RTX 5080 vs RTX 4080 for LLM Inference: Same 16GB, Different Answer (2026)

Key Takeaways

• The VRAM ceiling does not move. Both cards carry 16 GB across a 256-bit bus per TechPowerUp's entries for the RTX 5080 and the RTX 4080. Identical models fit; identical models spill.
• Bandwidth is up 34%, and generation follows it. 960.0 GB/s of GDDR7 versus 716.8 GB/s of GDDR6X yields 182 tok/s against 142.49 tok/s on Llama-2 7B q4_0 in the same llama.cpp scoreboard thread — a 27.7% gain.
• Prefill barely moves. The same thread puts prompt processing at 8,297 tok/s versus 8,031.64 tok/s — about 3%, tracking the modest shader-count difference (10,752 versus 9,728 CUDA cores).
• The gain holds at longer context. Hardware Corner logs 94.14 tok/s for Qwen3 8B at 16K context on the RTX 5080 against 77.90 tok/s for the RTX 4080 at the same context — roughly 21%.
• Sources disagree on MoE models. For gpt-oss 20B, Hardware Corner's llama.cpp runs put the 5080 at 140.48 tok/s and the 4080 at 136.50 tok/s, while Rost Glukhov's Ollama figures give the 4080 139.93 tok/s and Windows Central gives the 5080 128 tok/s. Sparse models are runtime-sensitive.
• Street price decides it. At launch MSRPs — $999 and $1,199 per TechPowerUp — the newer card wins cost-per-token outright. At the SpecPicks catalog listings of 2026-09-09 ($1,689.00 and $1,099.52) the older card wins by a wide margin.

Spec delta: Blackwell versus Ada at the same memory size

Specifications come from the TechPowerUp database entries for the GeForce RTX 5080 and the GeForce RTX 4080, cross-checked against NVIDIA's RTX 5080 product page.

The shape of this table is the article in miniature. One row changed by a third. One row changed by a tenth. One row — the one that decides what you can run — did not change at all.

Buy links: MSI RTX 5080 16G Ventus 3X OC Plus and MSI RTX 4080 16GB Ventus 3X OC.

Since both cards are 16GB, what actually fits?

Weights are only part of the budget. A GGUF occupies roughly params × bits-per-weight ÷ 8 gigabytes, and then KV cache, the compute buffer and — if the card also drives your monitor — the desktop framebuffer sit on top. Reserve 1.5–2 GB before you start.

Glukhov's Ollama measurements on a 16 GB card put empirical numbers behind those rungs: Qwen3 14B at q4_K_M reports about 12 GB of VRAM, gpt-oss 20B about 14 GB, and a 24B model about 19 GB — over the line (source). Neither card changes any of those figures.

Sparse mixture-of-experts models are the interesting exception. Because only a fraction of parameters activate per token, a 20B-parameter MoE behaves like a much smaller dense model at inference time: Hardware Corner records gpt-oss 20B at MXFP4 using 14 GB and running 136.50 tok/s on the RTX 4080 — faster than a dense 14B on the same card. If you want more capability from 16 GB, a sparse model is a better lever than a newer card. That thread is followed further in Best Hardware for Running MoE LLMs Locally in 2026.

How much faster is the RTX 5080 in tokens per second?

Three things are worth reading carefully in that table.

Where the harness is identical, the gain is largest and most trustworthy. The Llama-2 7B rows come from a single community scoreboard running the same build and the same prompt on both cards, and they show +27.7% generation against +3.3% prefill. That is the cleanest available measurement of what the memory upgrade buys.

Where the harness differs, the gain shrinks or inverts. The Qwen3 14B row compares a 16K-context run against a 4K-context run, which flatters the older card; the true gap is almost certainly wider. The gpt-oss rows disagree outright in direction depending on whether the runtime is llama.cpp or Ollama. Sparse-model performance is dominated by runtime kernel quality, and kernel quality for a new architecture improves over months.

The 27B and 32B rows are not wins. Markaicode's Gemma-class 27B testing on the 5080 reports 47 tok/s at q4_K_M using 11.2 GB and 39.5 tok/s at q5_K_M using 13.5 GB — those are aggressively quantized configurations that squeeze under the ceiling, not a 27B model at the rung most people would choose. At full q4_K_M a 32B model drops to 18–20 tok/s per Local AI Master, which is the offload penalty, not a generational difference.

Does the bandwidth uplift show up in generation or only in prefill?

Generation, overwhelmingly. The mechanism is straightforward.

During prefill, the runtime processes your whole prompt at once. Every token in the prompt is available simultaneously, so the work parallelizes across thousands of shaders and the arithmetic units become the limit. More cores, more prefill throughput. These two cards differ by 10.5% in core count and 3.3% in measured prefill — close enough to call it consistent.

During generation, the runtime produces one token, then uses it to produce the next. There is no parallelism across tokens, so for each token the entire active weight set has to be read out of VRAM. The arithmetic is trivial by comparison; the memory system is the bottleneck. Bandwidth up 34%, generation up 21–28% across the sourced rows above — the shortfall from the theoretical figure being the fixed per-token overheads that do not scale with memory speed.

Which one you feel depends on your prompt-to-output ratio:

• Chat and code completion — a few hundred tokens in, several hundred out. Generation-dominated, so the newer card's advantage is what you experience.
• RAG over retrieved documents — 8K–16K tokens in, a couple hundred out. Prefill-dominated. The upgrade buys you almost nothing; time-to-first-token barely moves.
• Bulk classification or summarization — long inputs, one-line outputs, thousands of times. Prefill-dominated again, and the worst possible case for justifying the spend.

If your machine mostly answers questions, upgrade. If it mostly reads documents, the money is better spent elsewhere.

What happens at long context?

KV cache is the second claim on a fixed 16 GB budget, and on both of these cards it is what breaks first. A rough working figure for a modern 8B model with FP16 cache is about 0.5 GB per 4K of context; a 14B model roughly doubles that.

The cost shows up before the wall does. Hardware Corner's own 4080 figures show Qwen3 8B q4_K_M at 102.70 tok/s with a 4K window and 77.90 tok/s at 16K — a 24% drop with no change to the model, purely from cache pressure and longer attention (source). The 5080 pays the same tax on the same curve; it just starts higher.

Quantizing the KV cache to 8-bit reclaims roughly half of that footprint at modest quality cost and is the highest-leverage setting on either card. It is the difference between a 14B model at 16K and a 14B model at 32K, and it costs nothing but a flag.

The honest framing: on a 16 GB card, context length rather than model size is the wall, and no amount of memory bandwidth moves a wall.

Is a second card the better spend?

If the constraint you keep hitting is capacity, adding VRAM beats adding bandwidth. llama.cpp splits layers across GPUs without difficulty, and aggregate VRAM is what determines whether a model stays resident.

Two ZOTAC RTX 3060 Twin Edge OC 12 GB cards reach 24 GB combined — enough to hold a 32B model at Q4_K_M — for a launch MSRP of $329 each per TechPowerUp. Per-card speed is much lower; Hardware Corner measures 55.20 tok/s on Qwen3 8B q4_K_XL at 4K context for the RTX 3060 12 GB, and layer-split inference runs at roughly the pace of the slower card rather than summing throughput. You are buying capacity, not speed.

The costs are real: two usable x8 slots, airflow for two cards, two cards' worth of idle draw forever, and no benefit at all to single-GPU workloads like gaming or most image generation. But if your recurring frustration is "this model does not fit," a second cheap card fixes it and a faster 16 GB card does not. The full build is in Best Parts for a Dual RTX 3060 24GB Local-LLM Build in 2026.

What the rest of the build needs

Host CPU. With a model fully resident the CPU is a spectator. It becomes decisive the moment you offload, because CPU-held layers then set the pace for every token. An AMD Ryzen 7 5800X — 8 cores, 16 threads, 105 W TDP, 4.7 GHz boost per AMD — is a sensible floor for a 16 GB build precisely because 16 GB cards offload more often than 24 GB ones do. Phoronix's AI testing of that part records 18.50 tok/s on a 70B q4_K_M configuration occupying 42.1 GB, which is the shape of what a strong host CPU salvages when the GPU runs out. Pair it with 32 GB of dual-channel DDR4 minimum, 64 GB if offloading is routine.

Model-library storage. A 14B model at Q4_K_M is roughly 8.5 GB; a 20B MoE at MXFP4 about 14 GB; a 32B at Q4_K_M about 19 GB. Add an embedding model, a vision model, a couple of quantization rungs of whatever you use daily, and a serious collection passes 500 GB without anyone planning for it.

Capacity matters more than peak sequential throughput, because a model loads once per session and then lives in VRAM — cold-load time is dominated by file size, not interface generation. A Crucial BX500 1 TB SATA SSD, listed at $179.99 in the SpecPicks catalog as of 2026-09-09 though prices move daily, holds a substantial working set for the price of a fraction of either GPU. The interface comparison is in NVMe vs SATA SSD for Local LLM Model Libraries in 2026.

Performance per dollar and per watt

Using the Llama-2 7B q4_0 figures, because they are the only rows measured on the same harness for both cards.

The two cost rows point in opposite directions, and that is the actual finding. Judged on the price NVIDIA announced, the newer card is 35% cheaper per token and also more efficient per watt — a straightforward win. Judged on what the two cards currently list for in the SpecPicks catalog, the older card is 17% cheaper per token, and the 27.7% throughput advantage costs a 54% price premium.

Nothing about the silicon resolves that. It is a purchasing question, and it turns entirely on what each card actually costs you on the day you buy. The 40 W power delta works out to roughly $23 a year at 10 hours daily and $0.16/kWh — enough to note, not enough to decide.

Common pitfalls

• Expecting the upgrade to change what fits. It does not. Same 16 GB, same rungs, same spill points. If your complaint is capacity, this is the wrong purchase.
• Reading prefill numbers as generation numbers. The 3.3% prefill gain and the 27.7% generation gain describe the same pair of cards. Which one you experience depends on your workload shape.
• Comparing figures at different context lengths. A 4K result against a 16K result is not a comparison. The 24% drop Hardware Corner shows between those two contexts on one card is larger than the generational gap between the cards.
• Running an old CUDA build on a new architecture. Binaries compiled against an older toolkit fall back to JIT compilation on first run — slower startup and, in some paths, measurably lower throughput. Update llama.cpp or Ollama to a build released after the card shipped.
• Trusting a single MoE benchmark. The gpt-oss 20B rows above disagree in direction across runtimes. For sparse models, test your own runtime rather than inheriting someone else's number.
• Ignoring physical fit. Both are triple-slot-class boards. Measure your case before ordering, not after.

When NOT to buy either card

Skip both if your models comfortably fit in 12 GB. The RTX 3060 12 GB runs Qwen3 8B q4_K_XL at 55.20 tok/s per Hardware Corner for a fraction of either price, and for chat and autocomplete that is not a compromise most people notice.

Skip both if what you actually need is a resident 32B model — neither card holds one at Q4_K_M, and the 18–20 tok/s offloaded figures reported by Local AI Master show what that compromise costs. Two 12 GB cards or one 24 GB card is the correct fix.

And skip the upgrade specifically if you already own the RTX 4080. Selling a working 16 GB card to buy a different 16 GB card for a generation-side gain, at current street pricing, is the weakest version of this trade.

Verdict matrix

Get the RTX 5080 if…

• You can buy near the $999 launch MSRP, where it is 35% cheaper per tok/s than the older card at its own MSRP.
• Your workload is generation-heavy — chat, code completion, agent loops — where the 27.7% measured gain is what you feel.
• You want the better tokens-per-watt figure on an always-on box: 0.506 against 0.445.
• You want current-branch driver support on a card that will run at load for hours a day for several years.

Keep or buy the RTX 4080 if…

• You are shopping at current street prices, where it is 17% cheaper per tok/s.
• Your pipeline is prefill-heavy — long retrieved contexts, short answers — and the 3.3% prompt-processing gap is all the upgrade offers you.
• You already own one. The capacity ceiling is identical and the generational gain does not justify a churn.
• You would rather put the price difference into a second GPU, more system RAM, or storage.

Buy neither and add VRAM instead if…

• You are regularly offloading a 24B or 32B model to system RAM. That penalty is far larger than any 16-to-16 GB upgrade recovers.
• Long context is your recurring wall — 32K on a 14B model is over the line on both cards without KV quantization.
• Two 12 GB boards at $329 launch MSRP each fit your slot and airflow budget, giving 24 GB aggregate.

The recommended pick

For most buyers in 2026, the RTX 5080 is the better card and the RTX 4080 is the better purchase. The silicon question is settled — 34% more bandwidth producing 21–28% more generation throughput, at better efficiency, with the same capacity. If both cards sat at their launch MSRPs, the newer one would be the obvious recommendation with no caveats.

What flips it is price. At the SpecPicks catalog listings as of 2026-09-09, the newer card costs 54% more for 27.7% more throughput on the cleanest available measurement. That is a losing trade unless throughput is genuinely your binding constraint — and for most people running a 16 GB card, it is not. The binding constraint is 16 GB.

So: buy the 5080 if you find it near MSRP or your workload is generation-dominated and you are throughput-limited today. Otherwise buy the 4080 at a discount and put the difference toward the thing that actually changes what your machine can do, which is more VRAM.

Bottom line

This is the rare GPU comparison where the newer card is unambiguously better and the recommendation still depends on your receipt. Both cards hold exactly 16 GB, so both run exactly the same models with exactly the same spill points and the same long-context wall. What the newer memory subsystem buys is 21–28% more tokens per second on models that already fit, and roughly 3% on the prompt-processing half of the request. That is a real improvement and a narrow one. Price it honestly against what each card costs you today, and remember that neither one answers the question most local-LLM builders are actually asking, which is how to get past 16 GB.

Related guides

• RTX 5070 Ti vs RTX 5090 for Local LLMs: 16GB vs 32GB
• Local LLM Autocomplete + Agentic Coding on 16GB VRAM
• Best Hardware for Running MoE LLMs Locally in 2026
• RTX 3090 vs RTX 4090 for LLM Inference: Same 24GB (2026)
• Ollama vs vLLM vs llama.cpp on a 12GB GPU: Which Wins for One User?

Live price comparison

Side by side with live catalog pricing, current Product schema and Amazon CTAs for both cards: RTX 5080 vs RTX 4080 head-to-head. Prices there refresh from the catalog and may differ from the figures above.

Citations and sources

• TechPowerUp — GeForce RTX 5080 specifications (accessed 2026-09-09)
• TechPowerUp — GeForce RTX 4080 specifications (accessed 2026-09-09)
• TechPowerUp — GeForce RTX 3060 12 GB specifications (accessed 2026-09-09)
• NVIDIA — GeForce RTX 5080 product page (accessed 2026-09-09)
• AMD — Ryzen 7 5800X product page (accessed 2026-09-09)
• llama.cpp GitHub — CUDA performance discussion #15013 (accessed 2026-09-09)
• Hardware Corner — GPU ranking for local LLMs (accessed 2026-09-09)
• Hardware Corner — RTX 4080 LLM benchmarks (accessed 2026-09-09)
• Hardware Corner — RTX 3060 12 GB LLM benchmarks (accessed 2026-09-09)
• Rost Glukhov — Choosing the best LLM for Ollama on a 16 GB VRAM GPU (accessed 2026-09-09)
• Local AI Master — RTX 5090 vs 5080 for local AI (accessed 2026-09-09)
• Markaicode — Ollama Gemma-class 27B throughput on the RTX 5080 (accessed 2026-09-09)
• Markaicode — Benchmarking local LLM token speed on NVIDIA RTX (accessed 2026-09-09)
• Windows Central — What matters most when running Ollama on your PC (accessed 2026-09-09)
• Compute Market — Best local LLMs on RTX 50-series (accessed 2026-09-09)
• Phoronix — AMD Ryzen 7 5800X AI testing (accessed 2026-09-09)

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