Web edition scheduled for Sunday, October 25, 2026. This edition is browser-readable and will also appear through the site’s RSS feed. Email signup and email delivery are not active; publication here does not mean an email was sent.
The market in one minute
At our October 24 public-board check, the homepage highlighted four of 88 screened offers across RTX A6000, RTX 3090, RTX 4090 and Mac Studio families. That count is a changing saved-inventory snapshot, not a claim that all listings are currently available.
- RTX A6000 48GB: one refurbished listing was shown at $4,489.99 with $0 reported shipping.
- RTX 3090 24GB: one used listing was shown at $1,399 with shipping unknown.
- RTX 4090 24GB: one new listing was shown at $2,073.49 with shipping unknown.
- Mac Studio M2 Ultra 64GB: one used complete system was shown at $3,199 with shipping unknown.
Prices exclude tax. Unknown shipping is not free. Unified memory is shared with the CPU and operating system and is not dedicated GPU VRAM. Recheck the current screened board and the seller’s current page before making a decision.
What changed in the hardware map
HighVRAM added durable guides for three capacity decisions that were previously scattered across product pages:
- How much VRAM a 70B LLM needs, with a published Qwen2.5-72B artifact and explicit KV-cache arithmetic.
- 24GB versus 48GB VRAM, including one GPU versus two and CPU-offload tradeoffs.
- DGX Spark and ASUS Ascent GX10, separating 128GB unified capacity from bandwidth, Arm compatibility and measured speed.
New exact-model planning
The new dense-Qwen ladder now connects official artifact sizes to complete-workload memory: Qwen3-4B, Qwen3-8B, Qwen3-14B and Qwen3-32B. Each guide uses Qwen’s official files and architecture-derived KV-cache arithmetic rather than a parameter label alone.
One calculation worth keeping
Qwen’s official Qwen2.5-72B GGUF repository lists Q4_K_M at approximately 44GB. Using the model’s published 80 layers, 8 KV heads and 128 head dimension, an illustrative one-sequence FP16 KV payload at 8,192 token slots is 2.5GiB. At 32,768 slots it is 10GiB.
That calculation explains why “the 44GB file fits in a 48GB card” is not a safe conclusion. File size is not peak resident memory; cache, buffers, unit conversion and runtime behavior still matter. Use the LLM VRAM calculator for a planning range, then test the exact artifact.
Muse Spark research update
The newest research cluster separates Meta’s official Blackwell deployment requirement from the smaller alternate-quant Mac path. Start with the Muse Spark Open memory requirements, then use the evidence appropriate to the artifact you intend to run.
- Official NVFP4 path: the listed checkpoint files total 533.011GB and Meta states at least 700GB of GPU memory. The hardware decision guide maps the published NVIDIA configurations without treating nominal capacity as proof of performance.
- Deployment path: the Muse Spark Open runtime guide maps Meta’s current SGLang, vLLM, TensorRT-LLM and distributed-MLX references while keeping llama.cpp and Unsloth quantized artifacts separate.
- 512GB M3 Ultra path: the launch article separately reports a llama.cpp Q2_K route using about 304GB at 18 tokens/second and an MLX oQ3e route using about 344GB at approximately 225 tokens/second prefill and 21 tokens/second generation. The Mac evidence guide explains why this does not validate 384GB or replace the official NVFP4 requirement.
- Contribute a comparable result: the benchmark builder now stages the exact NVFP4, Q2_K and oQ3e workload identities without pre-filling hardware or measured outcomes.
If you are still evaluating hardware, the private browser watchlist can stage either the published GPU-family path or the 512GB Mac candidate path. Presets do not save until you explicitly choose Save.
Muse Glimmer reaches consumer VRAM tiers
Muse Glimmer 30B is a separate model family from Muse Spark Open. Meta’s official 16.8GB Q4_K_M build targets 24GB hardware; the 19.7GB dynamic Q4_K_XL build targets 32GB. Image input uses a separate 1.4GB perception encoder, optional DFlash speculative decoding adds a 1.6GB drafter, and BF16 remains a 64GB target. A nominal 16GB device is not an official full-GPU target.
- Check the official 24GB, 32GB and 64GB workload ladder
- Compare capacity candidates without invented speed rankings
- Follow the llama.cpp setup and smoke-test guide
- Stage an exact Q4_K_M or dynamic Q4 benchmark record
- Stage the private 24GB-or-32GB hardware preset
Capacity is only the first gate. Pin the model revision and artifact, disclose vision and DFlash, use llama.cpp b10353 or newer, start below the 131,072-token ceiling, and record actual peak memory and behavior.
Community contribution
No permission-cleared community benchmark is included this week. We omit the section rather than inventing a contribution. If you have a reproducible result, use the benchmark submission builder; it records model, quantization, context, prompt and generation phases so readers can evaluate what was actually tested.
Your next action
Pick one model you genuinely want to run. Record its exact artifact, quantization and intended context, then compare that requirement with the hardware coverage map. If you already own suitable hardware, run the workload test before shopping.
Market observations were checked October 24, 2026 and can change. Manufacturer and model sources are linked from the referenced guides. This edition contains no sponsored placement. Some marketplace links on the site are affiliate links.
A lighter read
The first three editions of Just One More GPU are now available. The comic covers the experiment that becomes a GPU collection, the cheap accelerator that needs a server, and the auction win that costs more than buy-it-now. Start with issue 1 or browse the archive.