Gemma 4 26B-A4B on a 16GB GPU: RTX 5060 Ti 16GB vs RTX 3060 12GB Quant Table (2026)
A 16GB card holds a 4-bit build of the model entirely in VRAM. A 12GB card has to push experts out to system RAM.
By Mike Perry, Founder & Editor-in-Chief · Published 2026-11-11 · Updated 2026-11-11 · 16 min read
Quick Answer
Yes. A 16GB RTX 5060 Ti runs Gemma 4 26B-A4B fully in VRAM at the UD-IQ4_XS quant (13.6 GB per Unsloth's GGUF listing). One owner measured about 84 tok/s generation at that quant with a 65K context (njannasch.dev). A 12GB RTX 3060 can't hold a 4-bit build and has to offload experts to system RAM, which costs most of that speed.
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Gemma 4 26B-A4B is the model 16GB cards were made for. Google's model card lists 25.2B total parameters but only 3.8B active per token: 128 experts, 8 routed plus one shared expert per token, 30 layers and a 256K context window. That split matters for buyers. Generation speed tracks the 3.8B active slice, so a mid-range card produces tokens at small-model speed. The memory requirement still tracks the full 25.2B, though, so the whole file has to sit somewhere. A 16GB card is the cheapest NVIDIA tier where a 4-bit build fits entirely in VRAM.
That's why the RTX 5060 Ti 16GB keeps coming up in r/LocalLLaMA threads as the card people buy for local AI this autumn. It's also why owners of the still-popular RTX 3060 12GB ask whether the upgrade is worth it for this model. This page answers both questions using only published measurements. Each number cites where it came from, and where nobody has published a run, the page says so instead of estimating.
Key Takeaways - The largest 4-bit quant that fits 16GB is UD-IQ4_XS (13.6 GB). UD-Q4_K_M is 16.9 GB and does not fit a 16GB card with any working context (Unsloth). - On 12GB, only 2-bit and 3-bit builds load fully, and with little room for context. UD-IQ3_XXS is 11.4 GB and UD-Q2_K_XL is 10.5 GB. Anything at 4 bits needs expert offload to system RAM. - Context is cheap on this model. One RTX 5060 Ti owner ran a full 256K context in 13.2 GB of peak VRAM at IQ3_XXS with a q4_0 KV cache (njannasch.dev). - Who should buy which: buy the 5060 Ti 16GB if 26B-class MoE models are your main workload. Keep a 3060 12GB if you mostly run 7-14B dense models, where it holds its own.
Part of the RTX 3060 12GB local-LLM guide → Which models fit in 12 GB, each with its cited tok/s, and where two cards or RAM offload take over.
Dense 20-27B models need 14.3–16.7 GB for their Q4 weights; on the RTX 5060 Ti, the largest Q4 files in the class (16.7 GB) do not fit, but the smaller ones load: 24B at Q4 with a short context; 27B needs Q3 or offload. The model this article covers is a mixture-of-experts design, so its file size at the quant the article discusses, not this dense-class figure, decides whether it fits. RTX 5060 Ti carries 16 GB of VRAM. The weights column is the range of real Q4_K_M
files on Hugging Face for the models in each size class (about 0.6 GB per billion parameters),
and the runtime plus a usable context window wants about 2 GB on top; every tokens-per-second figure is a
median over community-reported Q4 runs SpecPicks tracks for this card, with the run count and
the source beside it.
Showing the model sizes this article covers and the band either side. Every size from 3B to 70B+, for every card SpecPicks tracks, is in the local-LLM GPU table.
Model size
Weights at Q4
Fits in 16 GB?
Measured
Left for context
Source
12-14B (Qwen 3 14B, Phi-4)Where 8 GB stops being enough. 12 GB is the smallest card that holds it with a usable context window.
20-27B (Gemma 3 27B, Mistral Small)A 27B Q4_K_M file (about 16.7 GB) is more than a 16 GB card holds, so 16 GB means a Q3 quant or partial CPU offload; a 24B loads with a short context. 20 GB holds the class with a usable context window, 24 GB with a long one.
14.3–16.7 GB
Partly: 24B at Q4 with a short context; 27B needs Q3 or offload
VRAM and quantization requirements: how much memory does each Gemma 4 26B-A4B quant need?
File sizes are Unsloth's published GGUF sizes, shown in decimal GB as Hugging Face lists them. A "16GB" card has 16,384 MiB, about 17.2 decimal GB. In practice about 15.5 GB is usable once the CUDA context and compute buffers load, which matches the usable figure the njannasch.dev write-up reports on a headless 5060 Ti. A "12GB" card has about 12.9 decimal GB, with roughly 11.5-12 GB usable.
"VRAM at 8K context" adds a working allowance for the KV cache and compute buffers. Gemma 4 uses a 1,024-token sliding window on most of its layers, so its KV cache is far smaller than a dense 26B model's would be. The allowance is an approximation, labelled as such, not a measurement.
Two things trip people up here. First, the 4-bit file most guides recommend, Q4_K_M, is 16.9 GB for this model. That is just under the 16GB card's raw capacity, but above what it can use once the runtime loads. IQ4_XS is the 4-bit build that fits. Second, the multimodal projector (mmproj, 1.19 GB at BF16) only loads if you want image input. Leave it off for text-only chat and you save over a gigabyte.
Benchmark table: tok/s on the RTX 5060 Ti 16GB vs RTX 3060 12GB
Single-user generation (tg) and prompt processing (pp) in llama.cpp. Every row names its source. None of these are SpecPicks measurements.
The RTX 3060 row is honest about a gap. As of November 2026, the SpecPicks benchmark database had no published, reproducible Gemma 4 26B-A4B run on a 3060 12GB, and the third-party "can I run it" pages that quote 8-26 tok/s are estimators, not measurements. The closest real data is the GTX 1080 offload run: an 8GB, 320 GB/s card held only part of the model and produced about 16-20 tok/s depending on how many expert layers stayed on the GPU. A 3060 has 4 GB more VRAM and more bandwidth, so it keeps more experts resident. Expect it to land at or above that reference, well short of the 5060 Ti's 84+ tok/s.
For models both cards hold fully, the aggregated medians in the SpecPicks benchmark database give the broader picture. The RTX 5060 Ti 16GB posts a 59.2 tok/s median on 7-9B models (n=13 runs, 9 sources). The RTX 3060 12GB posts 55.2 tok/s on 7-9B models (n=25 runs, 12 sources) and 28.7 tok/s on 12-15B models (n=14 runs, 6 sources). These are smaller-model baselines, not Gemma 4 26B figures.
Spec delta: what separates the two cards for MoE inference?
The bandwidth gap is only about 24%. The capacity gap is what decides this model. On a 16GB card every expert sits in fast GDDR7. On a 12GB card the experts that don't fit get read from DDR4 or DDR5 over PCIe for every token, which is an order of magnitude slower path. The 5060 Ti also uses a PCIe 5.0 x8 link and the 3060 a PCIe 4.0 x16 link. When the whole model is in VRAM the link only affects load time.
What happens on 12GB? Expert offload with llama.cpp --n-cpu-moe / -ot
llama.cpp's --n-cpu-moe N flag keeps attention, the shared expert and the routing on the GPU, and moves the routed expert tensors of the first N layers to system RAM. The older -ot (override-tensor) flag does the same with a regex. This is the right way to run an MoE model that doesn't fit, because the parts every token touches stay on the GPU and only the sparsely used experts move.
The published GTX 1080 run shows the tuning curve (dev.to (mdda)). That is an 8GB card with an i7-6700 and 32 GB of RAM, running Q4_K_M at 128K context:
--n-cpu-moe
Generation tok/s
Prompt tok/s
Note
29 (most experts on CPU)
15.6
52.7
stable
20
19.8
49.6
lowest setting that loaded without MTP
19
—
—
out of memory
21, with MTP draft
24.5
—
multi-token prediction enabled
Three practical lessons carry over to a 3060 12GB:
Lower N until it runs out of memory, then step back one. Each expert layer you keep on the GPU buys generation speed. With 4 GB more than the 1080, a 3060 can hold noticeably more of the 30 layers on the GPU.
Prompt processing collapses under offload. About 50 tok/s of prefill means a 4,000-token pasted document takes over a minute before the first output token. For retrieval and long-document work, that matters more than the generation figure.
The host matters. A Ryzen 7 5800X-class CPU with 32GB of dual-channel DDR4-3200 is a sensible floor for this setup. A 16GB system will page, because Q4_K_M is 16.9 GB on its own.
How does context length change the picture?
On the 16GB card, context barely costs memory. The njannasch.dev run started a 256K-token context at 13,151 MiB of VRAM with IQ3_XXS and a q4_0 KV cache, leaving headroom on a 16,384 MiB card. The cost shows up in speed instead, as the window fills:
Context filled
RTX 5060 Ti 16GB, IQ3_XXS (generation tok/s)
Ratio vs 20K
20K tokens
93
1.00x
50K tokens
69
0.74x
108K tokens
49
0.53x
Source: njannasch.dev. The same write-up warns against --swa-full, which makes llama.cpp allocate full-context KV for every layer and runs the card out of memory.
On the 12GB card the picture inverts. Under offload, generation is limited by expert reads from system RAM, so context length changes it less. The GTX 1080 reference produced 15.98 tok/s at 16K and 15.64 tok/s at 128K with the same offload setting. Expressed per context length against the 5060 Ti's IQ3_XXS figures:
About 16-20K filled: 93 tok/s on the 5060 Ti against 16-20 tok/s for the 8GB offload reference, roughly 4.5-6x.
About 108-128K filled: 49 tok/s against about 16-20 tok/s, roughly 2.5-3x.
These ratios compare a measured 5060 Ti with an 8GB offload reference, not with a 3060. A 3060 sits between the two, closer to the offload end.
Prefill vs generation: why MoE generation scales with memory bandwidth while prompt processing scales with compute
Generating one token means reading the active weights once: about 3.8B parameters, or roughly 2 GB at 4-bit. That's a bandwidth problem, and it's why a 448 GB/s mid-range card generates Gemma 4 26B-A4B at small-model speed. Prompt processing batches thousands of tokens through the same weights at once, which turns it into a matrix-math problem bound by tensor-core throughput. That's why the 5060 Ti's prefill on dense Qwen3 8B (2,965 tok/s at 4K) is about 1.75x the 3060's (1,697 tok/s), per Hardware Corner, even though its bandwidth lead is only 24%.
With MoE offload, prefill suffers twice. The CPU-resident experts have to be processed by the CPU or shuttled across PCIe for every batch. That's where the GTX 1080 reference's 50 tok/s prefill comes from, against roughly 2,000 tok/s on the 5060 Ti with everything in VRAM.
Perf-per-dollar and perf-per-watt
All ratios use launch MSRP ($429 for the RTX 5060 Ti 16GB, $329 for the RTX 3060 12GB) and board power from TechPowerUp. Street prices for both cards are far from MSRP in late 2026. Check the live price widget before applying any of this.
On models that fit both cards, the 3060 matches the 5060 Ti on dollars and trails slightly on watts. The 5060 Ti's case rests on the models that fit only its 16 GB, and Gemma 4 26B-A4B at 4-bit is the clearest example.
Which RTX 5060 Ti 16GB and RTX 3060 12GB cards to buy
PNY GeForce RTX 5060 Ti 16GB Dual Fan. A reference-clock two-fan card and usually the cheaper 16GB listing. It's the right pick for a dedicated inference box, where the extra clocks of OC models add little to bandwidth-bound generation. It's not the pick if you need a quiet card at sustained load in a small case. Larger coolers run quieter there.
ASUS Dual GeForce RTX 5060 Ti 16GB OC. A factory-overclocked two-fan design with ASUS's warranty and support. Choose it if you also game on the box and want the OC's few extra frames, or if the price gap to the PNY is small on the day. For pure LLM work the OC adds little, because GDDR7 bandwidth, not core clock, sets the generation ceiling.
MSI Gaming GeForce RTX 3060 12GB. MSI's Twin Fan Torx design. It's the right pick for a budget 7-14B inference box or a second card. Skip it as a new purchase for Gemma 4 26B-A4B specifically, because the model needs offload on 12 GB.
ZOTAC Gaming GeForce RTX 3060 Twin Edge OC 12GB. A compact two-fan card that suits small-form-factor builds. It's the same 12 GB and 360 GB/s as any 3060, so the same Gemma 4 limits apply. Buy it for size, not speed.
Verdict matrix
Get the RTX 5060 Ti 16GB if…
Gemma 4 26B-A4B, Qwen 3.6 35B-A3B or other 20-35B MoE models are your daily driver.
You paste long documents and need fast prompt processing, which collapses under offload.
You want 64K-256K context windows without fighting the memory limit.
Keep or buy the RTX 3060 12GB if…
Your workload is 7-9B models, or 14B at 4K-16K context, where the 3060 is about 80% as fast as the 5060 Ti (55.2 vs 69.2 tok/s on Qwen3 8B).
You already own one, run Gemma 4 26B-A4B only occasionally and can live with offload speeds.
Budget is the hard limit and your host has 32 GB or more of RAM to absorb offloaded experts.
Recommended pick: for a buyer whose target is Gemma 4 26B-A4B, the PNY RTX 5060 Ti 16GB is the card to get. It runs the UD-IQ4_XS build fully in VRAM at about 84 tok/s with a 65K window, and a 3-bit build at up to 256K context. A 3060 12GB can only reach either of those with offload. If you already own a 3060, try --n-cpu-moe tuning before you buy anything. If it's fast enough for your use, keep the money.
Common pitfalls
Downloading Q4_K_M because every guide says to. For this model it's 16.9 GB and won't fit a 16GB card. Use UD-IQ4_XS.
Loading the vision projector you don't use. The mmproj file adds 1.19 GB at BF16. Text-only chat doesn't need it.
Enabling --swa-full. It defeats Gemma's sliding-window KV savings, and the njannasch.dev write-up reports 8 of 10 configurations failing to start with it set.
Offloading whole layers with -ngl instead of experts with --n-cpu-moe. Moving attention to the CPU slows every token. Moving only experts keeps the hot path on the GPU.
Running offload on 16GB of system RAM. The OS, the runtime and the offloaded experts will not fit together, and the system will page.
This piece is editorial synthesis based on publicly available information. No independent first-party benchmarking is reported.
Small-model (7–9B) reference throughput
These figures are for 7–9B models, not the 20–27B models this article covers. The table holds model size fixed so the cards can be compared with each other; for the size in the title, use the article's own figures.
Median generation throughput at 7–9B models (Llama 3.1 8B, Qwen 3 8B), Q4 quantization, from community-reported runs SpecPicks tracks. Each row pools runs from different sources, runtimes and models in that class, so the rows are not a matched head-to-head; where the article compares cards on the same rig, its own figures are the like-for-like result. Street price is the second-lowest listing priced within the last 24 hours at 0.5–2× launch MSRP, so no single listing sets it, and a street figure well above MSRP says so in its cell; where too few listings pass that check the cell shows a dash and the launch MSRP, which is not comparable with the street figures. Rows marked for comparison are not the cards this article is about — they are included so the throughput column has something to be read against.
As an Amazon Associate, SpecPicks earns from qualifying purchases; we also earn on qualifying eBay purchases via the eBay Partner Network. Prices shown were last tracked at crawl time and may vary — check the listing for the current price.
📹 Watch a review
I'm still mad… but buy it anyway - RTX 3060 Review — Linus Tech Tips on YouTube
Frequently asked questions
Does Gemma 4 26B-A4B run faster than a dense model of the same file size?
Usually yes for generation. A mixture-of-experts model reads only its active experts for each token, so the bytes moved per token are closer to a 4B-class model than a 26B one. All weights still have to be resident somewhere, though, so the VRAM requirement follows the full file size while speed follows the active parameter count.
Can I run Gemma 4 26B-A4B on a 12GB RTX 3060 at all?
Yes, with partial offload. llama.cpp can keep the attention and shared layers on the GPU and push some expert tensors to system RAM with the --n-cpu-moe or override-tensor options. Generation slows because those experts are read over PCIe and system memory, so 32GB of dual-channel DDR4 or more and a recent llama.cpp build matter far more here than they do for a model that fits in VRAM.
Which quantization should a 16GB card use for Gemma 4 26B-A4B?
Start with the largest 4-bit quant that leaves about 1.5-2GB free for the KV cache at your working context length. For this model that is Unsloth's UD-IQ4_XS at 13.6 GB; the Q4_K_M build is 16.9 GB and does not fit a 16GB card. Drop to Q3_K_M or IQ3_XXS only if you need very long contexts, because quality loss rises below 4 bits.
Does the RTX 5060 Ti need a PCIe 5.0 motherboard for local LLMs?
No. The card uses a PCIe 5.0 x8 link, and on a PCIe 4.0 or 3.0 board it runs at half or a quarter of that bandwidth. When the whole model sits in VRAM, the link only affects load time. It matters only when you offload experts to system RAM, where an older board slows the shuttling of weights on each token.
Should I sell my RTX 3060 12GB to buy an RTX 5060 Ti 16GB for Gemma 4?
Only if 26B-class MoE models or 14B dense models at long context are your main workload. For 7-9B models, which already fit in 12GB, the 3060's 192-bit bus keeps it competitive, and the upgrade mostly buys headroom rather than speed. Check the live price on both cards before deciding, because street prices for both have moved well away from their launch MSRPs.
