) Read in full
S8 (P) Apple Mac Studio tech specs https://www.apple.com/mac-studio/specs/ Read in full
S9 (P) NVIDIA, "Introducing NVFP4…" https://developer.nvidia.com/blog/introducing-nvfp4-for-efficient-and-accurate-low-precision-inference/ Read in full
S10 (P) Reflection serving guide https://developers.reflection.ai/serving-weights Read in full
S11 (P) HF Beam model card and API https://huggingface.co/reflection/Beam-1.0-501B-A23B Read in full, plus API
S12 (P) Black Forest Labs blog https://bfl.ai/blog/flux-3-image-klein Read in full
S13 Wikipedia, "Gigabyte" https://en.wikipedia.org/wiki/Gigabyte Read in full
S14 Hugging Face, "Mixture of Experts Explained" https://huggingface.co/blog/moe Read in full

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File sizes from the Hugging Face API need no quote. The byte counts are the data.


1. Claims ledger

A. The model

# Claim Source and exact support Status
1 Meta released the Muse Spark Open weights on October 15 S2: "Date of release: October 15th, 2026". S6 is dated 2026-10-15. S7 (Oct 15) reports the release. verified, 2 sources
2 816 billion parameters S1: "816B total parameters (42B active)". S6: "816B total/42B active parameter". S7: "it has 816 billion parameters". verified, 3 sources
3 42 billion active per token S1 and S6, as in #2. S2: "816B total / 42B active". verified, 3 sources
4 256 experts, 8 per token S2: "Number of Experts: 256 Experts Activated per Token: 8". verified, 1 primary source (see §3, F5)
5 1M-token context S1: "a 1M-token context window". S6: "context window of 1M tokens". verified, 2 sources
6 Runs on two Mac Studios with M5 Ultra, with MLX S2: "optimized to run on two Mac Studios with M5 Ultra using MLX". S4: "Two Mac Studio computers with M5 Ultra and 512 GB of unified memory each, or four with 256 GB each". S7: "run locally across two or four connected Mac Studios powered by Apple's M5 Ultra chips, using Apple's MLX framework". verified, 3 sources
7 Meta's Mac setup: 2 × 512 GB or 4 × 256 GB S4, quoted in #6. S5: Apple row lists 2×512 GB or 4×256 GB, NVFP4, MLX. verified, 2 sources
8 "The model doesn't fit in one Mac's memory." S4: "The model doesn't fit in one Mac's memory." verified, exact quote
9 "Four Macs serve fastest." S4: "Four Macs serve fastest." verified, exact quote
10 Every pair of Macs is linked directly over Thunderbolt 5 S4: "a direct Thunderbolt 5 link between every pair of Macs". verified
11 Meta's stated minimum: 700 GB (NVFP4) / 2 TB (BF16) GPU memory S2: "requires a GPU with at least 700GB of VRAM for NVFP4 and 2TB for BF16". verified (unit ambiguity: §3, F1)
12 Every weight sits in memory, even when a token does not use it S14: "all parameters need to be loaded in RAM, so memory requirements are high." verified, general MoE fact
13 Shared parts, such as attention, run for every token S14: "there are shared computations, such as self-attention, which is applied for all tokens." verified, general MoE fact. Not stated for Muse specifically, but 8/256 × 816B = 25.5B < 42B, which is consistent with shared weights.

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B. Files and sizes

# Claim Source and exact support Status
14 NVFP4 files are 533 GB = 496 GiB S3: sum of file sizes = 533,011,023,472 B. Derived in §2 (D1). verified (derived)
15 BF16 files are 1,644 GB = 1,531 GiB S3: sum = 1,644,333,393,864 B. D2. verified (derived)
16 Formula at 16 bits gives 1,632 GB D3. verified (derived)
17 NVFP4 = 4 bits + one FP8 scale per 16 values = 4.5 bits S9: "NVFP4 stores one 4-bit value plus minor overhead of one FP8 scale per 16 values (4.5 bits per value) and one FP32 per tensor second-level scaling factor." verified. The per-tensor FP32 is negligible, so it is left out.
18 Formula at 4.5 bits gives 459 GB; 427 GiB in Figure 2 "Formula" mode D4. verified (derived)
19 NVFP4 files average about 5.2 bits per weight; BF16 about 16.1 D5. verified (derived); denominator caveat in §3, F3
20 "The model card does not say why the files are bigger." S2 was read in full and does not explain it. verified (absence). This replaced an unsupported claim: see §4.
21 Formula was 14% below the published size D4: (533.0 − 459.0) / 533.0 = 13.9%. verified (derived)
22 1 GB = 10⁹ bytes for files; memory sizes are binary, 1 GiB = 2³⁰ bytes S13: "a memory module that is labeled as having the size "1 GB" has one gibibyte (1 GiB) of storage capacity." S13: JEDEC "quote the gigabyte as 1 073 741 824 bytes". S8 footnote 1 (storage): "1GB = 1 billion bytes". verified, 2 sources (Apple does not state the unit for memory: §3, F1)
23 Hugging Face shows GB (10⁹) The HF file page shows a 4,831,841,856 B shard (S3) as "4.83 GB", so it uses decimal units. verified

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C. Machines

# Claim Source and exact support Status
24 Mac Studio M5 Ultra: up to 512 GB of unified memory S8: "Configurable to: 256GB or 512GB (M5 Ultra with 36‑core CPU and 80‑core GPU)". S4: "M5 Ultra and 512 GB of unified memory each". verified, 2 sources. 512 GB needs the top 36-core chip.
25 M5 Ultra memory bandwidth 1.2 TB/s S8: "1.2TB/s memory bandwidth". S1: "1.2 TB/s of memory bandwidth is fast enough to read the small slice each token needs". verified, 2 sources
26 Figure 2 machine "M5 Max 128 GB" S8: "48GB, 64GB, or 128GB (M5 Max with 18‑core CPU and 40‑core GPU)". verified
27 Figure 2 machine "8 GB GPU" A generic class, not a product claim. Its use with FLUX matches S12's INT8 claim. n/a (illustrative)
28 Figure 2 "4 × 256 GB" machine S4 and S8. verified

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D. Speed (estimates)

# Claim Basis Status
29 About 27 GB read per token D6. Assumes the active weights are stored at the file's average bits. estimate, labelled
30 Ceiling of about 44 tokens per second D6, using S8's bandwidth. estimate, labelled "ceiling, not a benchmark"
31 If dense, about 2 tokens per second; 533 GB and 2.3/s in the Figure 3 toggle D7. estimate, labelled. The explainer shows 2.3 (2.25 to one decimal) and the email says "about 2". Both are rounded from the same value.
32 The ceiling ignores the context cache, compute and the link; real speed is lower; "We did not measure it." Our method statement. verified (true of our method)

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E. Other models in the issue

# Claim Source and exact support Status
33 Beam: 501B, open-weight, Apache 2.0 S11 card: "501B total parameters and 23B active parameters". Table: "Total Parameters 501.3B". S11 API: license: apache-2.0; safetensors total 501,267,612,352. verified, 2 views of one primary source (card plus API metadata)
34 Beam files: 273 GiB NVFP4, 468.72 GiB FP8, 933.81 GiB BF16 S10: "The NVFP4 model is 273 GiB." "FP8 model is 468.72 GiB." "BF16 model is 933.81 GiB." Cross-check: the S11 API NVFP4 repo sum is 293,634,877,368 B = 273.47 GiB. verified, 2 sources for NVFP4
35 Beam about 4.7 / 8.0 / 16.0 bits per weight D8. verified (derived)
36 FLUX 3 Image Klein: 5.6B parameters; 11.2 GB BF16; 5.6 GB FP8 S12: "5.6B parameter"; "11.2 GB in BF16 and 5.6 GB in FP8". verified, 1 primary source
37 Exactly 16 and 8 bits per weight D9. verified (derived)
38 INT8 build runs in as little as 8 GB of VRAM S12: "INT8 build… as little as 8GB of VRAM". verified
39 FLUX FP8 on an 8 GB GPU fits with 2.78 GiB free D9. verified (derived). Generic machine, reserve 0.

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F. Sizing rules of thumb

# Claim Basis Status
40 At 4.5 bits, 8B ≈ 4.5 GB and 70B ≈ 39 GB D10. verified (derived); weights only, says "then add room"
41 "Total parameters decide if it fits. Active parameters set the speed limit." #12 and #13, plus S1: "fast enough to read the small slice each token needs". verified as a simplification

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G. Publication

# Claim Source and exact support Status
48 Issue date: October 17, 2026 Publication metadata. n/a

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Total: 42 ledger entries (#1 to #41 and #48). 37 verified (10 of them by derivation, and 5 general-method statements: #12, #13, #20, #32, #41). 3 are labelled estimates (#29 to #31). 2 are n/a (#27, #48). 0 unverified claims remain in the issue or the explainer. Claims #49 to #51 were added in the final design (§5).


2. Recomputations (step by step)

GiB = 2^30 = 1,073,741,824 B. Every line was rechecked in Python.

  • D1. Muse NVFP4 file size. 533,011,023,472 B ÷ 10⁹ = 533.0 GB. 533,011,023,472 ÷ 1,073,741,824 = 496.405 GiB → "496 GiB".
  • D2. Muse BF16 file size. 1,644,333,393,864 ÷ 10⁹ = 1,644.3 GB. ÷ 2³⁰ = 1,531.405 GiB → "1,531 GiB".
  • D3. Formula at 16 bits. 816×10⁹ × 16 ÷ 8 = 1.632×10¹² B = 1,632 GB. The real file is 0.75% larger.
  • D4. Formula at NVFP4 (4.5 bits).
    • 816×10⁹ × 4.5 ÷ 8 = 4.59×10¹¹ B = 459 GB = 459e9 ÷ 2³⁰ = 427.48 GiB (Figure 2 shows "427").
    • Gap: (533.011 − 459) ÷ 533.011 = 13.9% → "14% below".
  • D5. Average bits per weight.
    • NVFP4: 533,011,023,472 × 8 ÷ 816×10⁹ = 5.2256 → "5.2".
    • BF16: 1,644,333,393,864 × 8 ÷ 816×10⁹ = 16.12 → "16.1".
  • Memory headroom.
    • One Mac: 512 − 496.405 = 15.59 → "16 GiB".
    • Two Macs: 1,024 − 496.405 = 527.59 → "528 GiB".
    • Figure 2 default (2 Macs, 64 GiB reserve): 1,024 − 496.405 − 64 = 463.6 → "464 GiB free" (the explainer shows this).
    • Figure 2, 1 Mac with 64 reserve: 512 − 496.405 − 64 = −48.4 → "48.4 GiB short" (the explainer shows this).
    • Figure 2, Muse BF16 on 2 Macs: 1,531.405 − 1,024 = 507.4 → "507 GiB too big".
  • D6. Speed ceiling (MoE).
    • Bytes per token = 42×10⁹ × 5.2256 ÷ 8 = 27.43 GB.
    • Ceiling = 1.2×10¹² ÷ 27.43×10⁹ = 43.74 → "44 tokens/s".
  • D7. Speed ceiling (if dense). All 533.011 GB per token. 1.2×10¹² ÷ 533.011×10⁹ = 2.25 tokens/s (the explainer shows "2.3/s"; the email says "about 2").
  • D8. Beam bits per weight (total 501.3B):
    • BF16: 933.81 × 2³⁰ = 1,002.67 GB → ×8 ÷ 501.3e9 = 16.00.
    • FP8: 468.72 GiB = 503.28 GB → 8.03.
    • NVFP4: 273 GiB = 293.13 GB → 4.68 → "4.7". With the API bytes (293.63 GB): 4.69.
    • Figure 2, 1 Mac: 512 − 273 − 64 = 175 GiB free (matches the explainer).
  • D9. FLUX 3 Image Klein.
    • BF16: 11.2×10⁹ × 8 ÷ 5.6×10⁹ = 16.0.
    • FP8: 5.6×10⁹ × 8 ÷ 5.6×10⁹ = 8.0.
    • FP8 in GiB: 5.6e9 ÷ 2³⁰ = 5.22 GiB.
    • On an 8 GiB GPU: 8 − 5.215 = 2.78 GiB free.
  • D10. Rules of thumb.
    • 8×10⁹ × 4.5 ÷ 8 = 4.5 GB.
    • 70×10⁹ × 4.5 ÷ 8 = 39.4 GB → "about 39 GB".
    • 533,011,023,472 × 8 = 4,264,088,187,776 bits = 4.26×10¹² → "about 4.3 trillion".
    • ÷ 2²⁰ (1,048,576) = 4,066,551 → "about 4.1 million" chips. With 10⁶ bits per chip it would be 4.26M, so the 2²⁰ basis matters.
  • Experts check. 8 ÷ 256 × 816B = 25.5B < 42B active, so about 16.5B are shared weights. This is consistent with #13 and is not published.

3. Second pass: trying to disprove each claim

A second pass read the issue and the explainer again as a skeptical editor trying to find errors. Findings:

  • F1. GB or GiB in Meta's "700GB"?
    • The model card does not say. 700 GB = 652 GiB; 700 GiB = 752 GB.
    • Either way, one 512 GiB Mac is below it and two Macs (1,024 GiB) are above it.
    • Action: quote "700 GB" as written, give no conversion, and make no claim that depends on the unit.
    • Apple's footnote "1GB = 1 billion bytes" refers to storage. Treating Mac memory as binary rests on industry practice (S13, JEDEC), not on an Apple statement. Low risk: a 512 GB memory configuration is built from binary-sized parts.
  • F2. The speed ceiling's assumptions.
    • (a) The active weights may not be at the 5.2-bit average. Attention, embeddings and shared experts may stay at higher precision, so the real bytes per token could be higher and the ceiling lower.
    • (b) With 2 Macs, each Mac holds half the model. In a pipeline, each token still crosses both Macs in sequence, so one Mac's bandwidth is a fair per-token ceiling. Tensor parallel could beat it, and Meta's "Four Macs serve fastest" hints at that. So the ceiling is not a strict upper bound for every setup.
    • (c) KV cache reads are left out, and they grow with context.
    • Action: the issue says "ceiling, not a benchmark", lists what it ignores, quotes "Four Macs serve fastest", and says "Real speed is lower. We did not measure it." The explainer says "Splitting the work across Macs can change it." This is acceptable as an estimate.
  • F3. Is 816B the full parameter count?
    • The BF16 files imply 1,644.3e9 ÷ 2 = 822.2B values at 16 bits, so the files hold about 6B more than the headline. These may be extra heads or encoders, or the headline may be rounded.
    • Using 822.2B as the denominator gives 5.19 bits instead of 5.23. Both round to "5.2", so the issue holds.
    • HF's "Model size" for the NVFP4 repo shows a packed tensor count (the "484B params" figure). That counts packed 4-bit pairs, not weights, so it was not used anywhere.
  • F4. "Up to 512 GB" availability. S8 lists 512 GB only for the 36-core/80-core M5 Ultra. Some press (Engadget, via search) reported that the 512 GB build ships later than the base models. The issue says "up to 512 GB" and makes no claim about availability. No change.
  • F5. "8 of 256 experts." S2 gives "Experts Activated per Token: 8" and does not say "per layer". In standard MoE, routing happens per MoE layer. The issue says "each token uses 8 of them", which matches the card's wording. No change. It has one primary source; no second source was found.
  • F8. "Hugging Face shows GB." A shard of 4,831,841,856 B shows as "4.83 GB" on the HF tree page, so HF uses decimal units. OK.
  • F9. The issue says "The published BF16 files are 1,644 GB. That is close." The gap is 0.75%. "Close" is fair.
  • F10. The FLUX "exactly 16 and 8 bits". This is exact only because BFL's own sizes are rounded to 11.2 / 5.6 GB. The real files will differ slightly. Wording kept, but note that it rests on BFL's rounded figures.
  • F11. Single-source items: #4 (experts), #36 to #38 (FLUX, BFL only) and #17 (NVFP4, NVIDIA only). All are first-party sources about their own product, which is the best available source. No independent second source exists yet for a two-day-old release.
  • F12. Wording "Does not fit" in the Figure 2 legend. This is a legend label, not a claim. OK.
  • F13. Search-result sources. An earlier draft cited Apple, NVIDIA and the Beam card from search results. All three were re-read in full for this ledger (S8, S9, S11), and the quotes above come from those pages.

4. Corrections made before publishing

  • Corrected the Muse Spark Open NVFP4 file size from about 480 GB to 533 GB (496 GiB). The earlier figure came from the formula, not the file. The Hugging Face API file sizes sum to 533,011,023,472 B (S3, D1).
  • Corrected the average bits per weight from about 4.7 to about 5.2, recomputed from the real bytes (D5).
  • Replaced an unsupported explanation of the size gap (“some parts stay at higher precision”) with “The model card does not say why the files are bigger.” No Muse source states the reason.
  • Corrected the speed ceiling from about 48 to about 44 tokens per second, now labelled as a ceiling. The earlier figure used the wrong bytes per token (D6).
  • Corrected the dense-model speed from about 2.5 to 2.25 tokens per second (about 2) (D7).
  • Made the units consistent: “One Mac has 512 GiB. 496 GiB of weights leave 16 GiB.”
  • Changed “the unit memory is sold in” to “the unit memory sizes use”. Memory is sold labelled “GB”, but the quantity is binary (S13).
  • Fixed the Meta guide link, which returned 404, to dev.meta.ai/docs/cookbook/muse-spark-open-mac.
  • Removed “Two years ago this needed a server room”: vague and unsourced.
  • Removed per-GPU bandwidth examples that were not sourced to vendor spec pages. Only Apple’s sourced 1.2 TB/s remains.
  • Dropped an HBM die photo whose licence could not be verified.

5. Claims added in the final design

  • #49 (Figure 1): “At 16 bits the formula is within 1% of the real file.” Per D3, (1,644.33 − 1,632) ÷ 1,644.33 = 0.75%. Status: verified (derived).
  • #50 (Figure 1): the bar lengths are scaled to the 1,644 GB file.
    • 1,632 ÷ 1,644.33 = 99.25%.
    • 459 ÷ 1,644.33 = 27.91%.
    • 533.01 ÷ 1,644.33 = 32.42%.
    • Status: verified (derived).
  • #51 (explainer): “the weights of Muse Spark Open… How does a model that big fit on a desk?” and the 8B / 70B rules of thumb. These are the same claims as #1, #6 and #40.

Interactive figures re-checked. Scripted states of Figures 2 and 3 were compared with §2, and all match. Examples:

  • 496 GiB and 464 GiB free; 48.4 GiB short; 15.6 GiB.
  • Formula mode 427 GiB.
  • Muse BF16 507 GiB too big.
  • Beam 273 GiB with 175 GiB free; 468.72 + 64 − 512 = 20.7 GiB short; 933.81 − 512 = 422 GiB too big.
  • FLUX 5.22 GiB with 2.78 GiB free; FLUX BF16 10.4 GiB (10.43 to one decimal).
  • 27 GB and 44/s; dense 533 GB and 2.3/s.