Best Black Friday GPU Deals for Local LLMs in 2026: 12GB vs 16GB

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Step 0: diagnose before you shop

Black Friday and Cyber Monday fall on November 27 and November 30 in 2026, and GPU listings will be loud with percentage-off badges. None of them tell you whether a card runs the models you care about. So before price, answer one question: which model size do you actually run?

• 7-9B models (Llama 3.1 8B, Qwen3 8B, Gemma-class small models): any 12GB card runs these fully in VRAM with room for context. The budget pick is enough.
• 20B-class models (gpt-oss 20B, mixture-of-experts models with a few billion active parameters): the gpt-oss-20b model card sizes the model to run within 16GB of memory. A 16GB card holds it fully, with long context. A 12GB card runs it with short context or partial offload.
• 27-32B dense models at 4-bit: 16GB is the floor, and context will be tight. See the Qwen 3.6 27B on 16GB quant table.
• 70B and up: no single consumer card on this list is the answer; look at multi-GPU or unified-memory machines.

Your answer sets the VRAM tier. Inside a tier, memory bandwidth sets the speed, and only then does the discount matter. Most readers who run local models in 2026 land in the 16GB tier, and that is where the best overall pick sits: a card that runs gpt-oss 20B at nearly 130 tokens per second and keeps more than half that speed at 64K context.

Comparison table

Launch MSRP only. The live price buttons on each pick show the current price.

Specs per the TechPowerUp GPU Database (RTX 4070 Ti Super, RTX 5060 Ti 16GB, RX 9060 XT 16GB, RTX 4080, RTX 3060 12GB).

Top picks

🏆 Best Overall: GIGABYTE GeForce RTX 4070 Ti Super Eagle OC 16G

Verdict: Best balance of speed, VRAM and launch price for 20B-class local models.

Spec chips: 16GB GDDR6X · 256-bit · 672 GB/s · 285 W board power · CUDA

The GIGABYTE GeForce RTX 4070 Ti Super Eagle OC 16G pairs 16GB of VRAM with the most memory bandwidth you get below the RTX 4080 class. Per Hardware Corner's RTX 4070 Ti Super benchmarks, it runs gpt-oss 20B at 128.9 tok/s at 4K context and processes prompts at 5,628 tok/s. The context sweep from the same source shows how gracefully it degrades:

Source: Hardware Corner.

Pros

• About 40% faster than the RTX 5060 Ti 16GB at every context length in the sweep.
• Fast prefill makes long-document and retrieval workloads feel responsive.
• CUDA runs every major runtime: llama.cpp, Ollama, vLLM, ExLlamaV2.

Cons

• 285 W board power needs a stronger PSU than the entry cards.
• Same 16GB ceiling as the much cheaper RTX 5060 Ti; you pay for speed, not capacity.

See full details → · RTX 4070 Ti Super benchmarks

💰 Best Value: ASUS Dual GeForce RTX 5060 Ti 16GB OC

Verdict: The cheapest CUDA card that holds gpt-oss 20B with its full 128K context in VRAM.

Spec chips: 16GB GDDR7 · 128-bit · 448 GB/s · 180 W board power · CUDA

The ASUS Dual GeForce RTX 5060 Ti 16GB OC runs gpt-oss 20B at 92.1 tok/s at MXFP4 and 4K context, per Hardware Corner's RTX 5060 Ti 16GB page, which also reports the full 128K context fits in VRAM at 43.8 tok/s. GDDR7 gives it more bandwidth than the older RTX 4060 Ti 16GB, and that shows up directly in token generation.

Pros

• 16GB at the lowest NVIDIA launch price in the tier.
• 180 W board power suits compact builds and modest PSUs.
• Strong base for a later second card if you want 32GB across two GPUs.

Cons

• Prefill (3,585 tok/s at 4K per Hardware Corner) is well behind the RTX 4070 Ti Super.
• An 8GB RTX 5060 Ti shares the name; confirm "16GB" on the listing.

See full details → · RTX 5060 Ti 16GB benchmarks

🎯 Best for AMD/ROCm Builders: GIGABYTE Radeon RX 9060 XT Gaming OC 16G

Verdict: Most gpt-oss 20B tok/s per launch-MSRP dollar, if your tools support AMD.

Spec chips: 16GB GDDR6 · 128-bit · 320 GB/s · 160 W board power · ROCm / Vulkan

The GIGABYTE Radeon RX 9060 XT Gaming OC 16G posted 105.8 tok/s generation on gpt-oss 20B with llama.cpp's Vulkan backend, and 92.8 tok/s on the HIP/ROCm backend, in the llama.cpp performance discussion #15021. That is a single community result using llama-bench's short tg128 test, so treat it as indicative rather than a controlled comparison against the NVIDIA numbers above.

Pros

• At its $349 launch MSRP, the cited result works out to about 30 tok/s per $100, the best ratio on this list.
• 160 W board power, the lowest here.
• Vulkan backend in llama.cpp and LM Studio needs no ROCm install.

Cons

• vLLM, ExLlamaV2 and most fine-tuning tools assume CUDA.
• Only one cited gpt-oss source, and no published long-context sweep.

See full details → · RX 9060 XT 16GB benchmarks

⚡ Best Performance: ZOTAC Gaming GeForce RTX 4080 16GB Trinity OC

Verdict: The fastest card in the 16GB tier, for buyers who also game at 4K.

Spec chips: 16GB GDDR6X · 256-bit · 716.8 GB/s · 320 W board power · CUDA

The ZOTAC Gaming GeForce RTX 4080 16GB Trinity OC posts a median of 90.9 tok/s across 26 published 7-9B model runs from 15 sources, per the SpecPicks RTX 4080 benchmark page. On gpt-oss 20B, Hardware Corner's RTX 4080 page reports 136.5 tok/s at 4K context and 60.0 tok/s at 128K.

Pros

• Highest bandwidth on the list, so the fastest generation at every context length cited.
• A full 4K gaming card when it is not running models.

Cons

• Only about 6% faster than the RTX 4070 Ti Super on gpt-oss 20B at 4K, for a much higher launch price.
• Same 16GB ceiling; it does not open up larger models.
• The RTX 4080 is end-of-life per TechPowerUp, so stock is mostly remaining retail and resale.

See full details → · RTX 4080 benchmarks

🧪 Budget Pick: ZOTAC Gaming GeForce RTX 3060 Twin Edge OC 12GB

Verdict: The right card if you run 7-9B models and want to spend the least.

Spec chips: 12GB GDDR6 · 192-bit · 360 GB/s · 170 W board power · CUDA

The ZOTAC Gaming GeForce RTX 3060 Twin Edge OC 12GB posts a median of 59.5 tok/s across 41 published 7-9B model runs from 20 sources, per the SpecPicks RTX 3060 12GB benchmark page. That is plenty for chat with 8B-class models. gpt-oss 20B runs too, but with limited room for context; the gpt-oss 12GB community consensus covers the trade-offs. The MSI Gaming GeForce RTX 3060 12GB is the alternate SKU on the same chip.

Pros

• 12GB at the lowest launch price on the list.
• CUDA support for every runtime.

Cons

• Ampere-generation bandwidth caps speed on 20B-class models.
• Make sure the listing is the 12GB card; an 8GB RTX 3060 exists.

See full details → · RTX 3060 12GB benchmarks

What to look for in a Black Friday GPU for local LLMs

VRAM tier vs model size

VRAM decides what runs; everything else decides how fast. Match the tier to the Step 0 diagnosis above. Buying a faster 12GB card for 20B-class models gets you less than buying a slower 16GB card, because the 16GB card keeps the whole model and its context in VRAM.

Memory bandwidth

Token generation on local models is mostly a memory-read problem. In Hardware Corner's gpt-oss 20B results, the ordering of the NVIDIA cards follows bandwidth: 448 GB/s for the RTX 5060 Ti 16GB, 672 GB/s for the RTX 4070 Ti Super and 716.8 GB/s for the RTX 4080 line up with 92.1, 128.9 and 136.5 tok/s at 4K context. When two cards share a VRAM tier, compare bandwidth first.

8GB vs 16GB name collisions

The RTX 5060 Ti and RX 9060 XT each ship in 8GB and 16GB versions, and the RTX 3060 has an 8GB variant. Deal listings often lead with the chip name and bury the memory size. Read the full product title and spec table before you check out.

CUDA vs ROCm support

NVIDIA cards run every major local-inference runtime on CUDA. AMD's RDNA 4 cards work well in llama.cpp (Vulkan or HIP backends, per discussion #15021), but support in vLLM and training tools is narrower. If your workflow names a specific tool, check its AMD support page first.

PSU headroom

Rated board power runs from 160 W (RX 9060 XT) to 320 W (RTX 4080) on this list, per TechPowerUp. Check the card maker's recommended system power figure against your PSU's wattage and connectors; the RTX 4080 uses a 16-pin connector and TechPowerUp lists a suggested 700 W PSU for it.

Return window

Amazon's standard return window applies to most GPUs sold and shipped by Amazon, and holiday purchases can carry an extended return-by date. The exact date is shown in your order details; check it rather than assuming. Run a real inference load in the first days after delivery so any defect shows up well inside the window.

Common pitfalls on deal days

• Chasing the percentage badge. A large "off" figure is calculated against the listing's own reference price, which may sit above launch MSRP. Compare against the launch MSRPs in the table above instead.
• Buying a fast 8GB card for LLMs. An 8GB card at a great price still cannot hold gpt-oss 20B. VRAM tier comes first.
• Ignoring the case and PSU. The RTX 4070 Ti Super and RTX 4080 are triple-fan cards with higher board power than the entry picks. Check card length against your case and connectors against your PSU before checkout.
• Comparing benchmark numbers across runtimes. Ollama, llama.cpp CUDA, llama.cpp Vulkan and HIP report different results on the same card. The RX 9060 XT alone moves about 14% between Vulkan and HIP in the cited thread.
• Mixing up sellers. Third-party marketplace listings can carry different return terms and warranty paths from items sold by Amazon. Check who ships and sells the card.

Who should wait

Black Friday is a good time to buy if the card you want drops below its launch MSRP and below its own 60-90-day price range. It is not automatically a good time. Wait if:

• The card you want is still at or above launch MSRP during the sale, which means the "deal" is a list-price markdown.
• You are on a 12GB card, mostly run 7-9B models, and have no specific 20B workload yet. Your current card already covers that tier.
• You need more than 16GB. None of these cards changes that, and the answer is a different class of machine.

The live price buttons on this page show today's price. Compare them against the launch MSRPs in the table above before you decide.

Three worked examples

The 8B chat user. You run Llama 3.1 8B or Qwen3 8B in LM Studio for writing help and coding questions, with prompts under a few thousand tokens. The RTX 3060 12GB's median of 59.5 tok/s across 41 published 7-9B runs is several times faster than reading speed. Buy the budget pick only if it drops meaningfully below its usual range; otherwise the card you have may already be enough.

The gpt-oss 20B daily driver. You want gpt-oss 20B as your main local assistant, with long chat histories and pasted documents. That workload needs 16GB. The RTX 5060 Ti 16GB holds the full 128K context in VRAM and still generates 58.1 tok/s at 64K per Hardware Corner. The RTX 4070 Ti Super is the upgrade if you paste long inputs often, since its 4K-context prefill is about 57% faster.

The AMD-first tinkerer. You already use llama.cpp directly, you run Linux, and you do not need vLLM. The RX 9060 XT 16GB gives the best tok/s per launch-MSRP dollar on this list, provided its single cited result holds on your setup. Try both the Vulkan and HIP backends; the cited thread shows about a 14% generation gap between them on the same card.

Related guides

• GPU deals for local LLMs: 12GB vs 16GB
• Best 16GB GPU for local LLMs in 2026
• Best 12GB GPU for local LLMs in 2026
• gpt-oss 20B on 16GB GPUs: RTX 5060 Ti vs RX 9060 XT vs RTX 4070 Ti Super

Citations and sources

All sources accessed 2026-11-08.

• Hardware Corner — RTX 4070 Ti Super LLM benchmarks
• Hardware Corner — RTX 5060 Ti 16GB LLM benchmarks
• Hardware Corner — RTX 4080 LLM benchmarks
• llama.cpp GitHub discussion #15021
• Hugging Face — openai/gpt-oss-20b model card
• TechPowerUp GPU Database — RTX 4070 Ti Super
• TechPowerUp GPU Database — RTX 5060 Ti 16 GB
• TechPowerUp GPU Database — RX 9060 XT 16 GB
• TechPowerUp GPU Database — RTX 4080
• TechPowerUp GPU Database — RTX 3060 12 GB

This piece is editorial synthesis based on publicly available information. No independent first-party benchmarking is reported.

— Mike Perry