Best GPU for Local AI: 5090 vs 3090 Reality

The best GPU for local AI is decided by VRAM and bandwidth. Capacity determines which quantized model loads; bandwidth divided by file size gives an upper bound on generation speed. A used RTX 3090 loads a 4-bit 32B model; an RTX 5090 loads the same model at 6-bit and writes roughly 1.8 times faster; a 70B file at 4 bits needs a 64 GB or 128 GB Mac.

Best GPU for Local AI: Two Numbers Decide Everything

The best GPU for local AI is decided by memory capacity and memory bandwidth, not by the model number on the cooler. Capacity sets the largest quantized file the card can load. Bandwidth sets the ceiling for token generation, because every generated token reads the active weights from memory once.

Run a 4-bit 7B Llama 2 file of about 3.82 GB through that logic and the arithmetic is simple: bandwidth divided by file size. The desktop RTX 5090 divides 1,792 GB/s by 3.82 GB for a ceiling near 469 tokens per second. Its measured figure on the same file is 290 tokens per second, about 62% of that ceiling.

That gap is the practical rule. Across the cards discussed here, measured generation lands between roughly 60% and 75% of the bandwidth-divided-by-file-size ceiling. The older Apple M4 Max sits lower, at about 58%, and no primary source in the material explains where the missing third goes. Overheads and imperfect memory use are a plausible guess, not a documented cause.

The figures used here come from two different kinds of source, and mixing them up causes bad buying decisions. Nvidia and Apple publish bandwidth in their own specifications, while token rates come from volunteer submissions to llama.cpp, the open-source C/C++ inference engine for local models. Those rows come from different builds and drivers, so the table is a shared scoreboard rather than a controlled laboratory.

Does NVIDIA's rumored RTX 5090 cancellation change anything?

The rumored RTX 5090 cancellation does not change capacity or bandwidth, so it changes nothing about which model fits or how fast tokens generate. It moves price and availability only. As of the rumor reports, Nvidia had not confirmed any end to production.

The reporting chain is worth reading carefully. WCCFTech cited two accounts on X and stated in its own coverage that there is no official confirmation. Tom's Hardware reported that Nvidia did not respond to a request for comment. A factory notice with no named company attached is not a product discontinuation.

Price quotes in the coverage disagree by thousands of dollars, which is itself a signal about reliability. The RTX 5090 launched at $1,999, and later tracking put it between roughly $2,400 and $4,000 in the United States in one roundup, while other coverage cited $5,000 to $9,600. A used RTX 3090 ranged from about $800 in January 2026 tracking to about $1,450 by October 2026.

The one durable claim in the rumor coverage concerns a future product. Reports describe a 24 GB RTX 5080 as targeted for the first quarter of 2027, with partners said to have only verbal notice. No bandwidth figure for that card has been published, so capacity alone tells you it would load what a 24 GB RTX 3090 already loads.

RTX 5090 vs 3090 vs 5090 laptop: measured generation speeds

On llama.cpp's shared benchmark, the desktop RTX 5090 generates 290 tokens per second, the desktop RTX 3090 generates 158.2, and the RTX 5090 laptop generates 156.5 — a near tie with the older desktop card. The 5090 desktop wins generation; the same two cards separate differently on prompt processing.

The laptop result looks wrong until you read the specs. Nvidia lists the laptop chip at 896 GB/s of memory bandwidth, exactly half the desktop RTX 5090's 1,792 GB/s, and the laptop part carries 24 GB of GDDR7 against the desktop's 32 GB. Its generation score is a little over half the desktop's 290, which tracks bandwidth almost exactly.

Prompt processing flips the ranking. The laptop scored 6,667 against the desktop 3090's 5,175, because prompt processing is limited by compute rather than bandwidth. Batching the prompt lets each weight be reused across many tokens, which keeps the compute units busy. Generation writes one token at a time, and each new token needs the active weights pulled from memory again.

Direct comparison of the desktop cards shows how closely price tracks bandwidth. The RTX 5090's bandwidth is about 1.91 times the 3090's, and its measured generation speed was about 1.83 times higher. Prompt processing widened the gap, with 14,073 against 5,175, roughly 2.7 times.

Treat these numbers as one workload, not as a forecast. The rows describe a 4-bit 7B Llama 2 file of about 3.8 GB from 2023, run on a public scoreboard of volunteer machines. Only the bandwidth and file-size logic carries over to a current 32B model or to mixture-of-experts architectures.

Capacity decides whether your model loads at all

Memory capacity decides whether a quantized model loads, and quantization is what makes local inference practical. Storing each weight in fewer bits shrinks the file, and because every token reads the file, a smaller file also raises the speed ceiling.

The same model changes size dramatically across quantization levels. A 70B model like Llama 3.3 shrinks from about 75 GB at 8 bits to 42.5 GB at 4 bits, with a 3-bit file around 34.3 GB and a 2-bit file near 26.4 GB. A 32B model like Qwen 3 runs about 19.8 GB at 4 bits, 23.2 GB at 5 bits, 26.9 GB at 6 bits, and 34.8 GB at 8 bits.

Map those files onto the cards and the choices narrow fast. A 24 GB card holds the 4-bit 32B file of about 19.8 GB and leaves roughly 4 GB for the context cache, which is snug rather than comfortable. A 32 GB card reaches the 6-bit file at 26.9 GB. The 8-bit 32B file at 34.8 GB fits neither.

The 70B file at 4 bits is where the shortlist breaks. At 42.5 GB it exceeds the RTX 5090's 32 GB and the RTX 3090's 24 GB, and even the 3-bit file at 34.3 GB misses 32 GB. Only the 2-bit files load, leaving little room for context. What 2-bit compression costs in answer quality is a separate question, and the honest test is your own prompts.

Where Apple Silicon and the GTX 1080 Ti fit

Apple Silicon fits 70B models because its unified memory pool is much larger, while a GTX 1080 Ti fits only small models but costs very little. The Mac trades writing speed for capacity; the 1080 Ti trades capacity for price.

Apple lists the M5 Max with up to 128 GB of unified memory, one pool shared by the processor and graphics. A 42.5 GB file therefore fits on the 64 GB and 128 GB machines. At up to 614 GB/s, dividing by the file gives a ceiling near 14 tokens per second, which is a calculation rather than a measurement; at about two-thirds of that ceiling the realistic figure is closer to nine or 10.

The 64 GB configuration leaves roughly 21 GB for the operating system and the context cache combined, which is why the 128 GB machine is the safer choice for long-context work. Apple's MLX research gives the cleanest evidence for the compute-versus-bandwidth split: moving from the M4 to the M5 raised memory bandwidth from 120 to 153 GB/s, about 28%, while token generation improved by 19% to 27%. Prompt processing jumped by up to four times, which Apple credits to new matrix hardware.

At the cheap end, a GTX 1080 Ti runs about $150 used with 11 GB and scores 62 tokens per second on the same benchmark, according to a February 2026 community post. That is faster than most people read, as long as the model fits in 11 GB. The 1080 Ti and the RTX 5090 both fail to open the 70B file, which is a kind of fairness.

Mixture-of-experts models break the bandwidth rule

Mixture-of-experts models generate tokens much faster than dense models of similar file size, because each token routes through only a few active experts instead of the whole network. Hardware Unboxed measured Qwen 3's 30B mixture-of-experts model at 234 tokens per second and the dense 32B model at 61, both at 4-bit class quantization with a 4K context.

An MoE model keeps all of its weights in memory but reads only the active ones per token. Capacity therefore follows the whole file while the speed ceiling follows the active bytes. A 30B model with 3 billion active parameters reads far less per token than a dense 30B model, which is where the roughly four times speed advantage comes from.

That comparison carries caveats. It comes from one independent site's test, and the quantization format differs from the dense rows on the shared scoreboard. The 60% to 75% planning band was derived from dense models only, so treat it as an upper-bound heuristic for MoE workloads rather than a rule. Still check the whole file against memory before buying around an MoE result.

How to size a GPU purchase in four steps

Sizing a local AI GPU purchase starts with the model file, not the card. Work through the following steps in order, because a model that does not fit in memory will not run at any of these speeds.

1. Look up the quantized file size of the largest model you actually want to run, then subtract a few gigabytes for the context cache. If the file exceeds the card's memory minus that reserve, stop there and pick a different card or a smaller quantization.

2. Divide the card's published bandwidth by the file size. That quotient is the theoretical ceiling for token generation on a dense model.

3. Keep roughly 60% to 75% of that quotient. This band is where the dense benchmark rows landed, and it is a planning estimate rather than a guarantee.

4. For a mixture-of-experts model, divide by the active weights instead of the whole file for a higher ceiling, but still check the whole file against memory.

A worked example shows how close the estimate lands. A 4-bit 32B model at about 20 GB gives an RTX 3090 a quotient of 936 divided by 20, roughly 47, which becomes a band of about 28 to 35 tokens per second. The RTX 5090 gives 1,792 divided by 20, about 90, for a band of roughly 54 to 67. The measured 61 tokens per second for the dense 32B model falls inside that band, assuming its file is near 20 GB. That is a consistency check, not proof.

Choosing between the RTX 5090, 3090, and a Mac

The right card depends on your largest model file and your budget, and the three realistic answers point in different directions. Speed favors the RTX 5090, price per gigabyte favors a used RTX 3090, and 70B models at 4 bits require a Mac with 64 GB or more.

For models up to about 32B parameters, the RTX 5090 is the faster pick. Its 32 GB holds a 6-bit 32B file, and its 1,792 GB/s writes about 1.8 times faster than the RTX 3090 in the shared benchmark. Whether the premium is worth it depends on the price where you live, and the quotes differ by thousands of dollars.

On a tighter budget with the same model sizes, a used RTX 3090 gives 24 GB and a little over half the RTX 5090's writing speed. Its price decides the case. For 70B models at 4 bits, no single GPU on this list holds the file, so the answer is a Mac with 64 GB or 128 GB and a ceiling near 14 tokens per second on paper.

If you want a laptop, the RTX 5090 laptop gives desktop RTX 3090 speed up to 24 GB of model. The M5 Max gives up roughly a quarter to a third of that speed in exchange for up to 128 GB of room. A $150 GTX 1080 Ti is a fair way to learn on small models and a dead end past 11 GB.

FAQ

• Is the RTX 5090 the best GPU for local AI? It is the fastest option among the cards compared here for models that fit in 32 GB, and it generates tokens about 1.8 times faster than a desktop RTX 3090. It cannot load a 4-bit 70B file, so it is not the best choice for the largest open models. Capacity decides that outcome, and 32 GB is not enough for a 42.5 GB file.

• Can an RTX 3090 run a 70B model at 4 bits? No. A 4-bit 70B file is about 42.5 GB, which exceeds the RTX 3090's 24 GB of VRAM. Even the 3-bit file at roughly 34.3 GB misses a 32 GB card. Only the 2-bit files fit, and they leave almost no room for context.

• Why is the RTX 5090 laptop as fast as a desktop RTX 3090? Because it has exactly half the desktop RTX 5090's memory bandwidth: 896 GB/s against 1,792 GB/s. Token generation is paced by bandwidth, so its 156.5 tokens per second lands almost on the desktop RTX 3090's 158.2. Its prompt processing score of 6,667 still beats the 3090's 5,175 because that phase is compute-bound.

• What is the 60 to 75 percent rule? Divide the card's published memory bandwidth by the model file size to get a theoretical ceiling, then keep roughly 60% to 75% of that result. The dense benchmark rows landed inside that band. It is a planning estimate, not a guarantee, and no primary source explains where the missing third goes.

• Does the rumored RTX 5090 cancellation change the numbers? No. The rumor affects price and availability, and Nvidia had not confirmed any production end. Memory capacity and memory bandwidth stay the same regardless of supply. Your model's file size is the number that can change the verdict.

The model file is the only spec worth trusting

Two sessions of editing this comparison left the same conclusion standing: your model's file size decides the shortlist, not the newest nameplate. None of the RTX 5090, RTX 3090, or RTX 5090 laptop can OpenAI-bit 70B file, so the ceiling on what you can run sits below what the price tags imply. The bandwidth and file-size arithmetic is portable and dull; the benchmark rows are one 2023 test on volunteer hardware.

If you publish video where you explain this kind of hardware reasoning, the value is trapped in the timeline. I built Skala Blog for that. Paste a YouTube URL into skalablog.com, the transcript is pulled, and a publishable draft comes back for you to edit.

Teams shipping tools like Crazystack typescript face the same packaging problem, and so does anyone teaching developer content. The tooling habit is worth borrowing from Dev doido: keep the reasoning in writing where it can be found later, not only in a video that scrolls away.

Turn your local AI video into a written guide

A good benchmark explanation deserves to survive past the week its video was uploaded, and a written version is how that happens. If you have YouTube videos where you walk through bandwidth math, quantization tradeoffs, or hardware buying decisions, you can turn that existing content into an article. Take the URL of the video where you explain your own GPU comparison, paste it into skalablog.com, and Skalablog transcribes it and generates a draft you can review and publish.

Compare a local AI GPU stack with Crazystack

Once you have chosen a card, the local stack around it still has to be assembled, and pulling together a current toolchain is where many local AI setups stall. Crazystack assembles full application stacks, including a Crazystack typescript option for teams shipping typed JavaScript end to end, which shortens the distance between a working model server and a usable interface. It is a different problem from picking a GPU, and it is the one that tends to show up immediately after the hardware arrives.

Source video