Follow dated market reports, or save a private browser watchlist for the hardware you care about. HighVRAM does not require an account and this page does not send your watchlist to the server.
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Shopping in the mainstream tier? Use Best 16GB GPU for local AI? to compare RTX 4060 Ti, 5060 Ti, 5070 Ti and 5080 before saving the family and return cadence here.
Build a private hardware watchlist
Following published Muse Spark Open hardware paths? Read the B200, GB300, H200 and RTX PRO decision guide, then stage the preset below. It selects relevant NVIDIA families at 96GB+ per device; you must still confirm the published device count, at least 700GB aggregate memory, exact topology, runtime recipe and context target. Nothing is saved or transmitted until you explicitly choose Save.
Following the tested Muse Spark Open Mac path? Read the 512GB M3 Ultra evidence guide, then stage the anchored Mac Studio preset below. It records a 512GB unified-memory return criterion for the published Q2_K and oQ3e alternate-quant paths—not Meta’s official NVFP4 checkpoint, a 384GB recommendation, availability, performance or an alert. Confirm the exact Mac, chip, memory, artifact, revision, runtime, context, memory pressure and swap before saving. Nothing is saved or transmitted until you explicitly choose Save.
\nFollowing a local Muse Glimmer 30B path? Read the 24GB, 32GB and 64GB requirements guide, then stage the preset below. It selects consumer and workstation families with qualifying 24GB-or-larger configurations; choose the 32GB floor manually for the official 19.7GB dynamic Q4_K_XL target, and confirm the exact GPU, artifact, runtime, vision encoder, drafter and context before saving. Nothing is saved or transmitted until you explicitly choose Save.
Following a local Qwen3.8-27B GGUF path? Read the 16GB, 24GB, 32GB, 48GB and 80GB requirements guide, then stage the consumer/workstation preset below. It selects relevant 24GB and 32GB candidates for community Q4/Q5 artifacts; 24GB is the practical starting tier and 32GB leaves more headroom. The official FP8 path belongs in the 48–64GB tier. Confirm the exact artifact, runtime, context, modality, MTP and offload before saving. Nothing is saved or transmitted until you explicitly choose Save.
Following Qwen3.8-27B on Apple silicon? Read the Mac shared-memory guide, then stage the separate Mac Studio 32GB+ preset below. It records a return criterion only—not runtime support, fit, speed, availability or an alert. Confirm the exact Mac, chip, unified memory, artifact, runtime, memory pressure and swap before saving. Nothing is saved or transmitted until you explicitly choose Save.
Following hardware for Kimi K2.5? Read the 640GB–864GB capacity guide, then stage the distributed-hardware preset below. It selects candidate families at 80GB+ per device; you must still confirm enough devices for at least 640GB aggregate, topology, software support, and complete-system requirements. Nothing is saved or transmitted until you explicitly choose Save.
Shopping an RTX 3060 listing? Use the RTX 3060 8GB vs 12GB comparison, then stage the exact two-SKU preset below. It selects those two families and an 8GB minimum but does not save or transmit anything until you explicitly choose Save.
Buying for the smaller OpenAI open-weight model? Read the gpt-oss-20b GPU guide, then use the workload preset below to stage supported RTX 50-series families at 16GB+. The preset does not save or transmit anything.
What it does: this form remembers your preferred families, memory, condition and budget in this browser so you can refer to them later. It does not continuously scan listings, apply filters for you, send alerts, create an account, or transmit the saved choices to HighVRAM. Use the current board link in the saved summary and apply the matching filters yourself. You can optionally download a one-time calendar reminder; that file contains only the return date and site URL.
Following this 16GB Ada card? Read the RTX 4080 SUPER local-AI fit guide, then stage its exact watchlist preset below.
Following this 16GB Ada card? Read the RTX 4070 Ti SUPER local-AI fit guide, then stage its exact watchlist preset below.
Following this 12GB Ada card? Read the RTX 4070 SUPER local-AI fit guide, then stage its exact watchlist preset below.
No watchlist saved in this browser yet.
Planning NVIDIA datacenter hardware? Start with the H100/H200/B200/B300 decision hub, compare RTX PRO 6000 96GB with H100 80GB/94GB, compare RTX 6000 Ada 48GB with H100 80GB/94GB, compare RTX A6000 48GB with H100 80GB/94GB, review B200 180GB, then compare B300 and MI355X before saving a preset. The watchlist records your return criteria only; it does not verify availability or send alerts.
Watching the compact 16GB Intel path? Read the Arc Pro B50 local-AI guide, then use the combined B-Series preset or select the exact B50 family manually. Staging preserves the saved watchlist until explicit Save.
Watching the 32GB Intel partner-card path? Read the Arc Pro B65 local-AI guide, then use the combined B-Series preset or select the exact B65 family manually. Staging preserves the saved watchlist until explicit Save.
Watching the 32GB Intel flagship? Read the Arc Pro B70 local-AI guide, then use the B-Series preset or select the exact B70 family manually. Staging preserves the saved watchlist until explicit Save.
\n\nWatching the 24GB Intel path? Read the Arc Pro B60 local-AI guide, then use the combined B-Series preset or select the exact B60 family manually. Staging never replaces the saved watchlist until explicit Save.
\n\nChoosing an Intel workstation GPU for local AI? Read the Best Intel GPU for Local AI guide, then load the combined B-Series preset. It stages B50, B60, B65 and B70 at 16GB+ while preserving condition, budget and cadence until explicit Save.
\n\nChoosing across AMD consumer, workstation and data-center paths? Read the Best AMD GPU for Local AI guide, then load the combined AMD 24GB+ preset. It stages the available Radeon and Instinct family categories while preserving condition, budget and cadence until explicit Save. Radeon PRO W7900 does not have its own exact family control, so verify professional-GPU listings manually.
Planning AMD Instinct hardware? Use the MI300X/MI325X/MI350X/MI355X decision hub to compare the 192GB, 256GB and 288GB tiers, then review MI325X 256GB versus NVIDIA B200 180GB when both ROCm and CUDA platforms are viable. The watchlist records return criteria only; it does not verify software compatibility, inventory or performance.
How price-change reporting works
The inaugural report is a dated baseline. Later reports may describe a price change only when equivalent hardware, condition and cost treatment can be compared across snapshots. A new search result, disappeared listing or changed seller title is not automatically a market price movement.
RSS is currently the notification channel. Immediate per-listing email or SMS alerts are not active, and HighVRAM will not imply otherwise.
Start with the October 23 market baseline, browse the indexed Market Watch archive, or review the marketplace methodology.
Want a concise weekly selection of useful experiments, hardware developments and community discussions? See HighVRAM Weekly Digest and its signup status. Email signup is not open yet; RSS remains available.
Following an 80GB+ GLM-4.7-Flash path? Read the official checkpoint capacity guide, then load the staged preset above. It selects candidate families and an 80GB floor only; confirm exact usable memory, current runtime support, topology and complete-system requirements before saving.
A lighter reason to return
Just One More GPU is the site’s comic about local AI, home labs and upgrades that somehow become projects. Browse its issue archive. Comic pages are not currently announced by the main WordPress post feed, so bookmark the latest-edition page if you want to check back.
Current Mac Studio path: use the M5 Ultra 96GB–512GB local-AI guide before saving Mac Studio criteria in the private watchlist.
Choosing a 40GB, 48GB or 80GB Ampere return criterion? Review RTX A6000 vs A100, then load an A100 preset. A preset only stages the form; nothing changes in browser storage until Save.
Choosing between a 48GB Ada workstation and A100 40GB/80GB? Review RTX 6000 Ada vs A100, then load the existing exact preset. Presets do not write storage until Save.
Choosing a 48GB Ada server card or A100 40GB/80GB? Review L40S vs A100, then load an exact preset. Presets do not write browser storage until Save.
Choosing between L40S 48GB and H100 80GB/94GB? Review L40S vs H100, then load the existing exact preset. Presets do not write storage until Save.
Choosing A40 48GB or A100 40GB/80GB? Review A40 vs A100, then load an exact preset. Presets do not write storage until Save.
Choosing between A40 and RTX 6000 Ada at the same 48GB capacity? Review the direct comparison, then load either existing exact preset. Presets only stage the form and do not write browser storage until Save.
Choosing between RTX 5090 32GB and A40 48GB? Review the direct comparison, then load either existing exact preset. Presets only stage the form and do not write browser storage until Save.
Choosing between RTX 5090 32GB and A100 40GB/80GB? Review the direct comparison, then load the existing RTX 5090 or exact A100 capacity preset. Presets only stage the form and do not write browser storage until Save.
Choosing between RTX 5090 32GB and H100 80GB/94GB? Review the direct comparison, then load the existing RTX 5090 or exact H100 capacity preset. Presets only stage the form and do not write browser storage until Save.
Choosing between RTX 5090 32GB and H200 141GB? Review the direct comparison, then load the exact RTX 5090 or H200 preset. Presets only stage the form and do not write browser storage until Save.
Following the DGX Spark decision?
The DGX Spark preset selects only that family and the 128GB minimum while preserving your current budget, condition and cadence until you explicitly save. Compare RTX 3090 vs DGX Spark, RTX 4090 vs DGX Spark, RTX 5090 vs DGX Spark, DGX Spark vs Mac Studio, DGX Spark vs RTX PRO 6000, DGX Spark vs A100, DGX Spark vs H100, or DGX Spark vs H200 before saving. For the RTX 3090 pair, load the exact 24GB or DGX Spark 128GB preset; the preset stages one 3090 family, not an automatic 48GB two-card pool. For the RTX 4090 pair, load the exact 24GB or DGX Spark 128GB preset; the preset preserves condition, budget and cadence until Save. For the A100 pair, load the existing exact DGX Spark 128GB, A100 40GB or A100 80GB preset. For H200, use its exact 141GB preset. Presets only stage the form and do not write browser storage until Save. The DGX Spark 128GB label is shared system memory; A100 and H200 labels are dedicated device memory.
Following the 24GB CUDA decision? Read the Best 24GB GPU for local AI guide, then load the combined RTX 3090-or-4090 preset above. It selects both families and 24GB+ while preserving condition, budget and cadence; nothing is saved or transmitted until you explicitly use Save.
Following the 80GB NVIDIA decision? Read the Best 80GB GPU for local AI guide, then load the combined A100-or-H100 preset above. It selects the A100 and H100/H200 families plus 80GB+ while preserving condition, budget and cadence; nothing is saved or transmitted until you explicitly use Save. Confirm exact A100 or H100 identity because the family controls also cover other capacities.
\nFollowing the 32GB NVIDIA decision? Read the Best 32GB GPU for local AI guide, then load the combined RTX 5090-or-RTX 5000 Ada preset above. It selects both exact families and 32GB+ while preserving condition, budget and cadence; nothing is saved or transmitted until you explicitly use Save.
Following the 12GB NVIDIA decision? Read the Best 12GB GPU for local AI guide, then load the combined RTX 3060-or-RTX 5070 preset above. It selects both exact 12GB families while preserving condition, budget and cadence; nothing is saved or transmitted until you explicitly use Save. Confirm the RTX 3060 is the 12GB SKU.
Following the 8GB NVIDIA decision? Read the Best 8GB GPU for local AI guide, then load the combined RTX 4060-or-RTX 5060 preset above. It selects only those exact 8GB families and preserves condition, budget and cadence; nothing is saved or transmitted until you explicitly use Save.
Watching the cross-tier Blackwell decision? Read RTX 5070 12GB vs RTX 5060 Ti 16GB, then use the combined preset above. It stages only those two exact families and a 12GB minimum while preserving condition, budget and cadence until you explicitly save.
\nFollowing the RTX 5060 Ti capacity decision? Read RTX 5060 Ti 8GB vs 16GB for local AI, then load the combined preset above. It selects only those two exact SKUs with an 8GB minimum while preserving condition, budget and cadence; nothing is saved or transmitted until you explicitly use Save.
\nFollowing the RTX 4060 Ti capacity decision? Read RTX 4060 Ti 8GB vs 16GB for local AI, then load the combined preset above. It selects only those two exact SKUs with an 8GB minimum while preserving condition, budget and cadence; nothing is saved or transmitted until you explicitly use Save.
Following the RTX 3060 12GB versus RTX 4060 8GB decision? Read the capacity-versus-generation comparison, then load the combined preset above. It selects only those two exact families and an 8GB minimum while preserving condition, budget and cadence; nothing is saved or transmitted until you explicitly use Save.
Following the RTX 3060 12GB versus RTX 5060 8GB decision? Read the capacity-versus-Blackwell comparison, then load the combined preset above. It selects only those two exact families and 8GB+ while preserving condition, budget and cadence; nothing is saved or transmitted until you explicitly use Save.
\n\nFollowing the RTX 5080 versus RTX 5090 decision? Read the 16GB-versus-32GB Blackwell comparison, then load the combined preset above. It selects only RTX 5080 16GB and RTX 5090 with a 16GB minimum while preserving condition, budget and cadence; nothing is saved or transmitted until you explicitly use Save.
\n\nFollowing the RTX 4090 versus RTX 5080 decision? Read the 24GB-versus-16GB generation comparison, then load the combined preset above. It selects only RTX 4090 and RTX 5080 16GB with a 16GB minimum while preserving condition, budget and cadence; nothing is saved or transmitted until you explicitly use Save.
Follow RTX 4090 and RTX 5090 offers
Use the 24GB-versus-32GB flagship comparison, then stage both exact families at 24GB+. The preset changes this form only; it preserves condition, budget and cadence and does not write browser storage until you press Save.
Following the RTX 3090 versus RTX 5090 decision? Read the used-24GB-versus-current-32GB comparison, then load the combined preset above. It stages only RTX 3090 and RTX 5090 at 24GB+ while preserving condition, budget and cadence; nothing is saved or transmitted until you explicitly use Save.
Following the RTX 3090 versus RTX 5080 decision? Read the used-24GB-versus-current-16GB comparison, then load the combined preset above. It stages only RTX 3090 and RTX 5080 16GB at 16GB+ while preserving condition, budget and cadence; nothing is saved or transmitted until you explicitly use Save.
Watching the consumer AMD 24GB path? Read the RX 7900 XTX local-AI guide, then load the exact preset above. It stages only Radeon RX 7900 XTX 24GB at 24GB+ while preserving condition, budget and cadence; nothing is saved or transmitted until you explicitly use Save.
Following the 24GB Radeon-versus-CUDA decision? Read the RX 7900 XTX versus RTX 3090 comparison, then load the combined preset above. It stages only those two exact families at 24GB+ while preserving condition, budget and cadence; nothing is saved or transmitted until explicit Save.
Following the 24GB Radeon-versus-Ada flagship decision? Read the RX 7900 XTX versus RTX 4090 comparison, then load the combined preset above. It stages only those two exact families at 24GB+ while preserving condition, budget and cadence; nothing is saved or transmitted until explicit Save.
Watching both 32GB Intel tiers? Read the Arc Pro B65 versus B70 comparison, then select both exact families or use the combined B-Series preset. Staging preserves the saved watchlist until explicit Save.
Watching both Intel entry tiers? Read the Arc Pro B50 versus B60 comparison, then select both exact families or use the combined B-Series preset. Staging preserves the saved watchlist until explicit Save.
Watching the 24GB-versus-32GB Intel decision? Read the Arc Pro B60 versus B65 comparison, then select both exact families or use the combined B-Series preset. Staging preserves the saved watchlist until explicit Save.
Watching the two 24GB paths? Read the B60 versus RTX 3090 comparison, then select B60 and RTX 3090 manually below. This route does not alter or save your existing private watchlist.
Watching the two 32GB paths? Read the B70 versus RTX 5090 comparison, then select B70 and RTX 5090 manually below. This does not alter or save your existing private watchlist.
Watching the two 24GB paths? Read the B60 versus RTX 4090 comparison, then select B60 and RTX 4090 manually below. This does not alter or save your existing private watchlist.
Watching for a resident Llama 4 Scout BF16 candidate? Read the exact checkpoint and runtime-headroom guide, then load the 240GB+ preset above. It includes only families that can contain a qualifying configuration; confirm the exact usable memory, software support and topology. Maverick is intentionally omitted because its official FP8 and BF16 files generally require a topology-specific multi-accelerator plan that this offer watchlist cannot express. Loading a preset does not save or replace your browser watchlist.